applied statistics coursework

• Certificate in Applied Data Science
• What is Cybersecurity?
• MICS Class Profile
• What Is Data Science?
• Careers in Data Science
• MIDS Class Profile
• Study Applied Statistics
• International Admissions
• Fellowships
• Student Profiles
• Alumni Profiles
• Video Library
• Apply Now External link: open_in_new

Home / Data Science / Study Applied Statistics

Study Applied Statistics 

What is applied statistics.

Applied statistics is a uniquely analytical career field. Students who study applied statistics build critical thinking and problem-solving skills in data analysis and empirical research, preparing themselves for careers in various industries — from engineering to healthcare and beyond. If you’re interested in managing, analyzing, interpreting, and drawing conclusions from data, a degree focusing on applied statistics might be for you. 

Applied statistics involves analyzing data to help define and determine business needs. While potentially bringing you closer to your career goals, an applied statistics master’s degree program teaches students how to take their advanced statistical knowledge and complex quantitative skills and turn them into must-have assets for companies — big and small. 

Statisticians, data analysts, and other data professionals use applied statistics across industries to solve practical problems in today’s data-centric world. Applied statistics has a wide variety of uses, from determining the effectiveness of new products to improving marketing and sales efficiency. Government agencies and nonprofits can even use data to help prevent disease, collect demographic information, and steer political campaigns. Companies that know how to use data effectively can set themselves apart in the market, as data-driven strategies can help increase revenue and decrease spending. 

As business leaders grow their understanding of the power of data for their companies, the need for statisticians and other professionals with advanced applied statistics skills is becoming apparent. A wide variety of opportunities may be available to graduates with degrees focusing on applied statistics. Such career paths include:

• Statistician
• Data scientist
• Data analyst

Engineering

• Quality engineer
• Statistical engineer
• Validation engineer

Finance and Accounting

• Risk analyst
• Financial analyst
• Quantitative analyst
• Actuarial director
• Financial crimes analyst
• Compliance officer

Information Technology

• Machine learning researcher
• Intelligent automation associate
• Statistical programmer
• Data architect
• Marketing analyst
• Business analyst
• Marketing research manager

Medical and Healthcare

• Biostatistician
• Clinical informatics specialist
• Health research analyst
• Statistical scientist

Science and Research & Development 

• Cognitive AI data scientist

Study Applied Statistics with a Master’s in Data Science

Both data science and applied statistics are rooted in and related to the field of statistics. Applied statistics is the foundation on which data science is built, and both make big data relevant to businesses and industries. Much of the core courses and training designed for professionals in data science, statistics, and analytics are based on similar statistical education. Fields that involve analyzing data-based findings or results often leverage trained operations research analysts , statisticians , and scientists to interpret and report information. 

Earning a master’s in data science may be a viable choice for professionals interested in learning how to mine data to make predictions and guide strategy. Data science technology has the power to differentiate and optimize the way business leaders approach decisions, processes, and the future. Data scientists employ complex computing techniques to extract information from large data sets, help companies forecast potential problems, focus on areas with growth potential, and make strategic, data-driven business decisions.

A master’s in data science allows students to recognize patterns in data and skillfully obtain, continually reorganize, and manage data. While data science is rooted in statistics, applied statistics takes a mathematical approach to analyzing and solving problems with gathered data. Applied statistics is useful for solving real-world problems and drawing conclusions for business decisions. 

Applied Statistics Course

The Master of Information and Data Science (MIDS) program delivered online by the UC Berkeley School of Information (I School) prepares data science professionals to be leaders in the field. The WASC-accredited program features a multidisciplinary curriculum that draws on insights from the social sciences, computer science, statistics, management, and law.

While all universities and programs are different, courses are designed to provide students with an understanding of how data science is used to inform decision making in organizations. The MIDS curriculum features a wide range of core and elective courses, including:

• Introduction to Data Science Programming: This course is an introduction to the Python programming language. It highlights a range of Python objects and control structures. 
• Research Design and Application for Data and Analysis: This course introduces students to the burgeoning data science landscape, with a particular focus on learning how to apply data science reasoning techniques to uncover, enrich, and answer questions facing decision makers across various industries and organizations. 
• Statistics for Data Science: This course provides students with an understanding of many different types of quantitative research methods and statistical techniques for analyzing data.
• Fundamentals of Data Engineering: This course delves into the fundamentals of data storage, retrieval, and processing systems in the context of common data analytics processing needs. 
• Applied Machine Learning: This course covers the rapidly growing machine learning field. The goal of this course is to provide a broad introduction to the key ideas in machine learning through intuition and practical examples. 
• Experiments and Causal Inference: This course introduces students to experimentation and design-based inference. Students are taught to collect data in a way that’s creative and forward looking. 
• Machine Learning at Scale: This course teaches students how to apply crucial machine learning techniques to solve problems, run evaluations and interpret results, and understand scaling up from thousands of data points to billions. 
• Data Visualization: This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. Students in this course gain the practical knowledge needed to create effective tools for both exploring and explaining data. 
• Statistical Methods for Discrete Response, Time Series, and Panel Data: This course takes a more advanced look at both classical linear and linear regression models, including techniques for studying causality, and introduces the fundamental techniques of time series modeling.

Degree Information

The multidisciplinary online Master of Information and Data Science program educates burgeoning data science leaders by preparing them to derive insights from real-world data sets, use the latest tools and analytical methods, and interpret and communicate their findings in ways that change minds and behaviors. The core curriculum focuses on research design, data cleansing, data engineering, information ethics and privacy, statistical analysis, and other essential and specialized skills data science professionals typically use. 

With flexible program paths, it’s designed for the working professional’s schedule and can be completed in 12-32 months depending on your needs. The standard part-time pathway allows students to take two courses a semester and finish the program in 20 months.

Develop the Skills Needed to Become a Data Science Leader at Top Organizations

Earn a Master of Information and Data Science online from UC Berkeley.

Request More Information

Graduate Certificate in Applied Statistics

Stand Out with a 100% Online Graduate Certificate

In a data-driven world, build the specialized skills needed to meet the growing demand with Michigan Technological University’s Graduate Certificate in Applied Statistics. This fully online statistics certificate combines fundamental methodology with applied experience navigating industry-standard statistical and data analysis software.

Gain the quantitative skill set and confidence you need to solve real-world problems and boost your career potential.

Program Overview

• 100 percent online ; designed for working professionals
• Flexible enrollment (Fall, Spring, Summer)
• Accelerated, 7-week courses
• 9 credit hours; 3 courses
• Coursework applicable toward Michigan Tech's online Master of Science in Applied Statistics
• National Graduate Service Reduced Tuition Rate available to Military, AmeriCorps and PeaceCorps personnel; Learn more

Admission Requirements

• Hold a bachelor’s degree from an accredited institution
• Previous college coursework in calculus, linear algebra, and statistics
• Completed graduate application with resume and student statements
• GRE/GMAT not required, no application fee

What You’ll Study

All students will enroll in an introductory core course to learn fundamentals of the design, conduct and analysis of statistical studies. Two electives are also required, giving students a deeper understanding of a variety of traditional and modern topics.

A sample of courses are shown here. For a full list, schedule an appointment with an advisor .

Learning Outcomes

Upon completion of the Graduate Certificate in Applied Statistics, students can expect to advance their basic understanding of:

• Foundations of statistical methods: Identify an advanced statistical method that is appropriate for a given problem, apply that method, draw appropriate conclusions and communicate your findings.
• Statistical computing and reporting: Use popular statistical software (including R and SAS) to solve realistic problems.
• Real-world data analysis: Ability to work with real data, to clean the data, deal with missing data values, generally appreciate the complexities of handling real-world data.

Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.

Pathway to Further Study

Elevate your statistics and data analysis knowledge as you build a foundation to easily continue your studies. Discover how to take your education and career potential further by earning a Master of Science in Applied Statistics online.

Learn about our online Master's in Applied Statistics

Master’s vs. Certificate – Which is right for me?

Big data, big opportunities.

The US Bureau of Labor Statistics projects the employment growth rate for statisticians will be 29% higher than the average of all other occupations between 2019 and 2029 ( source ). Our Graduate Certificate in Applied Statistics online program is designed for busy professionals and those with a background in STEM to elevate their understanding of statistics and data analysis concepts, while building confidence and experience of navigating real data.

Focusing on one course at time allows you to keep up with your career and gain knowledge that is immediately applicable. Completion of this graduate certificate may qualify you for leadership opportunities, project management responsibilities or the opportunity to work with more specialized projects.

Applied Statistics Careers by the Numbers

35 percent Employment of statisticians is projected to grow by 35 percent from 2019 to 2029, much faster than the average for all occupations.

Best Business Job of 2020

U.S. News & World Report

Fasted growing job in the U.S.

