complex problem solving skills course

Designed to sharpen your critical thinking skills.

Your online learning journey

Frame your problem, think critically and engage with your stakeholders.

As a manager or aspiring leader, you know that to rise through the ranks requires strong and creative problem-solving skills. To get to the top, business leaders need to act and react to the priorities set for the organization.

They must exercise their critical thinking capabilities and decide which issues to focus on and when. So, what are the essential elements of effective problem-solving that you can apply to your particular scenario?

The program is structured into five units, covering the following topics:

• Learn to recognize when a problem requires a systematic approach.
• Articulate how the process works and how each part contributes.
• Understand why engaging stakeholders is critical throughout the process.
• Learn how to develop your Frame sequence and how to frame your problem.
• Apply the Holding Hands, Dolly the Sheep and Watson rules to the Frame sequence to make it robust.
• Learn how to map out all the possible problem causes using a WHY map.
• Identify which causes are at the root of the problem.
• Update the Frame sequence to integrate new information.
• Learn how to apply MECE thinking.
• Identify and organize all the potential solutions using a HOW map and determine which are feasible.
• Identify a set of criteria that represents your stakeholders’ views.
• Create a decision matrix to rank the attractiveness of the options.
• Make aligned decisions that support the solution.
• Craft a storyline to defend the line of thinking (bullet proof) which is being empathetic to stakeholders.
• See the big picture and plan your next actions.

Themes you will explore

Problem framing

Thinking creatively

Thinking critically

Question mapping

Hypotheses testing

Decision making

Engaging stakeholders

You will have a dedicated learning coach, making sure you receive a highly individualized learning experience.

Your professional learning coach accompanies you through your 5-week learning journey on this Complex Problem Solving online course. They provide support and feedback as you apply your learning directly into your workplace, where it has an immediate impact.

Their input helps you translate your learning to your particular context. By spreading this feedback regularly throughout the program, you’ll be sure to embed your ongoing learning directly in your daily work.

Your professional learning coach interacts with you via video, in writing, and over the phone. You have calls, spread across the 5-weeks, at intervals that consolidate your learning.

Your learning coach helps you

Find quick answers to your online program queries in our comprehensive FAQ section.

Schedule a call with a Program Advisor to discuss your professional development and see if this is the program for you.

Join online and experience a walkthrough of the Online programs, how they work, what you can expect and how it will impact your career.

Reinforce and consolidate your expertise in leadership, strategy, or digital acceleration through an immersive, fully online experience incorporating executive coaching.

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Creative Thinking: Innovative Solutions to Complex Challenges

Learn how to grow a culture of creativity to innovate competitive solutions.

All Start Dates

8:30 AM – 4:30 PM ET

2 consecutive days

$2,990 Programs fill quickly — free cancellation up to 14 days prior

Registration Deadline

October 8, 2024

$3,100 Programs fill quickly — free cancellation up to 14 days prior

April 22, 2025

Overview: Creative Thinking Skills Course

The tech breakthrough that makes smartphones irrelevant, a new viral ad campaign, your company’s next big revenue generator — ideas like these could be sitting in your brain; all you need are the creative thinking skills and strategies to pull them out.

This interactive program focuses explicitly on the creative thinking skills you need to solve complex problems and design innovative solutions. Learn how to transform your thinking from the standard “why can’t we” to the powerful “how might we.” Crack the code on how to consistently leverage your team’s creative potential in order to drive innovation within your organization. Explore how to build a climate for innovation, remove barriers to creativity, cultivate courage, and create more agile, proactive, and inspired teams.

You will leave this program with new ideas about how to think more productively and how to introduce creative thinking skills into your organization. You can apply key takeaways immediately to implement a new leadership vision, inspire renewed enthusiasm, and enjoy the skills and tools to tackle challenges and seize opportunities.

Innovation experts Anne Manning and Susan Robertson bring to this highly-interactive and powerful program their decades of experience promoting corporate innovation, teaching the art of creative problem solving, and applying the principles of brain science to solve complex challenges.

Who Should Take Creative Thinking Skills Training?

This program is ideal for leaders with at least 3 years of management experience. It is designed for leaders who want to develop new strategies, frameworks, and tools for creative problem solving. Whether you are a team lead, project manager, sales director, or executive, you’ll learn powerful tools to lead your team and your organization to create innovative solutions to complex challenges.

All participants will earn a Certificate of Completion from the Harvard Division of Continuing Education.

Benefits of Creative Thinking Skills Training

The goal of this creative thinking program is to help you develop the strategic concepts and tactical skills to lead creative problem solving for your team and your organization. You will learn to:

• Retrain your brain to avoid negative cognitive biases and long-held beliefs and myths that sabotage creative problem solving and innovation
• Become a more nimble, proactive, and inspired thinker and leader
• Create the type of organizational culture that supports collaboration and nurtures rather than kills ideas
• Gain a practical toolkit for solving the “unsolvable” by incorporating creative thinking into day-to-day processes
• Understand cognitive preferences (yours and others’) to adapt the creative thinking process and drive your team’s success
• Develop techniques that promote effective brainstorming and enable you to reframe problems in a way that inspires innovative solutions

The curriculum in this highly interactive program utilizes research-based methodologies and techniques to build creative thinking skills and stimulate creative problem solving.

Through intensive group discussions and small-group exercises, you will focus on topics such as:

• The Creative Problem Solving process: a researched, learnable, repeatable process for uncovering new and useful ideas. This process includes a “how to” on clarifying, ideating, developing, and implementing new solutions to intractable problems
• The cognitive preferences that drive how we approach problems, and how to leverage those cognitive preferences for individual and team success
• How to develop—and implement— a methodology that overcomes barriers to innovative thinking and fosters the generation of new ideas, strategies, and techniques
• The role of language, including asking the right questions, in reframing problems, challenging assumptions, and driving successful creative problem solving
• Fostering a culture that values, nurtures, and rewards creative solutions

Considering this program?

Send yourself the details.

Related Programs

• Design Thinking: Creating Better Customer Experiences
• Agile Leadership: Transforming Mindsets and Capabilities in Your Organization

October Schedule

• Creative Challenges: A Team Sport
• The Place to Begin: Reframe the Challenge
• Ideas on Demand
• Building a Creative Organization

April Schedule

Instructors, anne manning, susan robertson.

I really enjoyed the way the instructors facilitated the program. The combination of theory and practical exercises was powerful and effectively reinforced the concepts.

Innovation and Educational Research, Director, Vertex Pharmaceuticals, Inc.

Certificates of Leadership Excellence

The Certificates of Leadership Excellence (CLE) are designed for leaders with the desire to enhance their business acumen, challenge current thinking, and expand their leadership skills.

This program is one of several CLE qualifying programs. Register today and get started earning your certificate.

Harvard Division of Continuing Education

The Division of Continuing Education (DCE) at Harvard University is dedicated to bringing rigorous academics and innovative teaching capabilities to those seeking to improve their lives through education. We make Harvard education accessible to lifelong learners from high school to retirement.

Serving technical professionals globally for over 75 years. Learn more about us.

MIT Professional Education 700 Technology Square Building NE48-200 Cambridge, MA 02139 USA

Accessibility

Solving Complex Problems: Structured Thinking, Design Principles, and AI

Sang-Gook Kim

Download the Course Schedule

How do you solve important, large-scale challenges with evolving and contradictory constraints? In this 5-day course, transform your approach to large-scale problem solving, from multi-stakeholder engineering projects to the online spread of misinformation. Alongside engineers and leaders from diverse industries, you’ll explore actionable innovative frameworks for assessing, communicating, and implementing complex systems—and significantly increase your likelihood of success.

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS PART OF THE  PROFESSIONAL CERTIFICATE PROGRAM IN INNOVATION & TECHNOLOGY  OR THE  PROFESSIONAL CERTIFICATE PROGRAM IN DESIGN & MANUFACTURING .

Engineering projects with shifting goals. Inefficient national healthcare systems. The online spread of misinformation. Every day, professionals are tasked with addressing major challenges that present opportunities for great triumph—or significant failure. How do you approach an important, large-scale challenge with evolving and contradictory constraints? Is the solution a new technology, a new policy, or something else altogether? In our new course Solving Complex Problems: Structured Thinking, Design Principles, and AI , you’ll acquire core principles that will change the way you approach and solve large-scale challenges—increasing your likelihood of success. Over the course of five days, you will explore proven design principles, heuristic-based insights, and problem-solving approaches, and learn how to persuasively present concepts and system architectures to stakeholders. Methods utilize recent developments in AI and Big Data, as well as innovative strategies from MIT Lincoln Laboratory that have been successfully applied to large and complex national defense systems. By taking part in interactive lectures and hands-on projects, you will learn to think through and leverage important steps, including problem abstraction, idea generation, concept development and refinement, system-level thinking, and proposal generation. Alongside an accomplished group of global peers, you will explore the strategies and frameworks you need to implement large-scale systems that can have a significant positive impact—and minimize the probability of failure.

Certificate of Completion from MIT Professional Education  

• Approach and solve large and complex problems.
• Assess end-to-end processes and associated challenges, in order to significantly increase the likelihood of success in developing more complex systems.
• Implement effective problem-solving techniques, including abstracting the problem, idea generation, concept development and refinement, system-level thinking, and proposal generation.
• Utilize system-level thinking skills to evaluate, refine, down select, and evaluate best ideas and concepts.
• Apply the Axiomatic Design methodology to a broad range of applications in manufacturing, product design, software, and architecture.
• Generate and present proposals that clearly articulate innovative ideas, clarify the limits of current strategies, define potential customers and impact, and outline a success-oriented system development and risk mitigation plan.
• Effectively communicate ideas and persuade others, and provide valuable feedback.
• Confidently develop and execute large-scale system concepts that will drive significant positive impact.

Edwin F. David Head of the Engineering Division, MIT Lincoln Laboratory

Jonathan E. Gans Group Leader of the Systems and Architectures Group, MIT Lincoln Laboratory

Robert T-I. Shin Principal Staff in the Intelligence, Surveillance, and Reconnaissance (ISR) and Tactical Systems Division, MIT Lincoln Laboratory Director, MIT Beaver Works

This course is appropriate for professionals who design or manage complex systems with shifting needs and goals. It is also well suited to those who want to improve the quality and performance of their operations and decision-making in a large-scale system environment. Potential participants include engineers, group leaders, and senior managers in government and industries including automotive, aerospace, semiconductors, engineering, manufacturing, healthcare, bio-medical, finance, architecture, public policy, education, and military.

Computer Requirements

A laptop with PowerPoint is required.

• Courses for Individuals

Understanding and Solving Complex Business Problems

Management and Leadership

Certificate Credits

- Operations

- Systems Thinking

• Participants

Course Highlights

• Discover MIT's unique, powerful, and integrative System Dynamics approach to assess problems that will not go away
• Experience the Beer Game, which simulates the supply chain of the beer industry
• Learn a new way of thinking about and resolving complex, persistent problems that emerge from change
• Earn a certificate of course completion from the MIT Sloan School of Management

Why attend Understanding and Solving Complex Business Problems?

Systems thinking was designed to improve people's ability to manage organizations comprehensively in a volatile global environment. It offers managers a framework for understanding complex situations and the dynamics those situations produce. Systems thinking is a response to the rapid changes in technology, population, and economic activity that are transforming the world, and as a way to deal with the ever-increasing complexity of today's business.

Senior managers can use systems thinking to design policies that lead their organizations to high performance. The program is intended to give participants the tools and confidence to manage organizations with full understanding and solid strategy.

Course experience

This complex problem-solving course introduces participants to MIT's unique, powerful, and integrative System Dynamics approach to assess problems that will not go away and to produce the results they want. Through exercises and simulation models, participants experience the long-term side effects and impacts of decisions and understand the ways in which performance is tied to structures and policies.

Sample Schedule—Subject to Change

This program is designed for executives with decision-making responsibility who are looking for fresh ideas to resolve organizational problems.

Past participants have included

• VPs and EVPs
• Corporate planners and strategists
• Senior Project Managers
• Product Development Managers

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This program is designed to empower you to analyze complex problems in any area by using powerful yet very simple tools which are also very easy to use in real world, I enjoyed it a lot.

—Jia X.

Enroll Now!

What mit’s beer game teaches about panic hoarding, mit’s ‘beer game’ shows humans are weakest link in supply chains, the beer game, john sterman on system dynamics, how the ‘beer game’ helps retailers solve toilet paper crisis, course offerings.

Solving Complex Problems Specialization

Problem Solving Skills for Business and Innovation. Learn how to analyze, evaluate, and solve complex problems from all disciplinary perspectives SOLVING COMPLEX PROBLEMS will teach you revolutionary new problem-solving skills. Involving lectures from over 50 experts from all faculties at Macquarie University, we look at solving complex problems in a way that has never been done before.

This specialization uses the framework of Big History which synthesizes knowledge across the sciences and the humanities, and provides a powerful foundation to think and research in new ways. Big History has been embraced as an important global framework by the World Economic Forum (WEF). Presentations at WEF by Professor David Christian, one of the creators of this specialization, have included ‘Interdisciplinary Approaches to Solving 21st Century Challenges’ (Davos 2012), ‘Big History for Big Picture Thinking’ (Davos 2014), and ‘Big History, Big Decisions’ (Tianjin 2014). In 2015, the WEF Annual Meeting in Davos had four sessions devoted to Big History including three interdisciplinary ‘Big History, Big Future’ panels on cooperation, innovation, and global growth and stability. These interdisciplinary discussion panels were the inspiration for this Solving Complex Problems Specialization.

Applied Learning Project To solve complex problems, whether it is the challenge of developing a new product, or Einstein’s task of trying to explain how gravity worked – and literally everything else in between – it is not enough to take the problem and apply already existing skills. The skill that has always led to big breakthroughs in any field or industry is the skill of seeing something in a new way. That is the vital skill you will learn in this Coursera specialization.

From the very start of the specialization, your assignments will be geared toward tackling a complex issue of your choice which you face in your career path, industry, or field. Each phase of the course builds up to a briefing paper that analyzes, evaluates, and attempts to solve a highly complex problem. The specialization advances your knowledge of your own field by teaching you to look at it in new ways and it fosters your own revolutionary new innovations.

Course Information

Estimated Time: Approximately 6 months to complete Suggested pace of 2 hours/week

Difficulty: Beginner

Tags: Coursera , Specialisation

Analysing Complexity

Evaluating problems, creating innovation, solving complex problems capstone.