Top In Demand Skills 2020

Median annual salary, 2020

Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course.

Course will cover various topics in statistical data mining, including linear model selection and regularization, regression and smoothing splines, unsupervised learning, resampling methods, tree-based methods, and deep learning. This course will introduce modern statistical data mining techniques and their applications.

Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The SAS statistical package is an integral part of the course.

Request More Info

Online Programs

Why michigan tech online.

Copyright © 2024 | Privacy Policy | California Privacy Notice Michigan Technological University 1400 Townsend Drive, Houghton, Michigan 49931-1295

Michigan Technological University is an Equal Opportunity Educational Institution/Equal Opportunity Employer that provides equal opportunity for all, including protected veterans and individuals with disabilities.

ADA | & Title IX | Notice of Non-Discrimination

User Preferences

Content preview.

Arcu felis bibendum ut tristique et egestas quis:

• Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris
• Duis aute irure dolor in reprehenderit in voluptate
• Excepteur sint occaecat cupidatat non proident

Keyboard Shortcuts

Stat 800: introduction to applied statistics.

•   Overview
•   Materials
•   Assessment Plan
•   Prerequisites
•   Online Notes

Users of statistics -- researchers, government agencies like the Census Bureau and the Bureau of Labor Statistics, companies like the automakers and drug industry, etc. -- make extensive use of the computer in applying statistical methods to their problems. So will you! You will have plenty of practice in analyzing data from a variety of areas and should be well prepared for problem-solving involving statistics in the rest of your college courses, as well as gaining an understanding of the role of statistics in your daily life.

Course Topics

Statistics is the art and science of using sample data to make generalizations about populations. The topics covered in this course include:

• methods for collecting and summarizing data
• methods for evaluating the accuracy of sample estimates
• techniques for making statistical inferences
•   Introductory Statistics
•   Collecting Data
•   Summarizing Data
•   Sample Estimates
•   Statistical Inference
•   Applied Statistics

Course Author(s)

Dr. Linda Clark is the primary author of these course materials and has taught online courses for many semesters. Dr. Andrew Wiesner has made significant contributions to this course as well.

  This course uses Honorlock for proctored exams. For more information view O.3 What is a proctored exam? in the student orientation.

Students will use the Minitab web app. WC materials adoption app link. See the  Statistical Software page  for more details about these applications. Students wishing to use SAS, R, JUMP, etc. will not have support available through the course.

Agresti, A., Franklin, C.A., and Klingenberg, B. (2017).  Statistics: The Art and Science of Learning From Data , 5th Edition, Pearson. ISBN-13: 9780136468769

Last updated: FA23

Assessment Plan

• Attendance and Participation (20%)
• On-going Assessments (30%) - approx. 6
• Qualitative Article Critique (20%) - 1
• Final Assessment (30% ) 

PLEASE NOTE: This course may require you to take exams using certain proctoring software that uses your computer’s webcam or other technology to monitor and/or record your activity during exams. The proctoring software may be listening to you, monitoring your computer screen, viewing you and your surroundings, recording and storing any and all activity (including visual and audio recordings) during the proctoring process. By enrolling in this course, you consent to the use of the proctoring software selected by your instructor, including but not limited to any audio and/or visual monitoring which may be recorded.  Please contact your instructor with any questions . ( Read more... ) 

Prerequisites

APPLY NOW    REQUEST INFO

READY TO GET STARTED?

Take the next steps to enroll in a course. Get Started

Learn more about Penn LPS Online

Request More Information

MTHS 2200: Introduction to Applied Statistics

In this introductory statistics course students will be introduced to the fundamental techniques for using sample data to make inferences about populations. We will begin with developing the necessary probability framework and statistical intuition before moving to the specific procedures for statistical inferences from large and small samples, single and multiple linear regressions, and measuring correlation.

See Course Tuition

*Academic credit is defined by the University of Pennsylvania as a course unit (c.u.). A course unit (c.u.) is a general measure of academic work over a period of time, typically a term (semester or summer). A c.u. (or a fraction of a c.u.) represents different types of academic work across different types of academic programs and is the basic unit of progress toward a degree. One c.u. is usually converted to a four-semester-hour course.

Instructors

Elizabeth C. Scheyder

• Senior Instructional Technology Project Leader, School of Arts & Sciences Computing and Lecturer in SAS and LPS

Patrick Shields

• Mathematics Lecturer

Master's in Applied Statistics Degree Online

Immerse yourself in the study of applied statistics and build your skills as an analytical thinker and problem solver.

Bring data to life with UND's Master of Science in Applied Statistics. Set yourself apart with an education in a variety of foundational courses and statistical software packages.

Why earn an M.S. in Applied Statistics?

*Priority deadline

If you're an international student, refer to the international application process for deadlines.

At UND, you'll experience a Department of Mathematics & Statistics featuring small classes and collaborative relationships with industry partners. Working with the same data software programs that employers are looking for, you’ll build crucial skills in data visualization, predictive modeling and data analysis techniques.

Through the master's in Applied Statistics at UND, you’ll gain:

• The range of skills required to carry out programs of independent research as a data scientist
• The ability to effectively use a variety statistical software packages such as Python, SAS and R
• Analytical skills needed to work as a professional data analyst or data scientist
• Problem solving
• Data analysis
• Data science and data mining
• Machine learning

Designed to meet the needs of students interested in careers in statistical data analysis and data science with an emphasis in statistics, you'll take part in courses that cover a range of topics that are relevant to the current application of statistics in the workforce, from an applied perspective.

As an enrolled student in our master's in Applied Statistics program, you will use modern applied statistical methods for data analysis to complete foundational and methods courses with data science applications interwoven through projects.

Applied Statistics Master's at UND

Take courses in multivariate statistics and time series.

Learn to harness the power of Python, SAS and R software.

No GRE required. Plus you can complete the program 100% online and never come to campus.

Gain a competitive edge through UND's Accelerate to Industry (A2i) ™ program. This workforce readiness program provides immersive job training for graduate students and postdoctoral researchers. It is one of only 30 programs nationwide.

Study at a Carnegie Doctoral Research Institution ranked #151 by the NSF. Students are an integral part of UND research.

Enhance your professional skills at 60+ free workshops offered through the UND School of Graduate Studies. Our goal is to provide you with the workforce skills and job search strategies to succeed.

What can I do with a master's in Applied Statistics?

Median annual salary for mathematicians and statisticians

U.S. Bureau of Labor Statistics

Anticipated job growth for mathematicians and statisticians

Graduates of UND's master's in Applied Statistics program play critical roles in many industries, including business, engineering and more. Experts are in demand in a range of jobs including:

• Actuary: Play an instrumental role in helping organizations make informed financial decisions and plan for the future.
• Business Analyst: Dissect data to provide valuable insights that drive strategic business decisions.
• Data Engineer: Manage and optimize data pipelines and databases.
• Data Mining Analyst: Extract hidden patterns and insights from complex datasets to make data-driven decisions and uncover untapped opportunities.
• Data Scientist: Convert raw data into actionable insights, using advanced statistical methods and machine learning techniques.
• Data Visualization Expert: Transform data into compelling, easy-to-understand visual representations.
• Financial Quantitative Analyst: Use advanced statistical skills to develop models, assess risk, and optimize investment strategies.
• Statistical Software Designer: Contribute to the development and enhancement of statistical software tools.
• Statistician: Collect, analyze, interpret, and present data for evidence-based decision-making.

Applied Statistics Master's Courses

MATH 421. Statistical Theory I. 3 Credits.

Discrete and continuous random variables, expectation, moments, moment generating functions, properties of special distributions, introduction to hypothesis testing, sampling distributions, Central Limit Theorem, curve of regression, correlation, empirical regression by least squares, maximum likelihood estimation, Neyman-Pearson lemma, likelihood ratio test, power function, chi-square tests, change of variable, "t" and "F" tests, one and two-way ANOVA, nonparametric methods. Prerequisite: MATH 265 . F.

STAT 500. Computing for Statistics. 1 Credit.

Use and programming of computer packages for statistics. Preparation for use of software in graduate-level statistics courses. Packages covered may include R, Python, SAS, and others. Prerequisites: At least one course in statistics, and prior programming coursework or experience. Prerequisite: At least one course in statistics and computer programming coursework or experience. F,SS.

STAT 541. Linear Statistical Models. 3 Credits.

Distributions of quadratic forms, general linear hypotheses of full rank, least squares, Gauss-Markoff theorem, estimability, parametric transformations, Cochran's theorem, projection operators and conditional inverses in generalized least squares, applications to ANOVA and experimental design models. Prerequisite: MATH 422 or consent of instructor. F.

STAT 543. Design of Experiments. 3 Credits.

Design and analysis of experimental data. Includes the use of factorial designs, Latin square designs, randomized block designs, split-plot designs and others. Prerequisite: STAT 541 . S, odd years.

STAT 545. Multivariate Statistics. 3 Credits.

Theory-based statistical methods for analyzing and displaying multivariate data with applications in machine learning and data mining. Topics include inference in multivariate populations, multivariate analysis of variance, summarizing high dimensional data using principal component analysis, factor analysis, canonical correlation analysis, linear and quadratic methods of classification, cluster analysis, classification trees and random forests, multi-dimensional scaling, and support vector machines. Prerequisite: STAT 500 , STAT 541 , and MATH 442 or experience with linear algebra concepts. S, even years.

STAT 551. Statistical Graphics. 3 Credits.

Statistical graphics and visualization of one-, two-, or higher-dimensional data. Well-designed graphs and charts are essential for exploration of data, assessment of models, and presentation of results. Includes specific methods as well as general principles, such as effective use of color and motion. Prerequisite: STAT 500 . F, even years.

UND's Online Master's in Applied Statistics

best online university in the nation

best online graduate programs

Over a third of UND's student population is exclusively online; plus, more take a combination of online and on campus classes. You can feel reassured knowing you won't be alone in your online learning journey and you'll have resources and services tailored to your needs. No matter how you customize your online experience, you’ll get the same top-quality education as any other on campus student.

• Same degree:  All online programs are fully accredited by the Higher Learning Commission (HLC) . Your transcript and diploma are exactly the same as our on-campus students.
• Same classes: You’ll take courses from UND professors, start and end the semesters at the same time and take the same classes as a student on campus.
• Real interaction:  You can ask questions, get feedback and regularly connect with your professors, peers and professionals in the field.
• Your own academic advisor:  As an invaluable go-to, they’re focused on you, your personal success and your future career.
• Free online tutoring:  We're here to help you one-on-one at no cost. Plus, get access to a variety of self-help online study resources.
• Unlimited academic coaching:  Need support to achieve your academic goals or feeling stumped by a tough course? We'll help with everything from stress and time management to improving your memory to achieve higher test scores.
• Full online access:  Dig into virtual research from the Chester Fritz Library. Improve your writing skills with online help from the UND Writing Center. Get online access to career services, veteran and military services, financial services and more.
• 24/7 technical support:  UND provides free computer, email and other technical support for all online students.
• Networking opportunities: Our significant online student population means you’ll have a large pool of peers to connect with. UND has numerous online events and activities to keep you connected.

Best Online College

Our high alumni salaries and job placement rates, with affordable online tuition rates make UND a best-value university for online education. UND's breadth of online programs rivals all other nonprofit universities in the Upper Midwest making UND one of the best online schools in the region.

UND ranks among the best online colleges in the nation for:

• Affordability
• Student satisfaction (retention rate)
• Academic quality (4-year graduate rate)
• Student outcomes (20-year return on investment per Payscale.com)

Flexible Online Applied Statistics Master's Classes

With asynchronous classes, you do not attend class at a set time. If you need to balance work, family, and other commitments, this flexible format allows you to learn anywhere at any time.

Depending on your instructor, you’ll learn online through:

• Lesson modules
• Streaming video content
• Virtual libraries
• Posted lectures
• Online simulations

There will be times when you interact with your instructor and classmates through online discussion boards, polls, and chat rooms.

Your learning revolves around materials that can be accessed on your own time within a set time frame. However, this is not a self-paced course. You’ll have structure and deadlines.

What is the math prerequisite for the MAS program? ( Open this section)

To qualify for our MAS program, prospective students must have completed courses analogous to:

• Calculus II
• Calculus III
• Introduction to Linear Algebra
• Applied Statistical Methods

Is a thesis required for the MAS degree? ( Open this section)

No, a thesis is not a requirement for obtaining the MAS degree. The level of comprehension in applied statistics is assessed through comprehensive examinations that cover two general areas approved by the candidate's faculty advisor.

What criteria are used for selecting new MAS students? ( Open this section)

Admission into the MAS program is contingent on meeting the School of Graduate Studies' general admission criteria. While candidates from diverse academic backgrounds, including but not limited to engineering and computer science, are welcome, a fundamental proficiency in calculus, linear algebra and statistics is essential.

How long will the program take to complete? ( Open this section)

Our MAS program takes 2-3 years to complete, providing students with sufficient time for gaining a thorough understanding of applied statistics.

What career opportunities can I pursue with a master's in statistics? ( Open this section)

Our MAS program equips graduates with the necessary skills and knowledge for pursuing diverse career opportunities, including roles such as statistician, actuary, business analyst, data engineer, data mining analyst, data scientist, data visualization expert, financial quantitative analyst, and statistical software designer. The vital contributions that professionals with a master’s in Applied Statistics can provide for organizational processes, including decision-making, risk assessment, and data analysis, position them as valuable assets across various industries.

Leaders that Do

Students at UND take chances, seek challenges and become leaders in the community.

Check out the faculty you'll work with at UND or discover additional education opportunities.

• Department of Mathematics &Statistics
• Explore Similar Degrees
• Meet the Faculty

By clicking any link on this page you are giving your consent for us to set cookies, Privacy Information .

Applied Statistics

The ability to analyze and use such data requires a new set of skills that a Bachelor of Arts in Applied Statistics, or a Bachelor of Science in Applied Statistics offers.

Statistics is the science of learning from data. It includes planning for the collection of data, managing data, analyzing, interpreting, and drawing conclusions from data, and identifying problems, solutions and opportunities using the analysis. Massive amounts of data are being collected from digital applications and mobile devices in addition to those from the fields of engineering, environment, finance, healthcare, retail, and social sciences. The volume, variety and velocity of this data poses unique opportunities and challenges. The ability to analyze and use such data requires a new set of skills that an Applied Statistics major offers. This makes Applied Statistics one of the fastest growing career fields today. The Applied Statistics major builds critical thinking and problem solving skills in data analysis and empirical research. It prepares students for careers in business, industry, and government as well as for advanced degree programs in statistics and quantitative fields. The applied statistics major allows students to focus on their passions including genetics, healthcare, pharmaceuticals, public transportation, automotive areas, communication systems, financial markets, utilities, public policy, public health, government, manufacturing, quality control and others.

In addition to the major requirements, students must complete all  CASL Degree Requirements .

Prerequisites to the Major

Students majoring in Applied Statistics must take the following Prerequisites:

Major Requirements

24 credit hours at the 300+ level is required.

• At least 12 of the 24  upper level credit hours in Statistics (STAT) must be elected at UM-Dearborn
• Students cannot receive credit for both STAT 301 and STAT 325 . It is recommended that students complete STAT 325.
• STAT 305 and STAT 455 may not count toward the upper level electives.
• Students wishing to use graduate level courses (STAT 500+) as part of the 24 credit hours required for the major must submit a Petition to obtain the approval of the Applied Statistics Program Advisor.

Minor or Integrative Studies Concentration Requirements

A minor or concentration consists of 12 credit hours of upper-level courses (300 or above level) in Applied Statistics (STAT). Only one of STAT 301 or STAT 325   can be used to satisfy this requirement. Students with majors in mathematics, the natural sciences, or the social sciences may find the minor in Applied Statistics to be a valuable supplement to their major.

• A minimum GPA of 2.0 is required for the minor/concentration. The GPA is based on all coursework required within the minor (excluding prerequisites).
• A minimum of 9 credits must be completed at UM-Dearborn for a 12 credit minor/concentration.
• A minimum of 12 credits must be completed at UM-Dearborn for a 15 or more credit minor/concentration.
• Courses within a minor/concentration cannot be taken as Pass/Fail (P/F)
• Only 3 credit hours of independent study or internship may be used to fulfill the requirements for a 12 credit hour minor/concentration.  Only 6 credit hours of such credit may be used in a 15 or more credit hour minor/concentration.
• Minors requiring 12 credits may share one course with a major. Minors requiring 15 credits or more may share two courses with a major. This does not apply to concentrations for the Integrative Studies major.

Learning Goals

• Understand the fundamentals of probability theory
• Understand statistical and inferential reasoning
• Become proficient at statistical computing
• Understand the fundamentals of statistical modeling and understand its limitations
• Become skilled in the description, interpretation and exploratory analysis of data by graphical and other means
• Learn how to effectively communicate statistics

STAT 263     Introduction to Statistics     3 Credit Hours

Frequency distributions and descriptive measures. Populations, sampling, and statistical inference. Elementary probability and linear regression, use of statistical computer packages to analyze data. Students intending to elect this course should have taken at least one year of high school algebra. (F,W,S).