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Creative Thinking for Complex Problem Solving

The challenges businesses face today are increasingly complex and systemic, often resisting obvious and definitive solutions. This complexity is frequently met with oversimplification, over-analysis, and quick fixes. But complex problem solving requires unconventional thinking to make unexpected connections—connections that others might not see. You can create these connections by bringing play and rigor into your problem-solving process. The most effective problem solvers harness creative thinking to see problems from unique angles, experiment with new and innovative ideas, and maintain momentum throughout the problem-solving process to make measured progress and move from problems to possibilities. Our newest course will help you become a dynamic problem solver, equipped to take on today’s most intricate challenges with creative thinking and confidence.

Course Outcomes

• Look at problems through different perspectives to open up many possibilities.
• Refine your instincts into actionable and innovative solutions.
• Learn how to de-risk and experiment to build resilient strategies.
• Balance creative thinking and rigor to get to breakthrough ideas and sustainable solutions.

Skills You’ll Gain

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What You'll Learn

Introduction: welcoming complexity, watch a sneak peek, 2 video lessons.

Welcome Complexity: An Introduction to Mindsets and Methods—Delve into the essential components of curiosity, experimentation, and iteration to welcome complexity as an opportunity.

1 Assignment

Articulate a Complex Problem: In your organization, reflect on how play and rigor show up.

2 Discussions

When have you seen the power of adding more imagination or creativity into addressing a complex problem? What was the impact?

What common complex problem-solving pitfall tends to happen most on your team: oversimplifying, overanalyzing, or quick fixes? Why and how could you counter it?

2 Resources

Mindsets that Drive Complex Problem Solving: This guide provides information on embracing the mindsets of exploration, empathetic curiosity, and experimentation.

Overcoming Common Pitfalls: Strategies to recognize and address common pitfalls such as oversimplification, overanalysis, and premature solution finding.

Week 1: Open Up the Problem With Curiosity

4 video lessons.

Expand the Question: Engage Stakeholders and Invite Fresh Perspectives—Learn to uncover and ask the right questions by involving diverse stakeholders

Build Empathy: Put Humans at the Center—Apply critical thinking strategies to understand the biases and needs of stakeholders using three IDEO case studies

Diverge and Converge: Generate Possibilities and Make Choices—Explore IDEO’s diverge/converge process, and the powerful role ambiguity plays in problem solving

The Science of Play: Why Creative Problem Solving Works—Explore the neuroscience behind imagination and play, and why these concepts are so vital in problem-solving spaces.

Refine Your Problem Statement: Reflect on and apply techniques to deconstruct assumptions, broaden perspectives, refine your central problem statement based on human needs and resources.

3 Discussions

What “sacred myths” are present in your organization? How might they limit creativity and innovation?

Does your organization oversimplify, overanalyze, or jump to solutions when facing complexity? Why?

How can leaders nurture acceptance of uncertainty in the innovation process?

Uncover Assumptions: Tools to help you uncover starting points, hunches, and strong beliefs about your problem.

Right-Size the Question: Learn how to sharpen your problem statement with lessons from IDEO case studies.

Week 2: Get Tangible Through Experimentation

Level up Ideas—Techniques to evolve early hunches into tangible concepts

Build confidence—Learn to assess concepts using IDEO’s Desirability, Viability, and Feasibility framework

De-risk Through Experimentation—Learn how to use prototyping to de-risk your solutions

The Art of Observation—Techniques for capturing unbiased observations from your experiments

Create Prototypes: Bring your solutions to life with rapid prototyping, uncover hidden assumptions, and build resilience in your solutions.

What technique(s) helped you most in leveling up early ideas into testable concepts?

How might you increase the diversity of perspectives involved in shaping and assessing early prototypes?

In what ways can leaders nurture acceptance of uncertainty and nonlinearity in the early innovation process?

Tools for Prototyping and Experimentation: Guides on co-creation sessions, mock pitches, and boundary concepts.

Simulating Strategies and Solutions: Learn how to use strategy board games as tools for fostering problem-solving, creativity, and innovation.

Week 3: Iterate As You Learn

3 video lessons.

Meaning Making: Identify Patterns and Themes Through Synthesis—Balance playful synthesis with rigorous analysis to build compelling narratives

Pivot and Iterate—Techniques to adapt and evolve future solutions

Learn from The Future—Use future scenarios to pressure-test ideas and adapt to evolving concepts

Uncover Deep Insights: Apply the techniques of affinity clustering, stakeholder critiques, and working backward from future visioning to derive meaningful insights and identify moments to iterate or pivot.

What metrics would indicate you are making meaningful progress amidst complexity and uncertainty?

What insights challenged your assumptions about this problem space or audience?

In what ways can experiments that “fail” still provide value in complexity?

Find the Implications from Insights: Strategies for leveraging insights in problem-solving.

Measure Progress: Methods to track progress and align with future scenarios.

Conclusion: Maintain Momentum

1 video lesson.

Sustain Commitment—Learn how to inspire behavioral change and sustain commitment.

Reflect on the Mindsets and Methods to Drive Sustained Change: Determine everyday rituals that motivate teams and counter change fatigue. Adopt lenses assessing current strategies while envisioning aspirational futures.

Why is it important to define success by outcomes rather than only concrete outputs/deliverables? How might this shape your approach?

What everyday rituals can leaders employ to keep teams inspired and committed for the long haul of complex problem-solving?

Temperature Check: Evaluate your progress and strategize the next steps to enhance confidence in your problem-solving direction.

Meet Your Instructors

Kate Schnippering

Executive design director at ideo.

Kate Schnippering is an Executive Design Director at IDEO, with a focus on creative technology. Kate brings ‘build to learn' experimentation to make real the futures we imagine. She creates conditions for teams and partners to immerse in imagination as a collective act—uplifting dreams and rigor in equal measure. In nearly a decade at IDEO, Kate’s developed teams, leaders, and organizations.

Her work investigates pathways to positive, systemic change for people and nature—by harnessing expressive technologies to make science & data relatable, and grow the power of everyday people. She’s built a real-world ‘magic school bus’ that teaches rover engineering to middle schoolers on Mars, designed a product for patients to partner directly with medical researchers in the study of rare diseases, and guided a youth mental health platform from proof of concept to delivery.

Michelle Lee

Partner and executive managing director at ideo play lab.

Michelle Lee is a Partner and Managing Director at IDEO, where she has applied her passion for play to leading interdisciplinary teams of designers and researchers in bringing engaging, interactive, and playful experiences to market. She believes in leveraging the principles of play to connect with people on a deeper emotional level that captivates, delights, and empowers.

Through her work, she has helped clients enhance workplace culture, championed responsible digital design, inspired underrepresented students to pursue careers in STEM, and supported organizations as they adopted practices in line with a circular economy. Michelle has shared her passion for play at SXSW, The Delight Conference, The Culture Summit, Circularity 23 and through numerous podcasts and articles.

Frequently Asked Questions

How do ideo u cohort courses work does my time zone matter.

We offer three types of courses: self-paced courses, cohort courses, and certificate programs. Cohort courses run on a set calendar, with fixed start and end dates. Course learning is self-paced within those dates and requires approximately 4-5 hours per week over 5 weeks. Courses consist of videos, activities, assignments, access to course teaching teams, and feedback from a global community of learners. There are also optional 1-hour video Community Conversations, held weekly by the teaching team. 

All of our cohort courses are fully online, so you can take them from any time zone, anywhere in the world. With our cohort course experience , while you'll be learning alongside other learners, you'll still have the flexibility to work at the pace that fits your own schedule. There aren’t mandatory live components, so you don't have to worry about having to log in at a specific time. At the same time, you'll have access to a teaching team, which is composed of experts in the field who are there to provide you feedback, and there are also plenty of options to connect with your fellow learners.

What is the role of the instructor and teaching team? Will learners be able to get feedback?

Course instructors have a strong presence in the courses through the course videos, but they're not actively providing feedback or holding direct conversations with our learners. We have a teaching team to ensure that you have the feedback, guidance, and support you need to learn successfully in your course. Our teaching team members are design practitioners that have experience applying course methods and mindsets in a wide variety of contexts around the world.

Our teaching team consists of teaching leads and teaching assistants, who are experts in their fields. Many of them have been with IDEO U for many years, and we have selected those who have direct experience with applying the course methods and mindsets in all sorts of contexts around the world. They all go through multiple training sessions by our instructional designers on not only on the subject matter, but also on how to create safe and collaborative learning experiences and environments.

What are Community Conversations, and how are they related to the course material?

Community Conversations are one-hour live video conversations hosted by the teaching team on Zoom. These happen once per week, with each one having two to three time options to accommodate different time zones. Each week focuses on the lesson that you’ve just gone through, so the output and the content depend on the specific lessons. You'll have the opportunity if you work together with your peers on the tools and mindsets from the course, reflect on what you’ve learned, and also address any challenges that you might be going through.

What will I have access to during and after my course?

All course materials, including videos, activities, and assignments will be available while you are enrolled in a course. During the 5 weeks of the course, you will have full access to our learning platform and can refer back to it any time. You will only have access to the course materials while you are enrolled. 

Assignments must be submitted during the 5-week course duration in order for you to receive a certificate of completion.

Can I take the course with my team?

Absolutely! We have had many teams go through our courses together. For those taking our courses as a team, we provide a number of additional benefits:

1. A Team Learning Guide, developed to provide your team with resources to facilitate offline discussions that complement the in-course experience.

2. A Manage Learners function, which provides visibility into your team's progress within the course.

3. The ability to create a private Learning Circle, which is a closed space for discussion on the learning platform specifically for your team.

For more information, visit our Team Learning page.

Do you offer discounts?

We offer a discount when you enroll in multiple courses at the same time through some of our certificate programs, including Foundations in Design Thinking , Business Innovation , Human-Centered Strategy , and Communicating for Impact . 

You can also enter your email address at the bottom of this page in order to receive updates on future offers or possible discounts. 

Will I get a certificate after completing a course?

After completing a cohort course, you will be able to add it to your “licenses and certifications” on LinkedIn.

We also have certificate programs that consist of multiple courses. After completing a certificate, you will receive a certificate of completion via email as a downloadable PDF within 1-2 weeks of completing the final required course. Certificates are configured for uploading and sharing on LinkedIn.

How do I purchase a cohort course?

You can purchase a course on our website using a credit card, PayPal, or Shop Pay. For US customers, we also offer installment plans at checkout if you use the Shop Pay method of payment.

We typically are not able to accommodate bank transfer or invoicing. However, if your order includes 10 seats or more, please contact [email protected] and our team will be happy to review your request. 

Collaborate with a Global Community

Work with expert coaches.

Our teaching team has extensive applied industry knowledge. They'll help deepen your understanding and application of the course content by facilitating written discussions, live video moments, and assignment feedback.

Expand Your Network

Join virtual live discussion groups for deeper conversation, reflection, and connection led by teaching team members and available multiple times a week across time zones.

Receive Feedback

Gain tips, techniques, and a downloadable feedback guide; and share and receive feedback on assignments from peers.

Loved by Learners Across the Globe

“Michelle has a passion for thinking BIG, addressing complexity with playful creativity, and somehow making it all fun! She understands deeply the importance and implications of play across contexts, industries, and solutions - and uses it masterfully in her own work and in helping others come up with solutions and innovations. I would 100% choose her as my teacher and mentor in this space every time - and have!”

"Kate and her team brought people together from across the Ranger Business to engage in complex strategy development through a playful and curious program of work. With prototypes and ideas in hand, we explored new places and met new people, growing and learning together as a team. These glimpses into the future continue to inspire us, have changed our approach to work and compel us to continuously adjust and refine our Ranger strategy to support future generations."

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Foundations in Design Thinking Certificate

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Enroll As a Team

The practice and application of design thinking, innovation, and creativity is highly collaborative and team based—which is why we believe that learning is better together. Take a course as a team and develop new skills and mindsets, have deeper discussion during course kickoff and debrief sessions, and build a shared understanding.

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Turn Your Thinking Around: New Approaches to Problem-Solving

This free online course includes:.

• Hours of Learning
• CPD Accreditation
• Final Assessment

Rate This Course & Get Better Recommendations!

Thanks for your review, in this free course, you will learn how to.

Problem-solving refers to a person’s ability to successfully manage and find solutions for complex and unexpected situations in our daily life and workplace. The ability to solve simple and complex problems every day is essential to individuals and organizations. Employees with strong problem-solving skills often rise through the ranks quickly in organizations. This insightful free online course will teach you new problem-solving tools to solve problems efficiently and effectively. You will start by learning about the Copernican Revolution to develop a new way of thinking, find out why problem-solving efforts fail and study the principles of creative thinking.

Next, you will discover new problem-solving tools like the higher-level view and word association to enhance the skills of any problem-solving team. You will then learn to review processes in an organization, using a process map to identify areas for process improvement. Delve deeper into the root cause analysis of a problem to find better solutions. Learn to uncover the stages in problem-solving tools that work best with each of them, how to organize an effective problem-solving team and how to quantify the benefits of problem-solving.

Problem-solving skills are universally sought-after by employers. Many businesses rely on their employees to identify and effectively solve business problems. Without problem-solving skills, you spend time worrying about what you will do in case of a problem, and you may make decisions that have negative consequences on the organization. This course will teach you in-demand skills that will give you a competitive edge and make a big difference in your career and personal life. Anyone who wants to become proficient in problem-solving will find this course beneficial. Start learning today and become an effective problem-solver!

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• Professional Development
• Course Listing
• problem-solving-intermediate-

PROBLEM SOLVING (INTERMEDIATE)

Course overview

Self-Management

Course date

13-11-2024 to 13-11-2024

Course type

Instructor-Led

(including GST)

Course Fee GST 9% : $436.00

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SkillsFuture Credit

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Course Overview

When an organisation goes about to achieve its mission, it does so in an environment of unknowns and variables. Despite the best intention and effort, it will encounter problems. Things may fall short for customers. Operation constraints can affect the process and output. Unexpected events such as supply chain disruptions, natural disasters and world events can all throw a spanner in the works. Problems may be encountered by various functions and roles. Failure to solve problems effectively can slow down the business, even hurt an organisation’s reputation.

Problem solving skills is therefore important for organisations to find solutions to unexpected situations. It combines analytical and creative thinking to deal with business challenges. It also affords learning about cause and effect, and allows organisations to mitigate future challenges. At the individual level, it equips employees with valuable skills in listening, communicating and collaborating with one another. It provides employees a sense of satisfaction and pride from the ability to solving problems and contributes to the mission and vision of the organisation.

Course benefits

The course, Problem Solving, is a critical core skill endorsed by SkillsFuture Singapore. It supports learners in getting their team members to develop problem solving practices. It provides learners with the tools to perform root cause analysis. It equips learners with big picture thinking and the techniques to develop potential solutions. It helps learners to test solutions and determine risks and constraints. It provides learners with the techniques to evaluate and prioritise solutions, and engage stakeholders to secure buy-in. When learners apply these competencies, they will be able to contribute to problem solving at the workplace and enable the organisation to meet stakeholders’ objectives.