STAT 301     Biostatistics I     4 Credit Hours

This course focuses on statistical techniques and applications for biological and life sciences, as well as the relevant mathematical aspects of these statistical techniques. Topics include samples and populations, quantitative vs. categorical data, clinical vs. epidemiological studies, comparative displays and analysis, probability, Bayes' Theorem, point estimation, confidence intervals, hypothesis tests, ANOVA, and linear regression. Study design is emphasized: clinical trials in experimental settings, case-control, cohort studies in epidemiological settings, and review of some case studies from the literature. This course includes learning statistical software in labs with a biological focus. Students will be expected to write short lab reports. Students can receive credit for only one of STAT 301 and STAT 325 . (F, W, S).

Prerequisite(s): MATH 113 or MATH 115

STAT 305     Intro. to Data Science for All     3 Credit Hours

WIth increasing availability of data, companies, governments, and nonprofits alike are striving to convert this data into knowledge and insight. This course will provide students with the basic skill set needed to handle such data. The course will focus on three broad areas- inferential thinking, computational thinking, and real-word applications. We will discuss data collection, data cleaning and exploratory analysis of data so that students can connect the data to the underlying phenomena and be able to think critically about the conclusions that are drawn from the data analysis. The students will also learn how to write short programs to be able to automate the data analysis process developing an applied understanding of different analytics methods, including linear regression, logistic regression, clustering, data visualization, etc. Most of the material will be taught using real world data. (YR)

STAT 325     Applied Statistics I     4 Credit Hours

This course studies the principles and applications of statistics. Topics include descriptive statistics, random variables, probability distributions, sampling distributions, the central limit theorem, confidence intervals, hypothesis testing for mean and variance and the use of normal, chi-square, F and t distributions in statistical problems. Other topics are selected from regression and correlation, the design of experiments and analysis of variance. Students can receive credit for only one of STAT 301 and STAT 325 . (F, W).

Prerequisite(s): MATH 113 or MATH 115 or Mathematics Placement with a score of 116

STAT 327     Statistical Computing     3 Credit Hours

This course focuses on computational techniques that are crucial for statistics applications. Using the statistical packages R and SAS, the course teaches students about importing and storing data, manipulating and visualizing data, debugging and re-sampling, as well as simulation methods including bootstrap and Monte Carlo methods. (YR)

Prerequisite(s): STAT 325 or ( STAT 301 and STAT 305 )

STAT 330     Introduction to Survey Sampling     3 Credit Hours

An introduction to survey sampling techniques assuming only a limited knowledge of higher-level mathematics. Topics include: simple and stratified random sampling, estimation, systematic sampling, simple and two stage cluster sampling, and population size estimation. (AY).

Prerequisite(s): STAT 325

STAT 390     Topics in Applied Statistics     3 Credit Hours

A course designed to offer selected topics in applied statistics. The specific topic or topics will be announced together with the prerequisites when offered. Course may be repeated for credit when specific topics differ. (OC)

Restriction(s): Can enroll if Level is Undergraduate

STAT 430     Applied Regression Analysis     3 Credit Hours

Topics include single variable linear regression, multiple linear regression and polynomial regression. Model checking techniques based on analysis of residuals will be emphasized. Remedies to model inadequacies such as transformations will be covered. Basic time series analysis and forecasting using moving averages and autoregressive models with prediction errors are covered. Statistical packages will be used. Students cannot receive credit for both STAT 430 and STAT 530 .

Prerequisite(s): STAT 325 or STAT 425 or IMSE 317 or ( STAT 301 and STAT 305 )

STAT 431     Machine Learning and Computational Statistics     4 Credit Hours

Computational models trained with high dimensional data are increasingly important in industry and many academic disciplines. We will cover a wide range of topics in machine learning and statistical programming that enhance learning from data. Topics include an introduction to statistical learning, a review of simple and multiple linear regression, logistic regression, classification with linear and quadratic discriminant analysis and naïve Bayes, variable selection, shrinkage methods, dimension reduction methods, decision trees, deep learning (neural networks), and clustering methods. (W).

Prerequisite(s): STAT 325 or MATH 325 or IMSE 317 or ME 364 or ( STAT 301 and STAT 305 )

STAT 440     Design and Analysis of Expermt     3 Credit Hours

An introduction to the basic methods of designed experimentation. Fixed and random effects models together with the analysis of variance techniques will be developed. Specialized designs including randomized blocks, latin squares, nested, full and fractional factorials will be studied. A statistical computer package will be used. (W).

Prerequisite(s): STAT 325 or STAT 425 or ( STAT 301 and STAT 305 )

STAT 445     Survival Analysis     3 Credit Hours

Full Course Title: Reliability and Survival Analysis This course focuses on fundamentals of statistics with emphasis on environmental problems and their relevance in everyday life. The course topics include data visualization, parametric and non-parametric statistical inferences such as multiple linear regression, analysis of bivariate measurements, contingency table, goodness of fit tests, and comparison of several groups, and ANOVA testing. (AY)

Prerequisite(s): STAT 430

STAT 450     Multivariate Stat Analysis     3 Credit Hours

An introduction to commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. Topics include: multivariate analysis of variance, multivariate regression, principal components and factor analysis, canonical correlation, and discriminant analysis.

STAT 455     Environmental Statistics     3 Credit Hours

The primary objective of the course is to introduce statistical techniques to make data driven decisions to students majoring in the environmental and biological sciences. This course aims to nurture the importance of statistical methods to enhance the understanding of issues related to environmental sciiences. A one-semester course cannot be exhaustive in depth and width of literature but the aim of this course is to create interest and encourage students to delve more into the subject. (AY)

STAT 460     Time Series Analysis     3 Credit Hours

An introduction to time series, including trend effects and seasonality, while assuming only a limited knowledge of higher-level mathematics. Topics include: linear Gaussian processes, stationarity, autocovariance and autocorrelation; autoregressive (AR), moving average (MA) and mixed (ARMA) models for stationary processes; likelihood in a simple case such as AR(1); ARIMA processes, differencing, seasonal ARIMA as models for non-stationary processes; the role of sample autocorrelation, partial autocorrelation and correlograms in model choice; inference for model parameters; forecasting: dynamic linear models and the Kalman filter.

STAT 490     Topics in Applied Statistics     3 Credit Hours

STAT 490A     Topics in Applied Statistics     3 Credit Hours

TOPIC TITLE: Multivariate Statistical Analysis A coverage of commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. Topics include: Multivariate analysis of variance, multivariate regression, principal components and factor analysis, canonical correlation, discriminant analysis, and cluster analysis.

*An asterisk denotes that a course may be taken concurrently.

Frequency of Offering

The following abbreviations are used to denote the frequency of offering: (F) fall term; (W) winter term; (S) summer term; (F, W) fall and winter terms; (YR) once a year; (AY) alternating years; (OC) offered occasionally

Applied Statistics

With massive amounts of data collected from digital applications and mobile devices, applied statistics is one of the fastest growing career fields..

And with the knowledge gained on how to best learn from this data in the Applied Statistics program, you’ll be able to choose to work in a variety of fields — engineering, environment, finance, healthcare, government, retail, social sciences and more. 

Applied Statistics includes planning for the collection of data, managing data, analyzing, interpreting and drawing conclusions from data, and identifying problems, solutions and opportunities using the analysis. 

This major builds critical thinking and problem solving skills in data analysis and empirical research. In addition to career goals, it will prepare you for advanced degree programs in statistics and quantitative fields.

What Will I Learn?

• Understand the fundamentals of probability theory
• Understand statistical and inferential reasoning
• Become proficient at statistical computing
• Understand the fundamentals of statistical modeling and understand its limitations
• Become skilled in the description, interpretation and exploratory analysis of data by graphical and other means
• Learn how to effectively communicate statistics

Full list of Applied Statistics program goals can be found on the Office of the Provost site.

Degree Information

Visit the University Catalog to learn about  degree requirements and coursework  for the Applied Statistics major and minor.

For guidance on course selection, talk with the  Statistics Undergraduate Advisor .

Applied Statistics Major

• Outline of detailed requirements for the Major in Applied Statistics .
• All majors must complete the basic curriculum of MATH 115, 116, and 227 (alternatively biology double majors can take 113 and 114 in place of 115 and 116).
• All majors must take 18 credits of approved statistics courses and the two math classes Math 325 and Math 425.  
• All majors must take 6 credits of cognate courses (courses outside of Applied Statistics besides Math 325 and Math 425) that are selected from a specified list in subjects like Mathematics, Economics, Data Science, or Industrial and Manufacturing Systems Engineering. Courses that are not on the list can be petitioned, but should be approved in advance. For the current list of approved cognates, see the relevant section of the  detailed requirements for the Major in Applied Statistics .