Course outline

Pre-read: Personal Mindset

• Introduction to learning and unlearning for Good Work 

• Unlearn to Learn 

• Influences on Mindset

1: Define the problem

• Pitfalls of problem solving

• The innovative problem solving process

• Root cause analysis tools

• Defining the objective

2: Create and consider many options

• Facilitation techniques

• Data gathering

• Develop big picture thinking

• Quantitative and qualitative methods

3: Test options

• Identifying and prioritising your riskiest assumptions

• Risk assessment matrix

• Prototyping

• Experimenting and user testing

4: Choose a solution based on defined criteria

• Comparison matrix and analysis

• Prioritisation framework

5: Implement, refine and repeat

• Measuring what matters

• Stakeholder analysis matrix

• Stakeholder engagement techniques

• Present and pitch

Course runs

Who should attend?

Level 2 - Supervisor, Executive, & Emerging Managers Level 3 - New Managers Level 4 - Managers

Programme leader

Mapped to Critical Core Skills – Thinking Critically – Problem Solving (CCS-PRS-I002-1)

Programme Fees

Full Course Fee: $ 436 (incl of 9% GST)

Nett Fee (after funding) *:

For Employer Sponsored Participants:

SME: $ 156 Non-SME: $ 236

For Participants eligible for SkillsFuture Mid-Career Enhanced Subsidy:

40 years old and above

(Singapore Citizen only): $ 156

For Self Sponsored Participants:

SkillsFuture Credit is applicable for Singapore Citizens aged 25 years old and above only

Inclusive of 9% GST (GST is based on Full Course Fee)

*Terms and conditions apply

Programme Executive In Charge : Patricia Lee

Telephone : 62489447

Email : [email protected]

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Module 5: Thinking and Analysis

Problem-solving with critical thinking, learning outcomes.

• Describe how critical thinking skills can be used in problem-solving

Most of us face problems that we must solve every day. While some problems are more complex than others, we can apply critical thinking skills to every problem by asking questions like, what information am I missing? Why and how is it important? What are the contributing factors that lead to the problem? What resources are available to solve the problem? These questions are just the start of being able to think of innovative and effective solutions. Read through the following critical thinking, problem-solving process to identify steps you are already familiar with as well as opportunities to build a more critical approach to solving problems.

Problem-Solving Process

Step 1: define the problem.

Albert Einstein once said, “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.”

Often, when we first hear of or learn about a problem, we do not have all the information. If we immediately try to find a solution without having a thorough understanding of the problem, then we may only be solving a part of the problem.  This is called a “band-aid fix,” or when a symptom is addressed, but not the actual problem. While these band-aid fixes may provide temporary relief, if the actual problem is not addressed soon, then the problem will continue and likely get worse. Therefore, the first step when using critical thinking to solve problems is to identify the problem. The goal during this step is to gather enough research to determine how widespread the problem is, its nature, and its importance.

Step 2: Analyze the Causes

This step is used to uncover assumptions and underlying problems that are at the root of the problem. This step is important since you will need to ensure that whatever solution is chosen addresses the actual cause, or causes, of the problem.

Asking “why” questions to uncover root causes

A common way to uncover root causes is by asking why questions. When we are given an answer to a why question, we will often need to question that answer itself. Thus the process of asking “why” is an  iterative process —meaning that it is a process that we can repeatedly apply. When we stop asking why questions depends on what information we need and that can differ depending on what the goals are. For a better understanding, see the example below:

Problem: The lamp does not turn on.

• Why doesn’t the lamp turn on? The fuse is blown.
• Why is the fuse blown? There was overloaded circuit.
• Why was the circuit overloaded? The hair dryer was on.

If one is simply a homeowner or tenant, then it might be enough to simply know that if the hair dryer is on, the circuit will overload and turn off.  However, one can always ask further why questions, depending on what the goal is. For example, suppose someone wants to know if all hair dryers overload circuits or just this one. We might continue thus:

• Why did this hair dryer overload the circuit? Because hair dryers in general require a lot of electricity.

But now suppose we are an electrical engineer and are interested in designing a more environmentally friendly hair dryer. In that case, we might ask further:

• Why do hair dryers require so much energy?

As you can see from this example, what counts as a root cause depends on context and interests. The homeowner will not necessarily be interested in asking the further why questions whereas others might be.

Step 3: Generate Solutions

The goal of this step is to generate as many solutions as possible. In order to do so, brainstorm as many ideas as possible, no matter how outrageous or ineffective the idea might seem at the time. During your brainstorming session, it is important to generate solutions freely without editing or evaluating any of the ideas. The more solutions that you can generate, the more innovative and effective your ultimate solution might become upon later review.

You might find that setting a timer for fifteen to thirty minutes will help you to creatively push past the point when you think you are done. Another method might be to set a target for how many ideas you will generate. You might also consider using categories to trigger ideas. If you are brainstorming with a group, consider brainstorming individually for a while and then also brainstorming together as ideas can build from one idea to the next.

Step 4: Select a Solution

Once the brainstorming session is complete, then it is time to evaluate the solutions and select the more effective one.  Here you will consider how each solution will address the causes determined in step 2. It is also helpful to develop the criteria you will use when evaluating each solution, for instance, cost, time, difficulty level, resources needed, etc. Once your criteria for evaluation is established, then consider ranking each criterion by importance since some solutions might meet all criteria, but not to equally effective degrees.

In addition to evaluating by criteria, ensure that you consider possibilities and consequences of all serious contenders to address any drawbacks to a solution. Lastly, ensure that the solutions are actually feasible.

Step 6: Put Solution into Action

While many problem-solving models stop at simply selecting a solution, in order to actually solve a problem, the solution must be put into action. Here, you take responsibility to create, communicate, and execute the plan with detailed organizational logistics by addressing who will be responsible for what, when, and how.

Step 7: Evaluate progress

The final step when employing critical thinking to problem-solving is to evaluate the progress of the solution. Since critical thinking demands open-mindedness, analysis, and a willingness to change one’s mind, it is important to monitor how well the solution has actually solved the problem in order to determine if any course correction is needed.

While we solve problems every day, following the process to apply more critical thinking approaches in each step by considering what information might be missing; analyzing the problem and causes; remaining open-minded while brainstorming solutions; and providing criteria for, evaluating, and monitoring solutions can help you to become a better problem-solver and strengthen your critical thinking skills.

iterative process: one that can be repeatedly applied

• Problem solving. Authored by : Anne Fleischer. Provided by : Lumen Learning. License : CC BY: Attribution
• College Success. Authored by : Matthew Van Cleave. Provided by : Lumen Learning. License : CC BY: Attribution
• wocintech stock - 178. Authored by : WOCinTech Chat. Located at : https://flic.kr/p/FiGVWt . License : CC BY-SA: Attribution-ShareAlike
• Five whys. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Five_whys . License : CC BY-SA: Attribution-ShareAlike

Complex Problems, Sustainable Solutions - NXplorers Programme

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What is the course about?

Tariq Hussain

I am a Chartered Chemical Engineer, a Fellow of the IChemE with over 20 years in the hydrocarbon / energy industry specialising in process modelling and automation.

I am a Qualified Science Teacher and taught physics and engineering at high school, sixth form and university.

I am Shell's Programme Lead for NXplorers and as well as leading the programme, I conduct NXplorers workshops across the globe and most recently online.

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Complex Problem Solving Skills

Identifying complex problems and reviewing related information to develop and evaluate options and implement solutions.

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Problem-solving skills and how to improve them (with examples)

What’s life without its challenges? All of us will at some point encounter professional and personal hurdles. That might mean resolving a conflict with coworkers or making a big life decision. With effective problem solving skills, you’ll find tricky situations easier to navigate, and welcome challenges as opportunities to learn, grow and thrive. 

In this guide, we dive into the importance of problem solving skills and look at examples that show how relevant they are to different areas of your life. We cover how to find creative solutions and implement them, as well as ways to refine your skills in communication and critical thinking. Ready to start solving problems? Read on.

What is problem solving? 

Before we cover strategies for improving problem solving skills, it’s important to first have a clear understanding of the problem solving process. Here are the steps in solving a problem:

• Recognise the issue you are facing 
• Take a look at all the information to gain insights
• Come up with solutions
• Look at the pros and cons of each solution and how it might play out
• Plan, organise and implement your solution
• Continuously assess the effectiveness of the solution and make adjustments as needed

Problem solving skills

There’s more to problem solving than coming up with a quick fix. Effective problem solving requires wide range of skills and abilities, such as:

• Critical thinking: the ability to think logically, analyse information and look at situations from different perspectives.
• Creativity: being able to come up with innovative, out-of-the-box solutions.
• Decision-making:  making informed choices by considering all the available information.
• Communication:  being able to express ideas clearly and effectively.
• Analytical skills: breaking down complex problems into smaller parts and examining each one.
• Time management:  allocating time and resources effectively to address problems.
• Adaptability: being open to change and willing to adjust strategies.
• Conflict resolution:  skillfully managing conflicts and finding solutions that work for all.

Examples of problem solving skills

Problem solving skills in the workplace are invaluable, whether you need them for managing a team, dealing with clients or juggling deadlines. To get a better understanding of how you might use these skills in real-life scenarios, here are some problem solving examples that are common in the workplace.

• Analytical thinking

Analytical thinking is something that comes naturally to some, while others have to work a little harder. It involves being able to look at problem solving from a logical perspective, breaking down the issues into manageable parts. 

Example scenarios of analytical thinking

Quality control: in a manufacturing facility, analytical thinking helps identify the causes of product defects in order to pinpoint solutions.

Market research: marketing teams rely on analytical thinking to examine consumer data, identify market trends and make informed decisions on ad campaigns.

• Critical thinking

Critical thinkers are able to approach problems objectively, looking at different viewpoints without rushing to a decision. Critical thinking is an important aspect of problem solving, helping to uncover biases and assumptions and weigh up the quality of the information before making any decisions. 

Example scenarios of critical thinking

• Strategic planning:  in the boardroom, critical thinking is important for assessing economic trends, competitor threats and more. It guides leaders in making informed decisions about long-term company goals and growth strategies.
• Conflict resolution: HR professionals often use critical thinking when dealing with workplace conflicts. They objectively analyse the issues at hand and find an appropriate solution.

Decision-making

Making decisions is often the hardest part of problem solving. How do you know which solution is the right one? It involves evaluating information, considering potential outcomes and choosing the most suitable option. Effective problem solving relies on making well-informed decisions.

Example scenarios of decision-making

• Budget allocation: financial managers must decide how to allocate resources to various projects or departments. 
• Negotiation:  salespeople and procurement professionals negotiate terms, pricing and agreements with clients, suppliers and partners.

Research skills

Research skills are pivotal when it comes to problem solving, to ensure you have all the information you need to make an informed decision. These skills involve searching for relevant data, critically evaluating information sources, and drawing meaningful conclusions. 

Example scenarios of research skills

• Product development: a tech startup uses research skills to conduct market research to identify gaps and opportunities in the market. 
• Employee engagement:  an HR manager uses research skills to conduct employee surveys and focus groups.

A little creative flair goes a long way. By thinking outside the box, you can approach problems from different angles. Creative thinking involves combining existing knowledge, experiences and perspectives in new and innovative ways to come up with inventive solutions. 

Example scenarios of creativity

• Cost reduction: creative problem solvers within a manufacturing company might look at new ways to reduce production costs by using waste materials.
• Customer experience: a retail chain might look at implementing interactive displays and engaging store layouts to increase customer satisfaction and sales.

Collaboration

It’s not always easy to work with other people, but collaboration is a key element in problem solving, allowing you to make use of different perspectives and areas of expertise to find solutions.

Example scenarios

• Healthcare diagnosis: in a hospital setting, medical professionals collaborate to diagnose complex medical cases.
• Project management: project managers coordinate efforts, allocate resources and address issues that may arise during a project's lifecycle.

Conflict Resolution

Being able to mediate conflicts is a great skill to have. It involves facilitating open communication, understanding different perspectives and finding solutions that work for everyone. Conflict resolution is essential for managing any differences in opinion that arise.

Example scenarios of conflict resolution

• Client dispute: a customer might be dissatisfied with a product or service and demand a refund. The customer service representative addresses the issue through active listening and negotiation to reach a solution.
• Project delay: a project manager might face resistance from team members about a change in project scope and will need to find a middle ground before the project can continue.

Risk management

Risk management is essential across many workplaces. It involves analysing potential threats and opportunities, evaluating their impact and implementing strategies to minimise negative consequences. Risk management is closely tied to problem solving, as it addresses potential obstacles and challenges that may arise during the problem solving process.

Example scenarios of risk management

• Project risk management: in a construction project, risk management involves identifying potential delays, cost overruns and safety hazards. Risk mitigation strategies are developed, such as scheduling buffers and establishing safety protocols. 
• Financial risk management: in financial institutions, risk management assesses and manages risks associated with investments and lending.

Communication

Effective communication is a skill that will get you far in all areas of life. When it comes to problem solving, communication plays an important role in facilitating collaboration, sharing insights and ensuring that all stakeholders have the same expectations. 

Example scenarios of communication

• Customer service improvement:  in a retail environment, open communication channels result in higher customer satisfaction scores.
• Safety enhancement:  in a manufacturing facility, a robust communication strategy that includes safety briefings, incident reporting and employee training helps minimise accidents and injuries.

How to improve problem solving skills 

Ready to improve your problem solving skills? In this section we explore strategies and techniques that will give you a head start in developing better problem solving skills. 

Adopt the problem solving mindset

Developing a problem solving mindset will help you tackle challenges effectively . Start by accepting problems as opportunities for growth and learning, rather than as obstacles or setbacks. This will allow you to approach every challenge with a can-do attitude.

Patience is also essential, because it will allow you to work through the problem and its various solutions mindfully. Persistence is also important, so you can keep adapting your approach until you find the right solution.

Finally, don’t forget to ask questions. What do you need to know? What assumptions are you making? What can you learn from previous attempts? Approach problem solving as an opportunity to  acquire new skills . Stay curious, seek out solutions, explore new possibilities and remain open to different problem solving approaches.

Understand the problem

There’s no point trying to solve a problem you don’t understand. To analyse a problem effectively, you need to be able to define it. This allows you to break it down into smaller parts, making it easier to find causes and potential solutions. Start with a well-defined problem statement that is precise and specific. This will help you focus your efforts on the core issue, so you don’t waste time and resources on the wrong concerns.

Strategies for problem analysis

• Start with the problem statement and ask ‘Why?’ multiple times to dig deeper.
• Gather relevant data and information related to the problem. 
• Include those affected by the problem in the analysis process.
• Compare the current problem with similar situations or cases to gain valuable insights.
• Use simulations to explore potential outcomes of different solutions.
• Continuously gather feedback during the problem solving process. 