Applied Statistics Minor

• A minor in the College of Arts, Sciences, and Letters consists of four upper-level courses (12 hours approved courses numbered 300 or higher) for the major in a given discipline. Only one of STAT 301 or STAT 325 can be used to satisfy this requirement. A student must also fulfill all prerequisite courses for the elected upper-level courses.
• You must have at least a 2.0 grade point average for the 12 hours of upper-division Applied Statistics courses. 
• There are restrictions on how many transfer credits, internships, or “S/E” courses can be used to fulfill the 12 credit requirement (see the  CASL Advising and Academic Success website  or speak to Math & Stat advisor for specifics).
• Minors are NOT automatically granted. You must petition for recognition of a minor upon completion of the required coursework. Petition forms are available at the CASL Advising and Academic Success office.

Making the Most of Your Major

There are opportunities to develop skills and connect with others interested in anthropology beyond the classroom. Check out the  Applied Statistics Major Map  to get a more detailed, year-by-year view of how you can learn, engage, network and transform your community and prepare for life after graduation.

Get Involved

Join a professional Applied Statistics organization. Explore UM-Dearborn student organizations on VictorsLink .

Get Real World Experience

Internships ,  research  and  study abroad  opportunities are available for applied statistics students. Talk with your professors to learn more.

Plan for Life After Graduation

Applied Statistics prepares students with the skills necessary in the modern workplace.  Career Services  offers assistance with job searching, resumes, interviews and graduate school applications.

Applied Statistics Major Map

General program information.

• Bachelor of Arts/Science  - Major/Minor/Honors
• Applied Statistics Three-Year Course Cycle
• Internship  and  research  opportunities available
• Scholarships available

The faculty in the department are incredibly helpful... they gave us many opportunities where we could expand our knowledge. — THEREN WILLIAMS, APPLIED STATISTICS

Mathematics for Finance Certificate

Math learning center, department of mathematics and statistics, office hours.

• Academic Programs
• Graduate Programs
• Online Master's Degree in Applied Statistics
• Online Master’s Degree (MS) in Applied Statistics | Purdue University

• Job Outlook
• Virtual Events

Online Master of Science in Applied Statistics: Overview

Advance your career with a master’s degree in applied statistics.

Predictive analysis is a crucial organizational resource in the modern, data-driven market. For this reason, the demand for skilled statisticians is rising. According to the U.S. Bureau of Labor Statistics, there is a projected 35% job growth for statisticians through 2029.  

Excel in this lucrative industry with a 100% online Master’s degree in Applied Statistics from Purdue. Purdue’s market-driven curriculum teaches students professional skills in probability, statistical theory, statistical methods, experimental design, and data management - preparing them for high-demand positions in statistics and data analysis.  

Why a Master’s of Applied Statistics online at Purdue?

The Department of Statistics at Purdue University is one of the largest and most computationally modernized statistics programs in the United States. Our faculty conduct cutting-edge research in both theoretical and applied statistics, and our students use the latest computational tools to solve problems relevant to today’s world. Our graduates are equipped with the training and experience they need to obtain exciting jobs in industry, government, academic and non-profit institutions.  

Our online master’s program makes a world-class statistics education accessible to working professionals. The 33 credit-hour Master of Science in Applied Statistics program can be completed in 24-30 months of consecutive, part-time enrollment. Students will learn advanced statistical skills that will make them more competitive in the job market while being able to maintain full-time careers, family obligations, and other commitments.  

The online courses are taught by the same distinguished faculty that teach on-campus at Purdue. Online students will also take an innovative statistical consulting and collaboration course – a capstone-like experience that gives students the opportunity to work on real-world problems alongside experienced statistics faculty.

Get started with a Graduate Certificate in Applied Statistics

The Graduate Certificate in Applied Statistics is a 12 credit-hour program for those looking to improve their analytical abilities while gaining a greater knowledge of statistics. If you are accepted into the Master of Applied Statistics program, the credits you earn toward this certificate may be applied to the master's program.  

Request More Information

Contact information.

Email: [email protected]

Phone: 765-496-0990

Purdue Department of Statistics, 150 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

© 2023 Purdue University | An equal access/equal opportunity university | Copyright Complaints

Trouble with this page? Disability-related accessibility issue ? Please contact the College of Science .

Outside USA: +1‑607‑330‑3200

Applied Statistics Cornell Certificate Program

Overview and courses.

In a world where data is key to business operations and decision making, it is crucial to understand how to collect the right data and provide precise and clear insights to your teams and stakeholders.

In this certificate program, you will turn data into actionable information to make better business decisions. You’ll uncover the patterns and trends hidden within various types of data so that you can effectively share these insights with stakeholders. As you determine how to represent different types of data, you’ll discover how to apply the rules of probability to analyze that data using modeling, sampling, and regression. The fundamental analytical skills you gain will not only substantiate your business choices but also enable you to predict future outcomes and opportunities for your business.

The courses in this certificate program are required to be completed in the order that they appear.

Course list

• Data Analysis and Probability

Much of business involves translating data into insights that others can grasp and act on. To do this effectively, you need to excel in the art of presenting data-based insights in a clear, accessible way.

In this course, you will build a solid foundation in basic statistical concepts. You will discover how to model unpredictable events and incorporate them into your decision-making processes. You will also interpret the importance of random events. Finally, you will develop effective graphs and learn to model a decision or process, helping you make sound decisions for your teams.

• Decision Analysis

In a business environment, the main objective of gathering information is to harness it for decision making. By integrating data with statistical and probabilistic principles, you can make decisions that have a higher likelihood of yielding desired outcomes for your team and your organization as a whole.

In this course, you will develop skills in decision analysis. You will construct a decision tree, a process that also aids in determining the extent of effort required to collect information. In processes like these, some calculations may be carried out manually, while others can be streamlined with the use of spreadsheet software. You will be guided through both types of solutions, enabling you to select the right tool for each task in your future projects.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Continuous Distributions

When aiming to construct a model, the goal is to strike a balance between accuracy, ease of use, and audience comprehension. Interestingly, numerous phenomena in both the natural and business worlds can be captured using the renowned bell curve. Harnessing the power of the bell curve will set you up for success in your business forecasting efforts.

In this course, you'll employ the normal distribution as a new tool to generate more effective forecasts. You will identify cause-and-effect relationships that are relevant to your business decisions.You will also discover how to recognize when the accuracy of this tool falls short of expectations and employ corrective adjustments accordingly. By the end of this course, you will have the necessary skills to apply normal distributions when forecasting for your business.

Businesses rely heavily on data to make informed decisions. Yet data collection comes with its own costs. To optimize this process, it is key to assess how much data you truly need to gather to make precise decisions.

In this course, you will apply the science of sampling, using data from a sample of a population to draw conclusions about the entire population. You will identify the appropriate sampling method for a particular scenario and business goal. Once you have this data, you will utilize it to predict outcome probabilities with improved accuracy. Finally, you will also explore the reverse process: understanding how much data collection is required from a set accuracy goal. Both these methodologies hold substantial value in business planning and will set you up for a more optimized approach to data collection for your business.

• Hypothesis Testing

Basic statistical tools provide a starting point, but when it comes to tackling complex business scenarios, you often need more. Making informed decisions frequently requires the ability to devise and test hypotheses.

In this course, you will practice creating and testing hypotheses. You will examine how to construct a hypothesis that is rigorous and testable and test your hypotheses using different types of statistical data. Combining this skillset with your foundations in statistics and probability, you will enhance your understanding of potential outcomes. By the end of this course, you will be equipped with the skills necessary to back up business decisions with solid mathematical justification and foster improved communication about your decisions and with your stakeholders.

• Simple Regression

By integrating several tools and concepts in applied statistics, you are now ready to make even more precise future predictions. The process of fitting these tools into a model that represents your data accurately is known as regression. Despite the term “simple regression model,” it can prove to be a formidable tool in business decision making.

In this course, you will practice working with regression models. You will discover how to construct a linear model of the relationship between two variables. You will also use a simple regression model to calculate statistics of interest for your business question or hypothesis. Finally, you will make predictions about the future behavior of a system based on the regression model. Since a multitude of situations can be accurately explained and predicted using this type of model, this skillset will set you up for success in your future business analysis efforts.

Multiple Regression

A simple regression predicts outcomes based on the correlation between two variables; in the real world, however, most decisions are far more complex, often influenced by numerous factors. Multiple regression allows you to consider these additional factors when making decisions. By building on the foundational techniques, you can create a model that more accurately reflects reality, thus enhancing the confidence in your managerial decisions.

In this course, you'll discover how to improve a predictive model by incorporating more variables. You will also use a variety of statistical tools to verify the validity of your model. Additionally, since there might be situations where your system doesn't perfectly fit as you factor in more variables, you will examine how to identify such scenarios and compensate for them when constructing your predictive model. As you introduce multiple regression analysis into your skillset, you will gain a more comprehensive approach to the decision-making process, helping you overcome challenges in your business.