Develop critical thinking and creativity skills

Critical thinking and creativity are both important when it comes to looking at the problem objectively and thinking outside the box. Critical thinking encourages you to question assumptions, recognise biases and seek evidence to support your conclusions. Creative thinking allows you to look at the problem from different angles to reveal new insights and opportunities.

Enhance research and decision-making skills

Research and decision-making skills are pivotal in problem solving as they enable you to gather relevant information, analyse options and choose the best course of action. Research provides the information and data needed, and ensures that you have a comprehensive understanding of the problem and its context. Effective decision-making is about selecting the solution that best addresses the problem.

Strategies to improve research and decision-making skills

• Clearly define what you want to achieve through research.
• Use a variety of sources, including books, articles, research papers, interviews, surveys and online databases.
• Evaluate the credibility and reliability of your information sources.
• Incorporate risk assessment into your decision-making process. 
• Seek input from experts, colleagues and mentors when making important decisions. 
• After making decisions, reflect on the outcomes and lessons learned. Use this to improve your decision-making skills over time.

Strengthen collaboration skills

Being able to work with others is one of the most important skills to have at work. Collaboration skills enable everyone to work effectively as a team, share their perspectives and collectively find solutions. 

Tips for improving teamwork and collaboration

• Define people’s roles and responsibilities within the team. 
• Encourage an environment of open communication where team members feel comfortable sharing ideas.
• Practise active listening by giving full attention to others when they speak. 
• Hold regular check-in sessions to monitor progress, discuss challenges and make adjustments as needed.
• Use collaboration tools and platforms to facilitate communication and document progress. 
• Acknowledge and celebrate team achievements and milestones. 

Learn from past experiences

Once you’ve overcome a challenge, take the time to look back with a critical eye. How effective was the outcome? Could you have tweaked anything in your process? Learning from past experiences is important when it comes to problem solving. It involves reflecting on both successes and failures to gain insights, refine strategies and make more informed decisions in the future. 

Strategies for learning from past mistakes

• After completing a problem solving effort, gather your team for a debriefing session. Discuss what went well and what could have been better.
• Conduct a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) of resolved problems. 
• Evaluate the outcomes of past solutions. Did they achieve the desired results? 
• Commit to continuous learning and improvement. 

Leverage problem solving tools and resources

Problem-solving tools and resources are a great help when it comes to navigating complex challenges. These tools offer structured approaches, methodologies and resources that can streamline the process. 

Tools and resources for problem solving

• Mind mapping:  mind maps visually organise ideas, concepts and their relationships. 
• SWOT (Strengths, Weaknesses, Opportunities, Threats) Analysis:  helps in strategic planning and decision-making.
• Fishbone diagram (Ishikawa Diagram): this tool visually represents the potential root causes of a problem, helping you identify underlying factors contributing to an issue.
• Decision matrices:  these assist in evaluating options by assigning weights and scores to criteria and alternatives.
• Process flowcharts:  these allow you to see the steps of a process in sequence, helping identify where the problem is occuring.
• Decision support software:  software applications and tools, such as data analytics platforms, can help in data-driven decision-making and problem solving.
• Online courses and training: allow you to acquire new skills and knowledge.

Regular practice

Practice makes perfect! Using your skills in real life allows you to refine them, adapt to new challenges and build confidence in your problem solving capabilities. Make sure to try out these skills whenever you can.

Practical problem solving exercises 

• Do puzzles, riddles and brainteasers regularly. 
• Identify real-life challenges or dilemmas you encounter and practice applying problem solving techniques to these situations.
• Analyse case studies or scenarios relevant to your field or industry. 
• Regularly review past problem solving experiences and consider what you learned from them. 
• Attend workshops, webinars or training sessions focused on problem solving. 

How to highlight problem solving skills on a resumé

Effectively showcasing your problem solving skills on your resumé is a great way to demonstrate your ability to address challenges and add value to a workplace. We'll explore how to demonstrate problem solving skills on your resumé, so you stand out from the crowd.

Incorporating problem solving skills in the resumé summary

A resumé summary is your introduction to potential employers and provides an opportunity to succinctly showcase your skills. The resumé summary is often the first section employers read. It offers a snapshot of your qualifications and sets the tone for the rest of your resumé.

Your resumé summary should be customised for different job applications, ensuring that you highlight the specific problem solving skills relevant to the position you’re applying for.

Example 1: Project manager with a proven track record of solving complex operational challenges. Skilled in identifying root causes, developing innovative solutions and leading teams to successful project completion.

Example 2:  Detail-oriented data analyst with strong problem solving skills. Proficient in data-driven decision-making, quantitative analysis and using statistical tools to solve business problems.

Highlighting problem solving skills in the experience section

The experience section of your resumé presents the perfect opportunity to demonstrate your problem solving skills in action. 

• Start with action verbs: begin each bullet point in your job descriptions with strong action verbs such as, analysed, implemented, resolved and optimised.
• Quantify achievements: use numbers and percentages to illustrate the impact of your solutions. For example: Increased efficiency by 25% by implementing a new workflow process.
• Emphasise challenges: describe the specific challenges or problems you faced in your roles. 
• Solution-oriented language: mention the steps you took to find solutions and the outcomes achieved.

Including problem solving skills in the skills section

The skills section of your resumé should showcase your top abilities, including problem solving skills. Here are some tips for including these skills.

• Use a subsection:  within your skills section, you could create a subsection specifically dedicated to problem solving skills – especially if the role calls for these skills.
• Be specific: when listing problem solving skills, be specific about the types of role-related problems you can address. 
• Prioritise relevant skills:  tailor the list of problem solving skills to match the requirements of the job you're applying for. 

Examples of problem solving skills to include:

• Creative problem solving
• Decision making
• Root cause analysis
• Strategic problem solving
• Data-driven problem solving
• Interpersonal conflict resolution
• Adaptability
• Communication skills
• Problem solving tools
• Negotiation skills

Demonstrating problem solving skills in project sections or case studies

Including a dedicated section for projects or case studies in your resumé allows you to provide specific examples of your problem solving skills in action. It goes beyond simply listing skills, to demonstrate how you are able to apply those skills to real-world challenges.

Example – Data Analysis

Case Study: Market Expansion Strategy

• Challenge:  the company was looking to expand into new markets but lacked data on consumer preferences and market dynamics.
• Solution: conducted comprehensive market research, including surveys and competitor analysis. Applied this research to identify target customer segments and developed a data-driven market-entry strategy.
• Result:  successfully launched in two new markets, reaching our target of 30% market share within the first year.

Using problem solving skills in cover letters

A well-crafted cover letter is your first impression on any potential employer. Integrating problem solving skills can support your job application by showcasing your ability to address challenges and contribute effectively to their team. Here’s a quick run-down on what to include:

• Begin your cover letter by briefly mentioning the position you're applying for and your enthusiasm for it.
• Identify a specific challenge or issue that the company may be facing, to demonstrate your research and understanding of their needs.
• Include a brief story or scenario from your past experiences where you successfully applied problem solving skills to address a similar challenge. 
• Highlight the positive outcomes or results achieved through your problem solving efforts. 
• Explain how your skills make you the ideal person to address their specific challenges.

Problem solving skills are essential in all areas of life, enabling you to overcome challenges, make informed decisions, settle conflicts and drive innovation. We've explored the significance of problem solving skills and how to improve, demonstrate and leverage them effectively. It’s an ever-evolving skill set that can be refined over time. 

By actively incorporating problem solving skills into your day-to-day, you can become a more effective problem solver at work and in your personal life as well.

What are some common problem solving techniques?

Common problem solving techniques include brainstorming, root cause analysis, SWOT analysis, decision matrices, the scientific method and the PDCA (Plan-Do-Check-Act) cycle. These techniques offer structured approaches to identify, analyse and address problems effectively.

How can I improve my critical thinking skills?

Improving critical thinking involves practising skills such as analysis, evaluation and problem solving. It helps to engage in activities like reading, solving puzzles, debating and self-reflection.

What are some common obstacles to problem solving?

Common obstacles to problem solving include biases, lack of information or resources, and resistance to change. Recognising and addressing these obstacles is essential for effective problem solving.

How can I overcome resistance to change when implementing a solution?

To overcome resistance to change, it's essential to communicate the benefits of the proposed solution clearly, involve stakeholders in the decision-making process, address concerns and monitor the implementation's progress to demonstrate its effectiveness.

How can problem solving skills benefit my career?

Problem solving skills are highly valuable in a career as they enable you to navigate challenges, make informed decisions, adapt to change and contribute to innovation and efficiency. These skills enhance your professional effectiveness and can lead to career advancement and increased job satisfaction.

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Fall 2024 Course Descriptions

Cics 110: foundations of programming, instructor(s): meng-chieh chiu, aadam anish lokhandwala, ghazaleh parvini, shahrooz pouryousef, cole reilly, prit pritam shah, cooper sigrist.

An introduction to computer programming and problem solving using computers. This course teaches you how real-world problems can be solved computationally using programming constructs and data abstractions of a modern programming language. Concepts and techniques covered include variables, expressions, data types, objects, branching, iteration, functions, classes, and methods. We will also cover how to translate problems into a sequence of instructions, investigate the fundamental operation of a computational system and trace program execution and memory, and learn how to test and debug programs. Open to freshmen, sophomores, and juniors in Computer Science and Informatics. No previous programming experience required. (Gen. Ed. R2) Open to freshmen, sophomores, and juniors in Computer Science and Informatics. Prerequisite: R1 (or a score of 15 or higher on the math placement test Part A), or one of the following courses: MATH 101&102 or MATH 104 or MATH 127 or MATH 128 or MATH 131 or MATH 132. 4 credits.

CICS 160: Object-Oriented Programming

Instructor(s): jaime davila, cole reilly.

This course will expose students to programming practices beyond the introductory level, concentrating on Object Oriented Programming techniques and an introduction to Data Structures. Students will also study and analyze the complexity of both the algorithms presented in class and of the algorithms they develop. This course also provides experience with the development and analysis of recursive algorithms and programs. Before taking this course, students are expected to have been exposed to the following concepts through a college-level course or equivalent in some high level computer programming language: input and output operations, conditional statements, loops, arrays, recursion, and functions/methods. The course places an emphasis on the careful design and testing of programs. (Gen. Ed. R2) Open to freshmen, sophomores, and juniors in Computer Science and Informatics. Prerequisite: CICS 110 (previously INFO 190S) or COMPSCI 121 with a grade of C or above. 4 credits.

CICS 208: Defending Democracy in a Digital World

Instructor(s): ethan zuckerman.

This course explores the significance of the public sphere - from pamphlets, newspapers and letters to radio, television, the internet and social media - and its relationship to participatory, democratic society. Moving back and forth between the history of the public sphere and contemporary debates about the tensions between media and democracy, students will learn why democracies prescribe protected roles of the media, how media manipulation plays a role in politics, and how media spaces serve as deliberative spaces. Students will write short reaction papers to the readings, which will be used to shape class discussions, and a longer final paper, focused on applying the theories of the public sphere to regulation of contemporary online spaces. This course does not count toward CS or INFORM Major requirements. Cross-listed with COMM/SPP 208. (Gen. Ed. SB) 3 credits.

CICS 210: Data Structures

Instructor(s): mordecai golin, timothy richards.

An introduction to the design, analysis, and implementation of data structures. This course teaches you how to build, test, debug, document, and evaluate objects that encapsulate data and their associated operations using programming constructs and data abstractions of a modern programming language. Concepts and techniques covered include linear and non-linear structures, recursive structures and algorithms, traversal algorithms, binary search trees, balanced trees, priority queues, union-find, hash tables, bloom filters, and graphs. We will also informally compare and contrast the run time efficiency of algorithms and their performance characteristics including the concept of worst-case running time analysis and the classification of algorithms in terms of constant, logarithmic, linear, log linear, quadratic, and exponential time using Big-O notation. (Gen. Ed. R2) Open to freshmen, sophomores, and juniors in Computer Science and Informatics. Prerequisite: CICS 160 (previously INFO 190T) with a grade of C or better. 4 credits.

CICS 237: Introduction to Research in the Discipline

Instructor(s): neena thota.

The Introduction to Research in the Discipline course is part of the CICS Early Research Scholars Program (ERSP). It provides a group-based, dual-mentored research structure designed to provide a supportive and inclusive first research experience for a large number of early-career Computer Science and Informatics majors. 2 credits.

CICS 256: Make: A Hands-on Introduction to Physical Computing

Instructor(s): rui wang.

Inspired by the Maker movement, this course provides a hands-on introduction to physical computing: sensing and responding to the physical world using computers. Specific topics include: basic electronics and circuit design, microcontroller programming using Arduinos, sensing and responding to the physical world, rapid prototyping (3D printing and laser cutting etc.), soft circuits and wearable electronics. The course will encourage and empower students to invent, design, and build practical hardware projects that interact with the physical world. This course has a required lab section, and counts as one of the CS Lab Science Requirement courses for the BS-CS. Open to freshman and sophomore BS-CS students. Prerequisite: COMPSCI 187 (or CICS 210) with a grade of C or better and completion of the R1 (Basic Math Skills) Gen. Ed. 4 credits.

CICS 291T: Seminar - CICS Transfer Success

Instructor(s): emma anderson.

This seminar is intended to help you become fully prepared to succeed in CICS at UMass. Students in this seminar will be led by an instructor with a detailed understanding of the transfer student experience, and supported by various staff members in CICS. You will learn about which campus and College resources will be most helpful to you, how to best utilize these resources, and where you can look for other opportunities to connect. 1 credit.

CICS 298A: Practicum - Leadership: Communicating Across Expertise

Instructor(s): emma anderson, boming zhang.

No matter where you end up in tech, you will need to explain concepts, products and ideas to people with different technical backgrounds. This course is intended to help prepare you for these communication tasks. Through the lens of tutoring, we will work on explaining technical ideas clearly and compassionately to others. We will do some theoretical study, including a history of CS education as well asbrain and learning science, and some practice, including tutoring beginning students in CS. This course is intended for a broad range of students looking to pursue careers in tech, but will be particularly useful for those who are currently UCAs or intending to apply for UCA positions in the future. Open to freshmen, sophomores, and juniors in Computer Science and Informatics. Prerequisite: a grade of C or better in CICS 160 (previously INFO 190T), COMPSCI 186, or COMPSCI 187. 1 credit.

CICS 305: Social Issues in Computing

Instructor(s): erin butler, ryan cadrette, elizabeth gunther, siobhan meï, justin obara, matthew ross, christina sutcliffe.