How It Works

• View slide #1
• View slide #2
• View slide #3
• View slide #4
• View slide #5
• View slide #6

Faculty Author

• Certificates Authored

Dr. David C. Juran, who teaches courses in statistics for management and managing operations, is a winner of six teaching awards at Columbia Business School and at the Cornell Johnson Graduate School of Management, including the EMBA Globe Award for Teaching Excellence. Juran’s research has appeared in Management Science, Journal of Operations Management, and other journals. Juran’s academic interests are informed by extensive industrial and corporate experience at Pepperidge Farm, Spaulding Company, and Juran Institute, as well as his experience as an independent management consultant for organizations such as [x+1], Gordian Group, Johnson & Johnson, MarketBridge, MTV Networks, Opera Solutions, Veeco, and Carl Zeiss. Dr. Juran earned his Ph.D. at Cornell University’s Johnson Graduate School of Management, concentrating in the fields of operations management, operations research, and organizational behavior.

Applied Statistics

Key course takeaways.

• Determine how to represent different types of data and apply the rules of probability to analyzing that data
• Analyze data and probabilities to make informed business decisions to achieve desired outcomes
• Apply the “bell curve” model to various phenomena to make more accurate probability predictions in a scenario
• Understand the decisionmaking environment through the application of statistical sampling
• Use statistical methods to determine the truth or falsehood of a statement
• Examine the relationship between variables to better predict the future behavior of a system
• Consider multiple variables when predicting the behavior of a complex system

Download a Brochure

What You'll Earn

• Applied Statistics Certificate from Cornell’s SC Johnson College of Business
• 70 Professional Development Hours (7 CEUs)

Watch the Video

Who should enroll.

• Professionals looking to uncover insights from data
• Students who are pre-MBA or considering earning an MBA
• Analysts and researchers
• Individuals interested in moving into an analyst role
• Anyone seeking to leverage statistical or analytic skills

“eCornell’s courses in statistics made me look at data visualizations differently and start asking better questions of data scientists.”

Request information now by completing the form below..

Enter your information to get access to a virtual open house with the eCornell team to get your questions answered live.

About the Program

Statistics at Purdue University is one of the largest (students and faculty) in the United States. It is consistently rated by U.S. News and World Report as one of the top departments in the country. It offers courses in fundamental statistics and probability, and also courses that focus on statistical computation to train students as future data scientists.  Students enjoy a great deal of interaction with faculty as well as small classes. The department offers a master’s program in which a student can earn both a bachelor’s degree and a master’s degree in five years.

The statistics major consists of two options:

• Applied statistics
• Mathematical statistics (Mathematical statistics usually leads to a double major in mathematics and statistics.)

Statistics - Applied Statistics Website

Degree Requirements

120 credits required, curriculum and degree requirements.

A College of Science degree is conferred when a student successfully completes all requirements in their degree program.  Students will complete coursework or approved experiential learning activities to meet the following three degree components:

• Science Core Curriculum
• Free Electives

Students may use any of the following options to meet College of Science degree requirements:

• Purdue Coursework
• Ap, IB, and CLEP credit.  The use of AP and IB coursework varies between College of Science degree plans.
• Transfer Credit . Students should consult the Admissions Transfer Credit Resource page for all available transfer options.

College of Science degree programs vary widely in their approval and use of the proceeding options and thus students are strongly encouraged to work closely with their academic advisors and to regularly consult their MyPurduePlan to view the use of each option in their degree plan. 

Most College of Science degree programs contain free elective credits students may use to pursue courses that relate to their interests or which support their major area of study. The elective area of a degree plan may also be used to complete minors , second majors and certificates such as the Entrepreneurial Certificate. With the exception of courses on the No Count List, any Purdue course may be used to meet the free elective area of a student’s degree plan.

College of Science Core Requirements

All Students starting Purdue University Fall semester, 2007 or later are required to pursue the 2007 Science Core curriculum.

The College of Science Core Curriculum requires the completion of approved coursework and/or experiential learning opportunities in the following academic areas:

• Composition and Presentation    
• Computing     
• Culture and Diversity     
• General Education    
• Great Issues in Science     
• Laboratory Science     
• Mathematics      
• Multidisciplinary Experience    
• Statistics     
• Teambuilding and Collaboration      
• No Count List     

Earning Core Curricular Requirements through Experience

Students may meet selected core curriculum requirements through approved experiential learning opportunities. Interested students should contact their academic advisor for more information on this option and incorporating experiential learning into their four-year program of study. For more information on earning requirements through experience, please  click here .

Departmental/Program Major Courses (63-93 credits)

Required major courses (36-40 credits).

Average GPA in courses must be 2.00 excluding Calculus I, II, and III

• MA 35100 - Elementary Linear Algebra ♦ Grade of C or better required.
• STAT 35000 - Introduction To Statistics (satisfies Statistics Requirement)
• MA 36200 - Topics In Vector Calculus
• STAT 42000 - Introduction To Time Series
• MA 41600 - Probability ♦
• STAT 41600 - Probability ♦
• STAT 51600 - Basic Probability And Applications ♦
• STAT 41700 - Statistical Theory
• STAT 51700 - Statistical Inference
• STAT 51200 - Applied Regression Analysis

Calculus I Option (4-5 credits)

(satisfies Quantitative Reasoning for core)

• MA 16100 - Plane Analytic Geometry And Calculus I  ♦
• MA 16500 - Analytic Geometry And Calculus I ♦

Calculus II Option (4-5 credits)

• MA 16200 - Plane Analytic Geometry And Calculus II
• MA 16600 - Analytic Geometry And Calculus II

Calculus III Option (4-5 credits)

(satisfies Quantitative Reasoning for core) Grade of C or better required.

• MA 26100 - Multivariate Calculus
• MA 27101 - Honors Multivariate Calculus

Applied STAT Selective (6-7 credits)

(Check with advisor for additional approved courses.)

• STAT 51300 - Statistical Quality Control
• STAT 51400 - Design Of Experiments
• STAT 47201 - Actuarial Models- Life Contingencies
• STAT 47301 - Introduction To Arbitrage-Free Pricing Of Financial Derivatives
• STAT 50600 - Statistical Programming And Data Management
• STAT 52200 - Sampling And Survey Techniques

Other Departmental/Program Course Requirements (27-53 credits)

* Requirement may be met with a zero credit experiential learning option. See your advisor for more information

• ENGL 10600 - First-Year Composition (satisfies Written Communication and Information Literacy for core)
• ENGL 10800 - Accelerated First-Year Composition (satisfies Written Communication and Information Literacy for core)
• Language I Option * (Select courses COULD satisfy Human Cultures Humanities for core) - Credit Hours: 0.00 - 4.00
• Language II Option * (Select courses COULD satisfy Human Cultures Humanities for core) - Credit Hours: 0.00 - 4.00
• Language III/Culture/Diversity Option * (Select courses COULD satisfy Human Cultures Humanities for core) - Credit Hours: 0.00 - 4.00
• Technical Writing Option (Select courses COULD satisfy Oral Communication for core) - Credit Hours: 3.00 - 6.00
• Technical Presenting Option (Select courses COULD satisfy Oral Communication for core) - Credit Hours: 3.00 - 6.00
• Laboratory Science I Option (satisfies Science Selective for core) - Credit Hours: 3.00 - 4.00
• Laboratory Science II Option (satisfies Science Selective for core) - Credit Hours: 3.00 - 4.00
• General Education I Option (Select courses COULD satisfy Human Culture Behavioral/Social Science or Humanities for core) - Credit Hours: 3.00
• General Education II Option (Select courses COULD satisfy Human Culture Behavioral/Social Science or Humanities for core) - Credit Hours: 3.00
• General Education III Option (Select courses COULD satisfy Human Culture Behavioral/Social Science or Humanities for core) - Credit Hours: 3.00
• Computing Option - Credit Hours: 3.00 - 4.00
• Teambuilding and Collaboration Experience * - Credit Hours: 0.00 - 4.00
• Multidisciplinary Experience (Select courses COULD satisfies Science, Technology, and Society Selective for core) - Credit Hours: 0.00 - 3.00
• Great Issues Option - Credit Hours: 3.00

Electives (27-57 credits)

University core requirements.

• Human Cultures Humanities
• Human Cultures Behavioral/Social Science
• Information Literacy
• Science, Technology, and Society
• Written Communication
• Oral Communication
• Quantitative Reasoning

For a complete listing of course selectives, visit the Provost’s Website .

Prerequisite Information:

For current pre-requisites for courses, click here .

Program Requirements

Fall 1st year.

• ENGL 10600 - First-Year Composition
• ENGL 10800 - Accelerated First-Year Composition
• Free Elective - Credit Hours: 1.00 ( STAT 19000    Recommended)
• Language I Option - Credit Hours: 3.00 - 4.00
• Free Elective - Credit Hours: 4.00
• Calculus I Selective - Credit Hours: 4.00 - 5.00

15-18 Credits

Spring 1st year.

• Calculus II Option - Credit Hours: 4.00 - 5.00
• Computing Option ( CS 17700    Recommended) - Credit Hours: 3.00 - 4.00
• Language II Option - Credit Hours: 3.00 - 4.00
• Free Elective - Credit Hours: 3.00
• Free Elective - Credit Hours: 2.00

Fall 2nd Year

• Calculus III Option - Credit Hours: 4.00 - 5.00 ♦
• General Education I Option - Credit Hours: 3.00
• Language III/Culture/Diversity Option - Credit Hours: 3.00 - 4.00
• Free Elective - Credit Hours: 5.00

15-17 Credits

Spring 2nd year.

• MA 35100 - Elementary Linear Algebra
• STAT 35000 - Introduction To Statistics
• COM 21700 - Science Writing And Presentation Recommended for Technical Writing Option & Technical Presenting Option
• Free Elective - Credit Hours: 3.00 - 6.00

Fall 3rd Year

• STAT 41600 - Probability ♦  (or STAT 51600   )
• Laboratory Science Option I - Credit Hours: 3.00 - 4.00

15-16 Credits

Spring 3rd year.

• STAT 41700 - Statistical Theory (or STAT 51700   )
• Applied STAT Selective - Credit Hours: 3.00 - 4.00
• Laboratory Science II Option - Credit Hours: 3.00 - 4.00
• Free Elective - Credit Hours: 6.00

Fall 4th Year

• General Education II Option - Credit Hours: 3.00
• Multidisciplinary Experience - Credit Hours: 3.00
• Free Elective/Science, Technology & Society Selective Course - Credit Hours: 6.00 - 9.00

Spring 4th Year

• General Education III Option - Credit Hours: 3.00

Student should earn minimum of a C.

Students must earn a 2.0 average in MATH/STAT/IE courses required for major. Calculus I, II, and III must have a grade of C or higher.

2.0 Graduation GPA required for Bachelor of Science degree.

Foreign Language Courses

Foreign Language proficiency requirements vary by program.  For acceptable languages and proficiency levels, see your advisor:

American Sign Language, Arabic, Chinese, French, German, (ancient) Greek, Hebrew, Italian, Japanese, Latin, Portuguese, Russian, Spanish

Critical Course

The ♦ course is considered critical. A Critical Course is one that a student must be able to pass to persist and succeed in a particular major.

The student is ultimately responsible for knowing and completing all degree requirements.

The myPurduePlan powered by DegreeWorks is the knowledge source for specific requirements and completion.

• MS Applied Statistics
• OU Homepage
• The University of Oklahoma

• Minors and Certificates

Online M.S. in Applied Statistics

The OU online MS in Applied Statistics (MSAS) program is structured to provide students with a firm understanding of statistical concepts and statistical analysis across disciplinary boundaries.

With an interest in driving critical decisions, students learn to leverage technical and mathematical skills to be crucial problem solvers within their organizations, using data and technologies to drive business improvements and changes.

The 30-credit, 10 course program is 100% online and can be completed in just 18 months. Students can start in spring, summer, or fall terms.

The MSAS emphasizes data management and analytics skills with foundational courses in the following areas:

·       Scientific computing ·       Classical and Bayesian paradigms of statistics ·       Data management and analytics ·       Ethics related to applying data ·       Project-oriented capstone course focused on consulting/communication ·       Essential tools and techniques, including R, SAS, SQL, Linux Bash Shell, Stan, and JAGS

Program Benefits

1.     Gain High-Demand Valuable Skills

Gain skills highly prized by employers in an increasingly data-driven economy. Students learn scientific computing, statistical paradigms, data management/analytics, and essential tools, techniques, and software, preparing them to apply data to critical organizational decisions and solutions.

2.     Master Industry Tools

Apply learned skills in cutting-edge tools and techniques, including R, SAS, SQL, Linux Bash Shell, Stan, and JAGS.

Focus on the application of statistical and data analytics methods to solve real business challenges for your organization.

3.     Learn in a Flexible Program

Learn in a flexible and affordable online program designed for working professionals. Engage in stimulating discussions with your professors and fellow students while you gain valuable knowledge to prepare you for the challenges and opportunities of a data-driven economy.

4.     Expand Connections and Opportunities

Forge new career connections with faculty, students, and the worldwide OU alumni network. Enjoy support from the dedicated professionals in OU’s Career Services to help turn your valuable degree into rewarding opportunities.

Back to top

Program Structure

The MS in Applied Statistics (MSAS) is a 100% online, 30 credit, 10 course program that can be completed in just 18 months.

How to Apply

Online Application :   To apply to the online MSAS program, students must hold a bachelor’s degree from a regionally accredited institution. Any degree discipline is acceptable, but completion of calculus is recommended. 

Transcripts Unofficial transcripts are okay for review. Official transcript(s) are required for admission. Submit transcripts directly to [email protected] or have them mailed to the University of Oklahoma, Office of Graduate Admissions, 731 Elm Avenue, Room 318, Norman, OK 73019. Resume Include a professionally formatted document with your past education and work experience. Years of experience are not required to be admitted to this program.

Personal Statement Submit a statement addressing these questions: · What are your expectations for this program and receiving this degree? · What are your career ambitions? · What experience will you bring to the program and to your classmates?

Not Required Applicant interviews, GMAT/GRE scores, and personal references or recommendations are not required for admission to the MS in Applied Statistics program.

View core and affiliated faculty here . 

Tuition and Financing

Tuition is $818 per credit hour, totaling $24,540 for the entire program (30 credit hours), not including books/course materials. A $350 nonrefundable tuition deposit counts toward tuition and reserves your spot in the upcoming class. Financial aid is available for students who qualify. For more information, contact OU Online  Financial Aid at (405) 325-2929 or [email protected], or visit us at ou.edu/online/finaid .

To learn more about the online M.S. in Applied Statistics and how to apply please fill out the Contact Form and we will be in contact with you to discuss the program further.

Your browser does not support iframes.

• Accessibility
• Sustainability
• OU Job Search
• Accreditation
• Legal Notices
• Resources & Offices
• OU Report It!

Orange Alert

M.s. in applied statistics.

Created in 1989, the Applied Statistics Program at Syracuse University is an interdisciplinary program within the College of Arts and Sciences that includes faculty from computer and information science, management, mathematics, psychology, and the social sciences, among others. The program offers an undergraduate concentration in applied statistics, sponsors a speakers series that hosts visiting statisticians and scientists, and offers a master’s degree in applied statistics.

Master of Sciences Degree in Applied Statistics :

The Master of Science program in Applied Statistics is a professional degree program administrated by the Interdisciplinary Statistics Program based in the College of Arts & Sciences. The program prepares students to apply cutting-edge statistical methodologies and theory in making meaningful inferences on the measurements obtained from government, health science and services, industry, and management.

Applicants should apply for admission to the Applied Statistics Master’s degree program by March 15 .

Why do you come to Syracuse University for this degree?

Since our program includes professors from computer and information science, education, engineering, management, mathematics, psychology, and the social sciences, among others, the program is interdisciplinary in nature and it is distinguished from other graduate programs in statistics by its emphasis on applications. By learning a variety of statistical software and through proper training in statistical consulting, our graduates will be able to analyze real-world data correctly and efficiently.

Where are the career opportunities?

Applied statisticians are highly sought in diverse fields like government agencies, pharmaceutical companies, consulting firms and financial companies. Two recent articles revealed the fact that applied statisticians are in great demand:

• April 8, 2010 Wall Street Journal : New Hiring Formula Values Math Pros: Region's Employers Seek Statistical Experts Over Computer-Science Generalists
• August 5, 2009 New York Times : For Today’s Graduate, Just One Word: Statistics.

Vast career opportunities can be found at:

• Sloan Career Cornerstone Center: Career planning resources in statistics
• American Statistical Association: Career in Statistics

Who will you become after?

Upon completion of program, students will be able to:

• Implement trending statistical methods to solve problems;
• Analyze large data set using various statistical packages;
• Participate and work in problem-solving teams;
• Present results verbally and in writing.

What background is needed for the program?

The program is intended for quantitatively oriented students with bachelors' degrees in agriculture, biological sciences, business and management, computer science, engineering, mathematics, physical or social sciences or a related field. This program is also suitable for professionals who handle data in their current positions, and who are mostly interested in the practical side of statistics. All applicants are expected to have a basic foundation in statistical training that includes one course in introductory statistics, one course in regression analysis, and four courses in applications areas. Graduate Record Examination scores, or their equivalent, and performance in a student’s undergraduate degree program will be carefully evaluated.

Masters of Science Requirements

Applicants not currently enrolled in any program at Syracuse should use the online application by March 15.