Through a careful analysis and discussion of a range of computing issues, topics, and polices, we will explore various impacts of computers on modern society. This class satisfies the Junior Year Writing requirement by providing directed practice and specific instruction in a range of writing genres. Students will produce approximately 20-25 pages of polished written work over the course of the semester. CICS Primary Majors only. Prerequisite: CS Majors: ENGLWRIT 112 (or English Writing waiver), COMPSCI 220, COMPSCI 230 and COMPSCI 240 (or 250); INFORM Majors: ENGLWRIT 112 (or English Writing waiver) and INFO 248. 3 credits.

CICS 580: Introduction to Numerical Computing with Python

Instructor(s): ali montazeralghaem.

This course is an introduction to computer programming for numerical computing. The course is based on the computer programming language Python and is suitable for students with no programming or numerical computing background who are interested in taking courses in machine learning, natural language processing, or data science. The course will cover fundamental programming, numerical computing, and numerical linear algebra topics, along with the Python libraries that implement the corresponding data structures and algorithms. The course will include hands-on programming assignments and a project. No prior programming experience is required. Familiarity with undergraduate-level probability, statistics and linear algebra is assumed. Does not count toward graduate degrees. Open to Graduate students only. 1 credit.

COMPSCI 119: Introduction to Programming

Instructor(s): allison poh.

A complete introduction to computer programming using the Python language. Topics include coverage of all the supported data types and program code structures, functions (up through lambda expressions and recursion), reasoning about and debugging existing code, implementation of custom libraries, selection of data structures, and the fundamentals of object-oriented programming. Students will create, debug, and run Python 3 programs that explore each of these topics in turn, from simple loops up through the processing of large data sets, and eventually to the creation of professional-quality libraries to synthesize graphics images and audio files. No prior programming experience expected. Open to freshmen and sophomores in any major EXCEPT Computer Science. 3 credits.

COMPSCI 198C: Practicum - Introduction to the C Programming Language

Instructor(s): meng-chieh chiu, timothy richards.

This practicum assumes general background and experience in computer programming (such as that provided by COMPSCI 121 or a similar introductory programming course) and some knowledge of data structures. Content will include basic C data types, declarations, expressions, statements, and functions; simple use of macros; some common library calls (such as formatted input/output); basic pointer manipulation using linked lists; and introduction to using standard tools (gcc and make). A required prerequisite for COMPSCI 230, effective Fall 2023. This course is open to Freshmen, Sophomores and Juniors. Prerequisite: COMPSCI 121 WITH A GRADE OF B OR BETTER, OR CICS 160 (PREVIOUSLY INFO 190T OR COMPSCI 186 OR CICS 210) WITH A GRADE OF C OR BETTER. 1 credit.

COMPSCI 220: Programming Methodology

Instructor(s): marius minea.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs, including: design strategies and patterns, using functional and object-oriented approaches, testing and program verification, code refactoring, interfacing with libraries. There will be significant programming and mid-term and final examinations. Open to Computer Science majors only. Prerequisite: CICS 210 (or COMPSCI 187) with a grade of C or better. 4 credits.

COMPSCI 230: Computer Systems Principles

Instructor(s): phuthipong bovornkeeratiroj, meng-chieh chiu.

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results. On the other hand, many computer systems don't perform nearly as well as Google - hence the now-clich‚ "the system is down." In this class, we study the scientific principles behind the construction of high-performance, scalable systems. The course begins with a discussion of C data representation, and moves up the stack from there to the features of modern architectures, assembly languages, and operating system services such as I/O, process, and synchronization. This class assumes students have either taken COMPSCI 198C or have equivalent experience in the C programming language. Open to Computer Science majors only. Prerequisite: CICS 210 (or COMPSCI 187) with a grade of C or above and COMPSCI 198C. 4 credits.

COMPSCI 240: Reasoning Under Uncertainty

Instructor(s): shiting lan, ghazaleh parvini.

Development of mathematical reasoning skills for problems that involve uncertainty. Each concept will be illustrated by real-world examples and demonstrated through in-class and homework exercises. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large numbers, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Networks. Statistical topics such as estimation of parameters and linear regression, as time permits. Open to Computer Science majors only. Prerequisites: COMPSCI 187 (or CICS 160;INFO 190T or CICS 210) and a grade of C or above in MATH 132. 3 credits.

COMPSCI 250: Introduction to Computation

Instructor(s): david barrington, mordecai golin.

Basic concepts of discrete mathematics useful to computer science: set theory, strings and formal languages, propositional and predicate calculus, relations and functions, basic number theory. Induction and recursion: interplay of inductive definition, inductive proof, and recursive algorithms. Graphs, trees, and search. Finite-state machines, regular languages, nondeterministic finite automata, Kleene's Theorem. Problem sets, 2 midterm exams, timed final. Open to COMPSCI and Math majors only. Prerequisite: CICS 160 (PREVIOUSLY INFO 190T OR COMPSCI 187 OR ECE 241 OR CICS 210) AND MATH 132, BOTH WITH A GRADE OF C OR BETTER. 4 credits.

COMPSCI 311: Introduction to Algorithms

Instructor(s): ghazaleh parvini, hava siegelmann.

This course will introduce you to a variety of techniques to design algorithms, such as divide and conquer, greedy, dynamic programming, and network flow. You will learn to study the performance of various algorithms within a formal, mathematical framework. You will also learn how to design very efficient algorithms for many kinds of problems and recognize problems that currently do not have efficient algorithms. Assignments may include programming: you should be able to program in Java, C, or some other closely related language. Mathematical experience (as provided by COMPSCI 250) is required. This course is required for the CS Major (BS) and counts as an Elective toward the CS Major (BA). Open to senior and junior Computer Science majors only. Prerequisite: COMPSCI 187 (or CICS 210) and either COMPSCI 250 or MATH 455, all with a grade of C or better. 4 credits.

COMPSCI 320: Introduction to Software Engineering

Instructor(s): gordon anderson, yuriy brun, heather conboy, jaime davila.

In this course, students learn and gain practical experience with software engineering principles and techniques. The practical experience centers on a semester-long team project in which a software development project is carried through all the stages of the software life cycle. Topics in this course include requirements analysis, specification, design, abstraction, programming style, testing, maintenance, communication, teamwork, and software project management. Particular emphasis is placed on communication and negotiation skills and on designing and developing maintainable software. Use of computer required. Several written assignments, in-class presentations, and a term project. This course satisfies the IE Requirement and counts as a CS Elective for the CS Major. Open to senior and junior Computer Science majors only. Prerequisite: COMPSCI 220 with a grade of 'C' or better. 4 credits.

COMPSCI 325: Introduction to Human Computer Interaction

Instructor(s): ravi karkar.

Human-Computer Interaction design is "design for human use". Computers are a ubiquitous part of many interactions in our lives, from the mundane everydayness of light switches and "smart" vending machines to entertainment and education to sophisticated instruments and complex energy and defense systems. In this course, we will challenge you to broaden your grasp of what a user interface can and should be, and try your hand at doing better yourself. It is a fast-paced, hands-on, project-based experience that will challenge many of your ideas of what computer science is and can be. It is designed around active lecture sessions supported by readings, working classes, and team projects, where students practice and explore the concepts introduced in lecture, and go well beyond them to learn and apply HCI techniques that build into group projects. More specifically, the course adopts a human-centered design (HCD) approach and teaches a highly iterative process called design thinking. The design thinking process draws heavily on the fundamentals of human-computer interaction (HCI) methods. I also cover design methodologies, evaluation methodologies (both quantitative and qualitative), human information processing, cognition, and perception. This course counts as a CS Elective toward the CS Major and as a Required Core for the INFORM Major. Open to juniors and seniors in Computer Science or Informatics. Prerequisite: Prerequisites: COMPSCI 187 (or CICS 210) with a grade of C or better OR INFO 248 and COMPSCI 186 (or 187 or CICS 160;INFO 190T) with a grade of C or better. 3 credits.

COMPSCI 326: Web Programming

Instructor(s): timothy richards.

The web is arguably today's most important application platform. Web browsers run on practically every device, and even many phone applications are in fact web applications under the covers. This course will cover a broad range of client-side web technologies, including HTTP itself, HTML5, CSS, and JavaScript; it will additionally cover key concepts for the server side of web applications, including key value stores and SQL servers. This course will also cover key concepts and technologies including AJAX, JavaScript libraries (e.g., jQuery), and web security. This course is hands-on and heavily project-based; students will construct a substantial dynamic web application based on the concepts, technologies, and techniques presented during lectures and in readings. This course satisfies the IE Requirement and an Elective for both the CS and INFORM Majors. Open to juniors and seniors in Computer Science or Informatics. Prerequisite: COMPSCI 220 (OR COMPSCI 230) WITH A GRADE OF C OR BETTER. Note: as the name web programming denotes, programming is a key component of this class. Previous background in JavaScript is strongly recommended. 4 credits.

COMPSCI 328: Mobile Health Sensing and Analytics

Instructor(s): deepak ganesan.

The typical smartphone comes equipped with a plethora of sensors for monitoring activity, speech patterns, social interactions, and location. In addition, mobile accessories such as wearable wristbands now enable routine and continuous monitoring of a host of physiological signals (e.g., heart rate, respiratory rate, oxygen saturation, and others.). In conjunction, these sensors can enable higher-order inferences about more complex human activities/behavioral states (e.g., activity patterns, stress, sleep, social interactions, etc.). Such ubiquitous sensing in daily life, referred to as mobile health sensing, promises to revolutionize our understanding of human activities and health conditions. This course is a hands-on introduction to personal health sensing through mobile phones. Please note that this is a *programming-heavy* class so a solid programming background is required. All programming assignments are in Python, so programming experience with *Python* is recommended.This course counts as an Elective for the CS Major. Open to juniors and seniors in Computer Science or Informatics. Prerequisite: COMPSCI 187 (or CICS 210) with a grade of C or better OR INFO 248 and COMPSCI 186 (or 187 or CICS 160;INFO 190T) with a grade of C or better.. 3 credits.

COMPSCI 335: Inside the Box: How Computers Work

Instructor(s): charles weems.

How does the computer actually work? In this course we peel away the layers of abstraction and look at how switches become logic circuits, how logic circuits do math, and how programs really execute. We will wire up some simple examples of logic, then move on to programming an embedded ARM processor in a mix of assembly language and C, interfacing with various I/O devices and sensors, to experience what happens when machine code executes. We will also see the impact of hidden acceleration mechanisms like caches, pipelines, and branch predictors. This course counts as a CS Elective for the CS Major. Open to senior and junior Computer Science majors only. Prerequisite: Prerequisite: COMPSCI 220 or 230 with a grade of 'C' or better. 3 credits.

COMPSCI 345: Practice and Applications of Data Management

Instructor(s): alexandra meliou.

Computing has become data-driven, and databases are now at the heart of commercial applications. The purpose of this course is to provide a comprehensive introduction to the use of data management systems within the context of various applications. Some of the covered topics include application-driven database design, schema refinement, implementation of basic transactions, data on the web, and data visualization. This course counts as a CS Elective toward the CS Major. Students who have completed COMPSCI 445 are not eligible to take this course without instructor permission. Open to juniors and seniors in Computer Science or Informatics. Prerequisite: Prerequisites: COMPSCI 187 (or CICS 210) with a grade of C or better OR INFO 248 and COMPSCI 186 (or 187 or CICS 160;INFO 190T) with a grade of C or better. 3 credits.

COMPSCI 360: Introduction to Computer and Network Security

Instructor(s): shiqing ma.

This course provides an introduction to the principles and practice of computer and network security. A focus on both fundamentals and practical information will be stressed. The three key topics of this course are cryptography, privacy, and network security. Subtopics include ciphers, hashes, key exchange, security services (integrity, availability, confidentiality, etc.), security attacks, vulnerabilities, anonymous communications, and countermeasures. This course counts as a CS Elective for the CS Major. Open to senior and junior Computer Science majors only. Prerequisite: COMPSCI 230 with a grade of 'C' or better. 3 credits.

COMPSCI 363: Computer Crime Law

Instructor(s): marvin cable.

A study, analysis, and discussion of the legal issues related to crimes involving computers and networks, including topical actions by dissidents and governments. We will also study the technologies of forensic investigation, intelligence gathering, privacy enhancement, and censorship resistance. Our main legal topics will include recent and important case law, statutes, and constitutional clauses concerning authorization, access, search and seizure, wiretaps, the right to privacy, and FISA. Our technology topics will include methods of investigation and resistance in the context of the Internet and Cellular networks. Students are assumed to have no background in legal concepts. Students will be required to complete substantial legal readings, complete significant written analysis of rulings, learn about technologies in detail, and participate in lively class discussion. This course counts as a CS Elective for the CS Major.Open to senior and junior Computer Science majors only. Prerequisite: Prerequisite: COMPSCI 230 with a grade of 'C' or better AND either ENGLWRIT 112 (with a grade of 'C' or better) or the completion of the 'CW' General Education requirement. 3 credits.

COMPSCI 377: Operating Systems

Instructor(s): phuthipong bovornkeeratiroj.

In this course we examine the important problems in operating system design and implementation. The operating system provides a well-known, convenient, and efficient interface between user programs and the bare hardware of the computer on which they run. The operating system is responsible for allowing resources (e.g., disks, networks, and processors) to be shared, providing common services needed by many different programs (e.g., file service, the ability to start or stop processes, and access to the printer), and protecting individual programs from one another. The course will start with a brief historical perspective of the evolution of operating systems over the last fifty years, and then cover the major components of most operating systems. This discussion will cover the tradeoffs that can be made between performance and functionality during the design and implementation of an operating system. Particular emphasis will be given to three major OS subsystems: process management (processes, threads, CPU scheduling, synchronization, and deadlock), memory management (segmentation, paging, swapping), file systems, and operating system support for distributed systems. This course counts as a CS Elective for the CS Major. Open to senior and junior Computer Science majors only. Prerequisite: COMPSCI 230 with a grade of 'C' or better. 4 credits.

COMPSCI 383: Artificial Intelligence

Instructor(s): william mcnichols.

This course aims to give students a high level understanding of the prominent AI topics that are being employed in industry today. It will provide an introduction to each topic, an overview of its supporting algorithms, and examples of products powered by the technology. Particular emphasis will be had on Machine Learning and developing hands-on practical skills with this technology. Upon completion of this course, students will obtain a wider scope of understanding about modern AI trends in software technology and develop an intuition for how this software works. To succeedin this course, students will need a fundamental understanding of data structures and programming fundamentals. Graph and tree data structures will be used in particular. Programming assignments in this class will be done using Python. Experience in at least one programming language is required and it s strongly recommended you have some Python experience before starting. A mathematical foundation in statistics and linear algebra is not strictly necessary but will deepen understanding of course material. Open to senior and junior Computer Science majors only. This course counts as an Elective toward the CS and INFORM Majors. Prerequisite: [CICS 210 AND COMPSCI 240 (OR STATISTC 315; PREVIOUSLY STATISTC 515), BOTH WITH A GRADE OF C OR BETTER] OR [INFO 348 AND STATISTC 315; PREVIOUSLY STATISTC 515, BOTH WITH A GRADE OF C OR BETTER.] 3 credits.

COMPSCI 410: Compiler Techniques

Instructor(s): hui guan.

This course explores the basic problems in the translation of programming languages focusing on theory and common implementation techniques for compiling traditional block structured programming languages to produce assembly or object code for typical machines. The course involves a substantial laboratory project in which the student constructs a working compiler for a considerable subset of a realistic programming language, within a provided skeleton. The lectures are augmented by a discussion section that covers details of the programming language used to build the compiler, the operating system, the source language, and various tools. Use of computer required. Text: Engineering a Compiler, Cooper and Torczon. Open to senior and junior Computer Science majors only. This course counts as an Elective toward the CS Major. Prerequisites: COMPSCI 230 and either COMPSCI 250 or MATH 455, all with a grade of C or better.. 3 credits.

COMPSCI 420: Software Entrepreneurship

Instructor(s): matthew rattigan.

This course is geared towards students interested in developing software that moves from early stage proof-of-concept ideas towards marketable products with societal benefit. The course leverages the expertise of the Entrepreneurs in Residence (EIR) of the Ventures @ CICS initiative at CICS. The course is grounded in Challenge Based Learning (CBL), an active, student-directed instructional framework that was developed by Apple Inc. and educators. This course counts as a CS Elective for the CS Major. Open to juniors and seniors in Computer Science or Informatics. Students enrolled in the Undergraduate Certificate in Public Interest Technology are eligible via override. 3 credits.

COMPSCI 429: Software Engineering Project Management

The purpose of this course is to provide students with practical experience in the management of software development projects. Students in this course will gain this experience by serving as software development team technical managers for teams of software engineering students in COMPSCI 320. As project managers, the students in COMPSCI 429 will be responsible for: supervising and managing the work of teams of COMPSCI 320 students; interfacing with the other COMPSCI 429 students managing other teams in the course; interfacing with the course instructor, course TA, and course customer. COMPSCI 429 students will be assigned readings in software engineering project management to provide a theoretical basis for their work in this course. But the majority of work in the course will be related to the actual management of assigned development teams. As team managers, COMPSCI 429 students will set goals and schedules for their teams, track and report team progress, negotiate with leaders of other teams and the course customer, and evaluate the work of members of their teams. COMPSCI 429 course assignments may include: written team goals, plans and schedules; periodic reports on team progress; documentation of agreements reached with other team leaders and customers; evaluations of the applicability of theoretical papers to the work of this course. This course will meet at the same times and places as COMPSCI 320. Additional meetings with team members and other students in COMPSCI 429 are also expected to be arranged by mutual agreement An additional one hour weekly meeting of all of the students in COMPSCI 429 is required. This course counts as a CS Elective for the CS Major. Open to undergraduate Computer Science majors only. Enrollment in this course is only by permission of the instructor, and is restricted to students who have previously taken COMPSCI 320, and received a grade of B or better. 3 credits.

COMPSCI 445: Information Systems

Instructor(s): marco serafini.

This course is an introduction to the efficient management of large-scale data. The course includes principles for representing information as structured data, query languages for analyzing and manipulating structured data, and core systems principles that enable efficient computation on large data sets. Classical relational database topics will be covered (data modeling, SQL, query optimization, concurrency control), as well as semi-structured data (XML, JSON), and distributed data processing paradigms (e.g. MapReduce and Spark). Additional application topics may include web application development, data integration, processing data streams, database security and privacy. Open to senior and junior Computer Science majors only. This course counts as an Elective toward the CS Major. Prerequisites: COMPSCI 220 (or 230) and COMPSCI 311 and COMPSCI 345 with a grade of C or better. 3 credits.

COMPSCI 446: Search Engines

Instructor(s): james allan.

This course provides an overview of the important issues in information retrieval, and how those issues affect the design and implementation of search engines. The course emphasizes the technology used in Web search engines, and the information retrieval theories and concepts that underlie all search applications. Mathematical experience (as provided by COMPSCI 240) is required. You should also be able to program in Java or Python (other closely related languages may be acceptable) Open to senior and junior Computer Science majors only. This course counts as a CS Elective for the CS Major. Prerequisite: Prerequisite: COMPSCI 240 or COMPSCI 383 with a grade of 'C' or better. 3 credits.

COMPSCI 453: Computer Networks

Instructor(s): parviz kermani.

This course provides an introduction to fundamental concepts in the design and implementation of computer networks, their protocols, and applications with a particular emphasis on the Internet's TCP/IP protocol suite. Topics to be covered include: overview of network architectures, applications, network programming interfaces (e.g., sockets), transport, congestion, routing, and data link protocols, addressing, local area networks, wireless networks, network security, and network management. There will be five or six homeworks, two programming projects, several hands-on labs (that require an Internet-connected personal computer) and two exams. Open to senior and junior Computer Science majors only. This course counts as a CS Elective for the CS Major. Prerequisite: COMPSCI 230 (or COMPSCI 377) with a grade of 'C' or better. 3 credits.

COMPSCI 461: Secure Distributed Systems

Instructor(s): gregory stone.

This is a class devoted to the study of securing distributed systems, with blockchain-based cryptocurrencies serving as our real platform of interest. We'll start with the fundamentals of Lamport's, Fischer's, and Douceur's results that fence-in all consensus system, and discuss Byzantine fault tolerance. We'll also look at the efficiency of the network architectures for peer-to-peer/distributed system communication and attacks on their security, such as denial of service attacks. And we'll review relevant applied cryptography such as elliptic curves. We ll discuss in detail the mechanisms of Bitcoin and Ethereum and we ll program distributed applications for Ethereum. Other topics include economics and finance. Assignments will include programming projects and reading research papers. The grade is also based on exams and participation in discussion. The course is based on a flipped classroom and uses a hybrid instruction model. Some of the course content is delivered online, however students are required to attend weekly class meetings. This course counts as a CS Elective for the CS Major, as well an Any 2 menu choice for the former Security & Privacy track. Open to Computer Science majors only. Prerequisite: COMPSCI 326, COMPSCI 345, COMPSCI 377, COMPSCI 453, or COMPSCI 497P with a grade of C or better. 3 credits.

COMPSCI 485: Applications of Natural Language Processing

Instructor(s): brendan o'connor.

This course will introduce NLP methods and applications, such as text classification, sentiment analysis, machine translation, and other applications to identify and use the meaning of text. During the course, students will (1) learn fundamental methods and algorithms for NLP; (2) become familiar with key facts about human language that motivate them, and help practitioners know what problems are possible to solve; and (3) complete a series of hands-on projects to use, implement, experiment with, and improve NLP tools. This course counts as a CS Elective for the CS Major. Prerequisite: COMPSCI 220 and COMPSCI 240, or LINGUIST 429B (previously LINGUIST 492B). 3 credits.

COMPSCI 490Q: Quantum Information Science

Instructor(s): filip rozpedek.

Quantum information science (QIS) revolutionizes our understanding of the fundamental laws of the universe and promises world-altering improvements in a number of practical computational tasks. For theoretical computer scientists, QIS provides the means to unlock the ultimate computational powers available to us in this universe. For programmers and computer engineers, QIS offers the tools to run simulations and optimizations otherwise infeasible on classical computers. For theoretical physicists, QIS gives us hope of resolving paradoxes foundational to our understanding of Nature. And for experimentalists and engineers, QIS also enables the creation of exquisite sensors and novel communication tools, far outperforming what is classically permitted. This class will introduce the notion of quantum probability amplitudes, i.e., the "correct" probabilistic description of Nature, and describe how these quantum phenomena permit the creation of new types of computational machines. The introduction to foundational quantum information science will be followed by a few practical (and impractical) quantum algorithms, illustrating the counterintuitive computational powers of quantum mechanics. The latter half of the class would focus on the difficulties of creating such extremely fragile computational machines in our noisy and unforgiving real world. This course counts as a CS Elective for the CS Major. Open to junior and senior Computer Science and Informatics majors. Prerequisite: Math 132 and 235 and either COMPSCI 240 or STATISTIC 315;515, all with a grade of C or better. 3 credits.

COMPSCI 514: Algorithms for Data Science

Instructor(s): andrew mcgregor, cameron musco.

With the advent of social networks, ubiquitous sensors, and large-scale computational science, data scientists must deal with data that is massive in size, arrives at blinding speeds, and often must be processed within interactive or quasi-interactive time frames. This course studies the mathematical foundations of big data processing, developing algorithms and learning how to analyze them. We explore methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning. This course counts as a CS Elective for the CS Major. Open to Junior and Senior COMPSCI students. Undergraduate Prerequisite: COMPSCI 240 (or STATISTCS 515) and COMPSCI 311 with a grade of B+ or better in both, OR COMPSCI 240, STATISTCS 515, COMPSCI 311, Math 233, and Math 235 with a grade of C or better in each. 3 credits

COMPSCI 515: Algorithms, Game Theory and Fairness

Instructor(s): yair zick.

Recent years have seen a dramatic rise in the use of algorithms for solving problems involving strategic decision makers. Deployed algorithms now assist in a variety of economic interactions: assigning medical residents to schools, allocating students to courses, allocating security resources in airports, allocating computational resources and dividing rent. We will explore foundational topics at the intersection of economics and computation, starting with the foundations of game theory: Nash equilibria, the theory of cooperative games, before proceeding to covering more advanced topics: matching algorithms, allocation of indivisible goods, and mechanism design. Open to junior and senoir Computer Science students. This course counts as a CS Elective for the CS Major. Undergraduate Prerequisite: COMPSI 240 and 250 with a grade of C or better in both. 3 credits.

COMPSCI 520: Theory and Practice of Software Engineering

Instructor(s): juan zhai.

Introduces students to the principal activities and state-of-the-art techniques involved in developing high-quality software systems. Topics include: requirements engineering, formal specification methods, design principles & patterns, verification & validation, debugging, and automated software engineering. This course counts as a CS Elective for the CS Major.Open to junior and senior Computer Science students. Undergraduate Prerequisites: COMPSCI 320 (or COMPSCI 220 and 326) with a grade of C or better. 3 credits.

COMPSCI 528: Mobile and Ubiquitous Computing

Instructor(s): phuc nguyen.

This course will introduce students to the field of mobile sensing and ubiquitous computing (Ubicomp) an emerging CS research area that aims to design and develop disruptive technologies with hardware and software systems for real-world messy, noisy and mobile scenarios. The students will learn how to build mobile sensing systems, how to implement it with ubiquitous computing tools, how to make sense of the sensor data and model the target variables. Lastly, the students will learn how to critically think about problems in many application areas including Human-Computer Interaction, Medicine, Sustainability, Transportation, Psychology and Economics, and subsequently practice to find appropriate Ubicomp solutions. There is no exam in this course. The student is expected to work on different hands-on assignments, critique writing, and a final project. This course counts as an Elective toward the CS Major. Open to senior and junior Computer Science majors only Undergraduate Prerequisites: COMPSCI 230 and 240 with a grade of C or better. 3 credits.

COMPSCI 532: Systems for Data Science

Instructor(s): peter klemperer.

In this course, students will learn the fundamentals behind large-scale systems in the context of data science. We will cover the issues involved in scaling up (to many processors) and out (to many nodes) parallelism in order to perform fast analyses on large datasets. These include locality and data representation, concurrency, distributed databases and systems, performance analysis and understanding. We will explore the details of existing and emerging data science platforms, including MapReduce-Hadoop, Spark, and more. This course counts as a CS Elective for the CS Major. Open to junior and senior Computer Science students. Undergraduate Prerequisites: COMPSCI 377 and 445 with a grade of C or better in each. 3 credits.

COMPSCI 560: Introduction to Computer and Network Security

This course provides an introduction to the principles and practice of computer and network security with a focus on both fundamental principles and practical applications through hands-on approach. Many of the principles are taught through examples. The key topics of this course are a brief introduction to computer networking; applied cryptography; protecting users, data, and services; network security, and common threats and defense strategies. Students will complete a number of practical lab assignments as well as auto-graded quizzes/assignments. This course counts as a CS Elective for the CS Major. Open to undergraduate COMPSCI and CS-ENG students Undergraduate Prerequisite: COMPSCI 453 or E&C-ENG 374 with a grade of C or better. 3 credits.

COMPSCI 563: Internet Law and Policy

This course is meant for those looking for legal knowledge for use in computing- and Internet-related endeavors. The course will include topics related to security, policy, and the use of machine learning and related technologies. In additional, students will be assigned law review articles and will learn to do legal research so that they can remain updated after the course ends. Topics covered are all in the context of the ubiquity of the Internet and computing, and they include: basic legal principles, contract law, substantive laws, intellectual property law, ethics, dealing with third parties, policy issues, and topical issues such as implications of applying machine learning technology. This course was formerly numbered as INFOSEC 690L. This course counts as a CS Elective for the CS Major. Open to junior and senior Computer Science students. Undergraduate Prerequisite: either COMPSCI 311, 383, or 360 (previously 460) with a grade of C or better. 3 credits.

COMPSCI 575: Combinatorics and Graph Theory

Instructor(s): mark wilson.

This course is a basic introduction to combinatorics and graph theory for advanced undergraduates in computer science, mathematics, engineering and science. Topics covered include: elements of graph theory; Euler and Hamiltonian circuits; graph coloring; matching; basic counting methods; generating functions; recurrences; inclusion-exclusion; and Polya's theory of counting. This course counts as an Elective toward the CS Major. Open to juniors and seniors. Undergraduate Prerequisites: either COMPSCI 250 or MATH 455 with a grade of B or better. Modern Algebra - MATH 411 - is helpful but not required. 3 credits.

COMPSCI 576: Game Programming

Instructor(s): evangelos kalogerakis.

Game Programming introduces students to concepts of computer game development, including 2D and 3D modeling, character design, animation, game art, basic game AI, audio and video effects. The course will help students build the programming skills needed to turn ideas into games. Both runtime systems and the asset pipelines will be covered. Students will work on various game programming exercises with modern game engines and graphics APIs. This course counts as a CS Elective for the CS Major (BA or BS). Open to Computer Science juniors and seniors Undergraduate Prerequisites: COMPSCI 311 with a grade of C or better (or COMPSCI 250 with a grade of B+ or better) and received a grade of C or better in COMPSCI 220 and MATH 235. 3 credits.

COMPSCI 589: Machine Learning

Instructor(s): justin domke.

This course will introduce core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. On the theory side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, and empirical evaluation. The assignments will involve both mathematical problems and implementation tasks. Knowledge of a high-level programming language is absolutely necessary. Python is most commonly used (along with standard libraries such as numpy, scipy, and scikit-learn), but languages such as Matlab, R, Scala, Julia would also be suitable. While this course has an applied focus, it still requires appropriate mathematical background in probability and statistics, calculus, and linear algebra. The prerequisites for undergrads were previously COMPSCI 383 and MATH 235 (COMPSCI 240 provides sufficient background in probability, and MATH 131/132 provide sufficient background in calculus). Graduate students can check the descriptions for these courses to verify that they have sufficient mathematical background for 589. Strong foundations in linear algebra, calculus, probability, and statistics are essential for successfully completing this course. Graduate students from outside computer science with sufficient background are also welcome to take the course. This course counts as a CS Elective for the CS Major. Open to Sr/Jr CS majors.Undergraduate Prerequisites: a grade of C or better in MATH 545, STAT 315/515 & CS 240; OR a C or better in MATH 545 & B+ or better in CS 240; OR a C or better in STAT 315/515 & CS 240 and B+ or better in MATH 233 & 235; OR B+ or better in CS 240, MATH 233 & 235 3 credits.

COMPSCI 590X: Decarbonization and Data Science

Instructor(s): jayant taneja.

This course examines applications of Data Science in the decarbonization of energy systems. The course covers (i.) basic energy systems concepts and background with US and global examples, (ii.) an introduction to relevant methods in statistical and geospatial data analytics and machine learning, and (iii.) trends and challenges affecting decarbonization in the electricity sector and beyond, with a focus on end-uses of energy. This course incorporates a significant programming component, with assignments on electricity supply implications from fluctuating solar photovoltaic and wind generation; residential energy system planning including heat pumps, solar photovoltaic systems, and energy storage; and demand side management, including smart appliances and electric vehicles. This course counts as a CS Elective for the CS Major (BA or BS) and as an Elective for the INFORM Major. Open to junior and senior Computer Science and Informatics students. Prerequisites: Either COMPSCI 240 (or STATISTC 515) and CICS 210 (or COMPSCI 187), or INFORM 348. 3 credits.

COMPSCI 596E: Independent Study - Machine Learning Applied to Child Rescue

Instructor(s): brian levine.

Students will work collaboratively to construct production-grade software used to advance the goal of Child Rescue. This course is a group-based, guided independent study. Our goal is to build practical machine learning models to be used by professionals dedicated to rescuing children from abuse. Students will be encouraged to design and build their own diagnostic and machine learning tools, while also learning from professionals in the fields of digital forensics and law enforcement. An emphasis is placed on practicing real world professional software engineering skills, such as dealing with limiting scope, productionisationconcerns, and working in the presence of poorly defined problems. The entire student group will meet once a week to share progress via short presentations. Open to senior Computer Science majors, MS-CMPSCI majors, and CS PhD students. 3 credits

COMPSCI 602: Research Methods in Empirical Computer Science

Instructor(s): david jensen.

This course introduces concepts, practices, and tools for conducting effective research. You will learn how to read technical papers, interpret published research, assess the research frontier, select research topics, devise research questions and hypotheses, propose and plan research activities, analyze experimental results, and report those results. The course is structured around five activities: (1) Synchronous and asynchronous lectures on basic research strategies and techniques; (2) Synchronous activities that apply course concepts; (3) Reading and discussions of technical papers in computer science; (4) An individual semester-long empirical research project; and (5) Review and feedback on other student's projects. The course requires significant reading, reviewing, and writing. Students are expected to participate actively in class activities and to provide meaningful comments on the work of other students. For PhD students, this course will help accelerate your current and future research. For MS students, this course will provide a grounding in research methods that will aid your entry into research-oriented industrial positions and PhD studies. For undergraduates considering graduate studies, this course will help inform and accelerate that direction. For undergraduates, this course can be used to satisfy the 499Y requirement for Departmental and Multidisciplinary Honors students whose theses or projects have a substantial empirical component. Undergraduates must obtain approval of the Computer Science Honors Program Director prior to registering. Open to graduate Computer Science students only. 3 credits.

COMPSCI 610: Compiler Techniques

This course explores the basic problems in the translation of programming languages focusing on theory and common implementation techniques for compiling traditional block structured programming languages to produce assembly or object code for typical machines. The course involves a substantial laboratory project in which the student constructs a working compiler for a considerable subset of a realistic programming language, within a provided skeleton. The lectures are augmented by a discussion section that covers details of the programming language used to build the compiler, the operating system, the source language, and various tools. Use of computer required. Open to graduate Computer Science students only. Text: Engineering a Compiler, Cooper and Torczon. 3 credits.

COMPSCI 611: Advanced Algorithms

Instructor(s): hung le.

Principles underlying the design and analysis of efficient algorithms. Topics to be covered include: divide-and-conquer algorithms, graph algorithms, matroids and greedy algorithms, randomized algorithms, NP-completeness, approximation algorithms, linear programming. Open to graduate Computer Science students only. Prerequisites: The mathematical maturity expected of incoming Computer Science graduate students, knowledge of algorithms at the level of COMPSCI 311. 3 credits.

COMPSCI 646: Information Retrieval

Instructor(s): razieh rahimi.

The course will cover basic and advanced techniques for text-based information systems. Topics covered include retrieval models, indexing and text representation, browsing and query reformulation, data-intensive computing approaches, evaluation, and issues surrounding implementation. The course will include a substantial project such as the implementation of major elements of search engines and applications. Open to Masters and PhD Computer Science students only. 3 credits.

COMPSCI 648: Quantum Information Systems

Instructor(s): stefan krastanov.

Fundamentals of quantum information systems, including quantum computation, quantum cryptography, and quantum information theory. Topics include: quantum circuit model, qubits, unitary operators, measurement, entanglement, quantum algorithms for factoring and search, quantum key distribution, error-correction and fault-tolerance, information capacity of quantum channels, complexity of quantum computation. Open to Masters and PhD Computer Science students only. 3 credits.

COMPSCI 653: Computer Networking

Instructor(s): arun venkataramani.

The goals of this course are to teach advanced fundamental principles underlying computer network systems. The course will cover topics in the following categories: 1) routing and transport protocols, 2) resource management, 3) datacenter network design, 4) software defined networking 5) wireless networks, and 6) network security. Prerequisites: Introductory (undergraduate level) courses in computer networks (e.g., COMPSCI 453), and algorithms (e.g., COMPSCI 311). Some familiarity with probability, optimization theory, and operating systems will be helpful. Open to Masters and PhD Computer Science students only. 3 credits.

COMPSCI 655: Performance Evaluation

Instructor(s): donald towsley.

This course will provide an introduction to the tools and techniques needed to construct and analyze performance models of computer systems, distributed systems, and communication networks. The course covers three topics: i) analytical methods including discrete and continuous time Markov chain models, queues in isolation, queueing networks, and fluid queues; ii) computer/communication system measurement methodology including statistical inference and estimation of pertinent performance metrics, optimal measurement design, and bias removal; iii) applications to solving real world problems including model validation against measurements and/or simulation, case studies will be drawn from the areas of parallel and distributed systems, and networks. The goal is to teach fundamentals with a long half-life. Students are expected to have taken probability theory at at least the undergraduate level. Open to Masters and PhD Computer Science students only. 3 credits.

COMPSCI 660: Advanced Information Assurance

Instructor(s): amir houmansadr.

This course provides an in-depth examination of the fundamental principles of information assurance. While the companion course for undergraduates is focused on practical issues, the syllabus of this course is influenced strictly by the latest research. We will cover a range of topics, including authentication, integrity, confidentiality of distributed systems, network security, malware, privacy, intrusion detection, intellectual property protection, and more. Open to Masters and PhD Computer Science students only. 3 credits.

COMPSCI 661: Secure Distributed Systems

This is a class devoted to the study of securing distributed systems, with blockchain-based cryptocurrencies serving as our real platform of interest. We'll start with the fundamentals of Lamport's, Fischer's, and Douceur's results that fence-in all consensus system, and discuss Byzantine fault tolerance. We'll also look at the efficiency of the network architectures for peer-to-peer/distributed system communication and attacks on their security, such as denial of service attacks. And we'll review relevant applied cryptography such as elliptic curves. We ll discuss in detail the mechanisms of Bitcoin and Ethereum and we ll program distributed applications for Ethereum. Other topics include economics and finance. Assignments will include programming projects and reading research papers. The grade is also based on exams and participation in discussion. The course is based on a flipped classroom . Open to Masters and PhD Computer Science students and Electrical + Computer Engineering students. 3 credits.

COMPSCI 666: Theory and Practice of Cryptography

Instructor(s): adam o'neill.

This is a graduate-level introduction to cryptography, emphasizing formal definitions and proofs of security. Though the course is theoretical in nature, its viewpoint will be "theory applied to practice." We will discuss cryptographic algorithms that are used in practice and how to reason about their security. More fundamentally, we will try to understand what security "is" in a rigorous way that allows us to follow sound cryptographic principles and uncover design weaknesses. Tentatively, we will cover: blockciphers, pseudorandom functions and permutations, symmetric encryption schemes and their security, hash functions, message authentication codes and their security, authenticated encryption schemes and protocols such as SSL/TLS, public-key encryption schemes and their security, digital signature schemes and their security, and public-key infrastructures. Open to graduate Computer Science students only. 3 credits.

COMPSCI 670: Computer Vision

Instructor(s): grant van horn.

This course will explore current techniques for the analysis of visual data (primarily color images). In the first part of the course we will examine the physics and geometry of image formation, including the design of cameras and the study of color sensing in the human eye. In each case we will look at the underlying mathematical models for these phenomena. In the second part of the course we will focus on algorithms to extract useful information from images. This includes detection of reliable interest points for applications such as image alignment, stereo and instance recognition; robust representations of images for recognition; and principles for grouping and segmentation. Time permitting we will look at some additional topics at the end of the course. Course assignments will highlight several computer vision tasks and methods. For each task you will construct a basic system, then improve it through a cycle of error analysis and model redesign. There will also be a final project, which will investigate a single topic or application in greater depth. This course assumes a strong background in probability, calculus, linear algebra, and Python. Prior experience in signal/image processing is useful but not required. Open to graduate Computer Science students only. 3 credits.

COMPSCI 677: Distributed and Operating Systems

This course provides an in-depth examination of the principles of distributed systems and advanced concepts in operating systems. Covered topics include client-server programming, distributed scheduling, virtualization, cloud computing, distributed storage, security in distributed systems, distributed middleware, ubiquitous computing, and applications such as the Internet of Things, Web and peer-to-peer systems. Prerequisites: Students should be able to easily program in a high-level language such as Java, C++ or Python, have had a course on data structures, be familiar with elements of computer architecture and have had previous exposure to the operating system concepts of processes, virtual memory, and scheduling. A previous course on uniprocessor operating systems (e.g., COMPSCI 377) will be helpful but not required. Open to Online MS in Computer Science students only 3 credits.

COMPSCI 682: Neural Networks: A Modern Introduction

Instructor(s): chuang gan, subhransu maji.

This course will focus on modern, practical methods for deep learning with neural networks. The course will begin with a description of simple classifiers such as perceptrons and logistic regression classifiers, and move on to standard neural networks, convolutional neural networks, some elements of recurrent neural networks, and transformers. The emphasis will be on understanding the basics and on practical application more than on theory. Many applications will be in computer vision, but we will make an effort to cover some natural language processing (NLP) applications as well.The current plan is to use Python and associated packages such as Numpy and Pytorch. Required background includes Linear Algebra, Probability and Statistics, and Multivariate Calculus. All assignments will be in the Python programming language. Open to graduate Computer Science students only. 3 credits.

COMPSCI 687: Reinforcement Learning

Instructor(s): bruno castro da silva.

This course provides a thorough introduction and overview of reinforcement learning. Reinforcement learning algorithms repeatedly answer the question "What should be done next?", and they can learn via trial-and-error to answer these questions even when there is no supervisor telling the algorithm what the correct answer would have been. Applications of reinforcement learning span across medicine (How much insulin should be injected next? What drug should be given next?), marketing (What ad should be shown next?), robotics (How much power should be given to the motor?), game playing (What move should be made next?), environmental applications (Which countermeasure for an invasive species should be deployed next?), and dialogue systems (What type of sentence should be spoken next?), among many others. Broad topics covered in this course will include: Markov decision processes, reinforcement learning algorithms (model-based/model-free, batch/online, value function-based, actor-critics, policy gradient methods, etc.), and representations for reinforcement learning. Special topics may include ensuring the safety of reinforcement learning algorithms, hierarchical reinforcement learning, theoretical reinforcement learning, multi-agent reinforcement learning, and connections to animal learning. This course assumes a very strong mathematical background in calculus, linear algebra, and strategies for proving theorems. We will emphasize hands-on experience in class and through assignments, which will require implementing and applying many of the algorithms we discuss. Therefore, a strong background in programming is also necessary, as we will require that students implement sophisticated learning algorithms using C++ and/or Python. Finally, we assume that students have a background in machine learning (COMPSCI 589 or 689) and artificial intelligence (COMPSCI 683). Open to graduate Computer Science students only. 3 credits.

COMPSCI 688: Probabilistic Graphical Models

Instructor(s): daniel sheldon.

Probabilistic graphical models provide an intuitive language for describing the structure of joint probability distributions using graphs. They enable the compact representation and manipulation of exponentially large probability distributions, which allows them to efficiently manage the uncertainty and partial observability common in real-world problems. As a result, graphical models have become invaluable tools in a wide range of areas from computer vision and sensor networks to natural language processing and computational biology. The aim of this course is to develop the knowledge and skills necessary to effectively design, implement and apply these models to solve real problems. The course will cover (a) Bayesian and Markov networks; (b) exact and approximate inference methods; (c) estimation of the parameters and structure of graphical models; (d) broader topics in probabilistic inference for statistics and machine learning. Students entering the class should have good programming skills and knowledge of algorithms. Undergraduate-level knowledge of probability and statistics and a prior machine learning course are recommended. Open to graduate Computer Science students only. 3 credits.

COMPSCI 689: Machine Learning

Instructor(s): benjamin marlin.

Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the popular frameworks for learning, including supervised learning, reinforcement learning, and unsupervised learning. The course will provide a state-of-the-art overview of the field, emphasizing the core statistical foundations. Detailed course topics: overview of different learning frameworks such as supervised learning, reinforcement learning, and unsupervised learning; mathematical foundations of statistical estimation; maximum likelihood and maximum a posteriori (MAP) estimation; missing data and expectation maximization (EM); graphical models including mixture models, hidden-Markov models; logistic regression and generalized linear models; maximum entropy and undirected graphical models; nonparametric models including nearest neighbor methods and kernel-based methods; dimensionality reduction methods (PCA and LDA); computational learning theory and VC-dimension; reinforcement learning; state-of-the-art applications including bioinformatics, information retrieval, robotics, sensor networks and vision. Prerequisites: undergraduate level probability and statistics, linear algebra, calculus, AI; computer programming in some high level language. Open to graduate Computer Science students only 3 credits.

COMPSCI 690K: Advanced Robot Dynamics and Control

Instructor(s): donghyun kim.

This advanced course focuses on the dynamics and control of robotic systems, concepts crucial for understanding how robots move and interact with their physical surroundings. The content covered will go into greater depth than the more general course, CompSci 603 Robotics. Students will learn the kinematics and dynamics of robots with multiple degrees of freedom, as well as the analysis and control of these systems. Subjects covered include Lie group-based kinematics, Lagrangian dynamics, whole-body control, contact simulation, and actuation mechanisms. The course will utilize Google Colab and Python programming to develop simulation and analysis tools. Expect in-class exercises, weekly assignments/quizzes, a midterm examination, and a final project. Key topics to be explored are: actuators, homogeneous transformations, forward and inverse kinematics, 3D orientation representation, Newtonian dynamics, Lagrangian dynamics, whole-body control, and contact dynamics. While this course builds upon some themes introduced in CompSci 603, students are not required to take CompSci 603 before enrolling in this course. We will cover the foundational concepts necessary for the advanced study in this course. Open to graduate students in Computer Science, Electrical + Computer Engineering, and Mechanical + Industrial Engineering. 3 credits.

COMPSCI 692L: Seminar - Natural Language Processing

Instructor(s): mohit iyyer.

Weekly seminar requiring students to read an NLP paper and discuss and review it from a variety of perspectives. Some weeks will feature invited speakers instead of paper reviews. 1 credit.

COMPSCI 692P: Seminar- Hot Topics in Software Engineering Research

Instructor(s): yuriy brun, juan zhai.

This seminar covers research spanning programming languages, software engineering, security and systems. Open to graduate Computer Science students only 1 credit.

COMPSCI 692PA: Seminar - Advanced Topics on Privacy and Security for Generative Models

Instructor(s): evgeny bagdasaryan.

New capabilities of language and diffusion models enable applications that interact with users across different modalities, perform independent actions, and leverage external tools. In the seminar, we will study how these capabilities create new privacy and security challenges by analyzing recent papers in ML and S&P communities and connecting discovered problems to fundamental issues from previous decades. As part of the course there will be an opportunity to conduct a research project that goes deeper into these problems. 1 or 3 credits.

COMPSCI 692X: Seminar - Machine Learning on Biological Sequence Data

Instructor(s): anna green.

A seminar in which students will read, present, and discuss research papers on recent and advanced topics in computational biology, specifically related to machine learning models fit to biological sequence data (proteins and DNA). This semester, the seminar will primarily cover the following topics: foundation models of DNA and protein sequences (including transformer-based models), predicting the effects of biological mutations, predicting the structure of proteins (including AlphaFold), and supervised vs. unsupervised learning on sequences. Students are expected to read up to two papers per week. For one or more sessions in the semester, students are expected to make summary presentations and lead discussion of the papers. Students should have taken COMPSCI 690U Computational Biology and Bioinformatics, or have comparable background. 1 credit.

COMPSCI 698W: Practicum - CS Research Writing Practicum

Instructor(s): justin obara.

This CS research writing class uses a workshop format to focus on structure and phrasing while engaging students in a process-based approach to writing. Instruction will emphasize genre and discourse analysis and engage students in activities to strengthen audience awareness. As such, students will analyze representative examples of computer science research writing for stylistic and argumentative conventions and then integrate the awareness of these conventions and moves into their own writing. Students will produce or substantially revise a complete piece of writing. 6 weeks. 1 credit.

COMPSCI 701: Advanced Topics in Computer Science

Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science. The 3 credit option is for the second semester of a two semester sequence, 701 followed by 701Y. The 6 credit option is for a project that will be completed over two semesters with enrollment in only one semester.

COMPSCI 701Y: Advanced Topics in Computer Science (1st Semester)

Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science. Indicates the first semester of a two-semester sequence, 701Y (3 credits) followed by 701 (3 credits), with grade for both assigned at the end. 3 credits.

COMPSCI 879: Teaching Assistants as Tomorrow's Faculty

Teaching Assistants as Tomorrow's Faculty prepares Teaching Assistants (TAs) at the College of Information and Computer Sciences to fulfill their duties in an effective and pedagogically sound manner. The two credit (not repeatable) course is semester long and taken by all TAs prior to assuming assistantship. 2 credits.

COMPSCI 891M: Seminar - Theory of Computation

The theory seminar is a weekly meeting in which topics of interest in the theory of computation - broadly construed - are presented. This is sometimes new research by visitors or local people. It is sometimes work in progress, and it is sometimes recent material of others that some of us present in order to learn and share. This seminar may be taken repeatedly for credit up to six times. 1 credit.

COMPSCI H311: Honors Colloquium for Introduction to Algorithms

The design and analysis of efficient algorithms for important computational problems. Emphasis on the relationships between algorithms and data structures and on measures of algorithmic efficiency. Advanced graph algorithms, dynamic programming applications, NP-completeness and space complexity, approximation and randomized algorithms. Experimental analysis of algorithms also emphasized. Use of computer required. Prerequisite: Students must be enrolled in or have completed COMPSCI 311. 1 credit.

COMPSCI H335: Honors Colloquium for Inside the Box: How Computers Work

Honors section students are expected to meet weekly with the instructor. These meetings can be a combination of lecture, student research presentations, project progress reports, discussions, demonstrations of work, and problem solving. Students define their own program of enrichment, which will typically be either a research project or a project to develop an application of embedded systems. Students may work individually or in teams. Grading is based upon participation in the weekly meetings and the quality of the finished project. Prerequisite: Students must be enrolled in COMPSCI 335. 1 credit.

COMPSCI H446: Honors Colloquium for Search Engines

This course is an honors colloquium for COMPSCI 446. Students will explore and discuss topics from the 446 curriculum in greater detail, with an intended focus on contemporary issues related to search engines -- for example, large language models, fairness, and/or explainability. Students will also collaboratively design programming project that builds on the programming project from 446. All students will produce a final report and may implement the expanded programming project to replace part of that report. Required reading (available free via a UMass subscription): Michael D. Ekstrand, Anubrata Das, Robin Burke and Fernando Diaz (2022), Fairness in Information Access Systems , Foundations and Trends© in Information Retrieval: Vol. 16, No. 1-2, pp 1 177. DOI: 10.1561/1500000079. Additional readings from open-source and freely available material may be used. 1 credit.

INFO 101: Introduction to Informatics

Instructor(s): michelle trim.

An introduction to the main concepts of Informatics. There are several 'Big Ideas' in computing, including but not limited to abstraction, data and information, algorithms, programming, the internet, and the global impacts of computing. This class provides an introduction to those ideas and considers some of the ways that those computing principles might be used to solve real world problems. Computer-based assignments are an integral part of this course but no programming knowledge or prior programming experience is expected or required. Open to undergraduate students NOT majoring in Computer Science.. 3 credits.

INFO 150: A Mathematical Foundation for Informatics

Instructor(s): david barrington.

Mathematical techniques useful in the study of computing and information processing. The mathematical method of definition and proof. Sets, functions, and relations. Combinatorics, probability and probabilistic reasoning. Graphs and trees as models of data and of computational processes. Prerequisite: R1 math skills recommended. Open to INFORM majors. Not intended for Computer Science majors students interested in a majors-level treatment of this material should see COMPSCI 240 and 250 (or MATH 455). Open to INFORM majors. 3 credits.

INFO 203: A Networked World

Instructor(s): mohammadhassan hajiesmaili.

The course will cover the technical foundations of today s communication networks, particularly the Internet. It will also address key social, policy, economic and legal aspects of these networks, their use (and abuse), and their regulation. This course covers computer science topics, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. Not intended for Computer Science majors students interested in a CS majors-level treatment of this material should see COMPSCI 453. Open to INFORM majors. 3 credits.

INFO 248: Introduction to Data Science

Instructor(s): gordon anderson.

This course is an introduction to the concepts and skills involved with the collection, management, analysis, and presentation of data sets and the data products that result from the work of data scientists. Privacy, algorithmic bias and ethical issues are also discussed. Students will work with data from the financial, epidemiological, educational, and other domains. The course provides examples of real-world data that students work with using various software tools. This course consists of two lecture meetings and one lab meeting per week. Readings will be assigned as preparation for each class meeting. A semester project will be assigned. Students work in pairs to develop their project over the semester. The project provides students with an opportunity to work collaboratively to explore the topics in more depth in a specialized domain. A midterm and final exam will be given. Grades are determined by a combination of scores on lab activities, projects, and exam scores. Software: all software is freely available. Open to INFORM majors. Prerequisites: a grade of C or better in the following courses: COMPSCI 121 (or CICS 110, 160, or COMPSCI 119) and either PSYCH 240, OIM 240, STATISTC 240, RES-ECON 212, SOCIOL 212, or STATISTC 515. 4 credits.

INFO 324: Introduction to Clinical Health Informatics

Instructor(s): sunghoon lee.

This course aims to introduce the fundamentals of Clinical Health Informatics to prepare students as forerunners of the future of digital health care systems. More specifically, this course aims to teach students the fundamentals of and tools for quantitative analysis of clinical health data and the practical application of the tools on various health data. The detailed components of the course are as follows. Following an overview of the clinical health informatics industry, the course covers a broad range of introductory topics, including the structure of current health care systems, types of health data, the theory and practical use of quantitative analytic methodologies, and ethics related to healthcare. More specifically, this course will introduce key health informatics technologies and standards, including electronic health records, medical claims data, imaging data, free-text clinical notes, patient-reported outcomes, traditional and machine learning-based analytic algorithms, data visualization, and clinical research and experimental procedures. Note, however, that the course is not designed to introduce new types of machine learning or artificial intelligence algorithms for health-related data. This course is taught in the same classroom with students from COMPSCI 524. However, students enrolled in INFO 324 will be evaluated independently of students from COMPSCI 524. This course fulfills a concentration core requirement for the Health and Life Sciences track, and it can be used to fulfill an elective requirement for the Data Science concentration of the Informatics major. Open to INFORM majors. Prerequisite: Prerequisite: INFO 248 (or STATISTIC 315;515 or COMPSCI 240) with a grade of C or better. 4 credits.

INFO 348: Data Analytics with Python

The modern world is awash with data, and making sense of it requires specialized skills. This course will expose students to commonly used data analytics techniques. Topics include the acquisition, manipulation, and transformation of structured data, exploratory data analysis, data visualization, and predictive modeling. Students in this course will learn and use the Python programming language and tools for working with data. Analysis will be performed using real data sets. Does not count as a CS Elective (BA or BS). Satisfies one of the Data Science Concentration requirements and counts as an elective for the Health and Life Sciences Concentration for the Informatics major. Open to INFORM majors. Prerequisite: INFO 248 and CICS 160 (or INFO 190T or COMPSCI 186 or 187), both with a grade of C or better. 3 credits.

INFO 390C: Introduction to Computational Biology and Bioinformatics

This course is designed to provide Informatics students with a broad, practical introduction to the field of computational biology and bioinformatics. The course will discuss at a high level the models and algorithms used to analyze biological sequence data, as well as practical applications and data analysis. Background in biology is not assumed. The primary focus of the course will be analysis of genomic data, including sequence alignment, genome assembly, genome annotation, phylogeny construction, mutation effect prediction, population genetics, RNA-seq data analysis, and genotype-phenotype association studies. Throughout the course, we will emphasize the unique challenges to working with biological data. Through lectures and hands-on programming problem sets, students will develop the necessary skills to tackle computational challenges in biology. This course counts as a CS Elective toward the CS Major and as an Elective toward the INFORM Major. Open to juniors and seniors in Computer Science or Informatics. Prerequisites: A grade of C or better in INFO 248 or a grade of C or better in both CICS 210 and COMPSCI 240 3 credits.

Last automatic generation: 8/23/2024 at 3:15:36 PM

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NVMS CRC Learning Portal

Upcoming: mediation skills and process 2025.

This intensive, interactive online training combines theory and practice to teach the fundamental concepts and skills of facilitative mediation including interest-based negotiations, reflective listening, facilitation and structured problem-solving.

Course details

June 2025 dates subject to change, 9:00am-1:00pm est, online via zoom, continuing education credits.

22 (Virginia MCLE) -- 28.75 SHRM PDCs

Approved to complete the 20-hours basic mediation training requirements for Virginia Court Mediator Certification levels.

Cost: $1,465, topics covered.

Session 1 - Understanding Conflict

• Introductions and course overview
• Understanding conflict
• Conflict cycles
• Interest-based negotiation as a basis for mediation

Session 2 - The Mediation Process

• Meeting the parties’ needs
• Introduction to mediation
• What is mediation?
• Mediation demonstration
• Pre-mediation preparation
• Stage 1: Orientation

Session 3 - Understanding Parties, Identifying Issues and Mediator Skills

• Stage 2: Understanding parties and identifying issue
• Mediator communication skills: Summarizing, paraphrasing, asking questions, managing emotions, reframing, active listening
• Handling intense emotions

Session 4 - Stage 3: Problem Solving

• Role-play #1
• Power imbalance
• Stage 3: Problem-solving
• Ethical considerations

Session 5 - Stage 4: Agreement Writing

• Video demonstration
• Role-play #2
• Stage 4: Agreement Writing

Session 6 - Unauthorized Practice of Law

• Role-play #3
• Unauthorized Practice of Law (UPL)

Session 7 - Mediator Ethics

• Role-play #4

Program structure

Blend of self-paced and live online sessions.

Instructors

Tracey Pilkerton Cairnie (She/Her), MA, PCC is a court-certified mentor mediator, ICF certified coach, facilitator, and trainer. She specializes in relationship and group dynamics, as well as management and leadership optimization, and she mediates resolutions and provides coaching to individuals and teams. Mrs. Cairnie is an adjunct professor at The Carter School at George Mason University. She holds an MA in Conflict Analysis and Resolution. 

James Q. Pope (He/Him), MSW, JD is an attorney, mediator, and consultant in conflict management and subjects related to workplace conflict, employee relations, civil disputes, and divorce. Mr. Pope has conducted mediation training for the governments of Israel and Palestine and has conducted conflict management, mediation, and negotiation training for numerous public and private organizations. He is an adjunct professor at the Catholic University Columbus School of Law and at George Mason University School of Law.