The master’s degree in applied statistics requires completion of 33 graduate credits. Each candidate must submit a coherent program of 11 courses beyond the bachelor’s degree, subject to the following requirements.

Within the first semester after admission to the degree program, the students will plan their course of study in consultation with their advisors and submit it for approval to the Statistics Program Director. In order to graduate, a student must earn (1) at least a 3.0 grade in each of the four core courses, (2) a GPA of 3.0 or better in this program of study leading to the M.S. in applied statistics, and (3) no more than two Cs in his/her statistics program coursework.

The absence of either a comprehensive final examination or a master’s thesis is compensated for by an additional 3 credits of coursework, represented by STT 690, whose objective is to apply knowledge of statistics to some real world problem.

Core Courses (12 credits)

• MAT 521: Introduction to Probability and Statistics
• MAT 525: Mathematical Statistics (or MAT 652)
• APM 630 [1]
• STT 750 / MAT 750: Statistical Consulting [2]

[1] Courses with an APM prefix are offered by the SUNY College of Environmental Science and Forestry

[2] For those students who do not include STT 750/MAT 750 in their programs of study, they should take STT 690: Independent Study (to be taken toward the end of the program of study; its objective is to apply knowledge of statistics to some real world problem).

View the course catalog link in the Course Catalog link box to the right .

Previous and current students:

• Jesse Lecy received our Master’s degree in Applied Statistics in 2009 and a Ph. D. degree in Social Sciences in 2010. He started a job in the Andrew Young School of Policy and Management at Georgia State University in Fall 2010.

Jesse said: “My undergraduate work was in critical studies so I did not have strong quantitative training, although I increasingly found myself confronted with literature in economics and policy that required a high degree of mathematical literacy. The Masters in Applied Statistics gave me a way to bring rigor to by studies and integrate my research interests in nonprofits and economic development. In many ways, statistics is the lingua franca of modern social sciences - if you understand statistics you can access research across a broad array of disciplines that is otherwise indecipherable. This is not to preference quantitative research over qualitative, but good qualitative research makes the subject accessible without specialized training whereas quantitative research becomes incomprehensible without a strong background in the subject. The degree has enhanced my research and has no doubt strengthened my profile as a job candidate. I believe that the masters in statistics set me apart from other similar candidates.”

• Ying Lin received her Master’s degree in Applied Statistics and Ph. D. in Electrical Engineering from Syracuse University in 2006 and 2007, respectively. She was an Assistant Professor in the Department of Electrical and Computer Engineering at State University of New York at New Paltz from 2007 to 2009. She joined the faculty of Engineering Technology at Western Washington University in 2009 and she is an Assistant Professor there.

Ying said “The Applied Statistics course work at Syracuse University has been very beneficial to both my research and teaching. I currently teach Engineering Statistics and Wireless Communication Systems courses. The foundation built through my graduate study of Applied Statistics strengthens my understanding and delivering of the course materials. Statistics and probability theory are indispensible to my research work as well. My interest has been using classical and advanced statistical signal processing techniques to solve engineering problems in wireless sensor network and wireless communication systems.” She also said: “In summary, my education experience in Applied Statistics greatly facilitates my career.”

• Mark Prince received the Master’s degree in Applied Statistics and a Ph. D. degree in Psychology in March 2014. He is currently a Postdoctoral Associate Research Institute on Addictions.

After receiving a BS degree in Psychology from Columbia University, he moved to San Diego, CA to work with professors at UC San Diego and San Diego State University in addictive behaviors. The completion of a Master's thesis at San Diego State gave him further insight into the necessity of advanced statistical training in his academic and career goals. Mark came to Syracuse University in 2007 and finds that the pairing of clinical psychology and applied statistics to be extremely beneficial for his goals. Mark said: "I believe that without adequate training in both fields my research would be lacking in depth and impact. Further, as many psychological constructs are difficult to measure and change often across time advanced statistical training is imperative to accurately capture and describe the phenomena of interest. Finally, I believe that my statistical training will set me apart from my peers in the Clinical Psychology program and help me to secure a job in my desired field of study."

Pinyuen Chen

[email protected]

315.443.1577

Program Faculty

Eddie Bevilacqua

[email protected]

315.470.6697

[email protected]

315.443.3679

Vernon L. Greene

[email protected]

315.443.9059

Jennifer Karas Montez

[email protected]

315.443.9064

H. Hyune-Ju Kim

[email protected]

315.443.1494

[email protected]

315.443.8424

Jianxuan Liu

[email protected]

315.443.1554

[email protected]

315.443.3716

Jan Ondrich

[email protected]

315.443.3115

Arthur Owora

[email protected]

315.443.5555

Stephen V. Stehman

[email protected]

315.470.6692

[email protected]

315.443.3526

[email protected]

315.443.4763

Janet Wilmoth

[email protected]

315.443.5053

[email protected]

315.443.3751

Lianjun Zhang

[email protected]

315.470.6558

Academic Resources

• Academic Calendar
• Academic Catalog
• Academic Success
• BlueM@il (Email)
• Campus Connect
• DePaul Central
• Desire2Learn (D2L)

Campus Resources

• Campus Security
• Campus Maps

University Resources

• Technology Help Desk

Information For

• Alumni & Friends
• Current Students
• Faculty & Staff

Mathematical Sciences

• Undergraduate
• Combined Degree Programs
• Applied Mathematics (MS)
• Degree Requirements

Admission Requirements

• Concentration Requirements
• Student Handbook
• Mathematics Education (MA)
• Mathematics for Teaching (MS)
• Pure Mathematics (MS)
• Applied Statistics Certificate
• Mathematics for Community College Teaching Certificate
• Certificate Programs
• Class Search
• Student Resources
• Admission & Aid
• Request Info
• Apply Online
• Visit Campus

College of Science and Health > Academics > Mathematical Sciences > Graduate Programs > Applied Statistics (MS)

Applied Statistics (MS)

Use data to unlock insights.

From health care to academics to sports, the ability to research, analyze and interpret data is invaluable across a range of industries. DePaul’s MS in Applied Statistics not only emphasizes data-analytics skills, but explores how to tackle problems of statistical design, analysis and control. Learn about computation and technologies that play an important role in problem-solving across the job market, including research equipment and software used for data analysis, numerical analysis, simulations and mathematical modeling.

Choose from three areas of concentration:

• Data science
• General applied statistics

Courses in the Applied Statistics program are offered weekday evenings at the Lincoln Park Campus. The program can be completed in two years by taking two classes per quarter.

For international students: this is a STEM-designated program , which can qualify you to extend your post-graduation stay in the United States.

Analysts are in demand across the city

As the third-largest city in the nation, Chicago is home to countless opportunities to put statistical analysis to work: eight major-league sports teams, more than 30 Fortune 500 companies, numerous colleges and universities, non-profits, insurance companies, and hospital and health care systems.

Application Deadlines

Applications are accepted on a rolling basis.

Submit an online application, official transcripts and a personal statement.

Required Courses

You’ll take seven core and five concentration courses.

of Applied Statistics graduates were employed, continuing their education or pursuing other goals within six months of graduation.

The reported median salary of Applied Statistics graduates was $76,420 upon graduation.

Faculty who solve real-world problems

Study with Associate Professor Desale Habtzghi, director of the College of Science and Health’s Statistical Consulting Center. Through his work in the center, Professor Habtzghi oversees faculty and students as they provide expert statistical assistance to clients like government agencies, university researchers and nonprofits.

“The Biostatistics concentration of the program helped me prepare for a job in the market. Survival analysis and experimental design are widely used, especially in big tech companies. A/B testing requires deep knowledge in experimental design; survival analysis is useful to predict user churn for a company and credit default models for banks.”

Junhua (MS ’17)

Data scientist, charles schwab, alumni network.

Alumni of the MS in Applied Statistics program have gone on to careers with top companies and governmental organizations such as ACNielsen, Discover Card, the Internal Revenue Service, Kraft and PepsiCo.

Scholarships

Merit-based tuition waivers and graduate assistantships are available. Learn more about the financial aid and scholarship opportunities available to you.

Contact Information

For more information about applying, contact The Office of Graduate Admission for DePaul’s College of Science and Health at (773) 325-7315 or [email protected] .

Take the next step

We’ll send you information about the degree, admission requirements and upcoming info sessions. Let’s get started.

• OU Homepage
• OU Social Media
• The University of Oklahoma

Applied Statistics, Graduate Certificate

Minimum Total Hours: 15

Program Code: G215 (G216 Online)

Certificate Requirements

• Students must have a ≥ 3.0 GPA for courses applied to the graduate certificate to earn the graduate certificate.
• Courses for which a grade of less than C is earned will not apply toward the certificate.
• at least half of all credit hours must be at or above the 5000-level.
• No more than 6 credits hours may be taken from courses offered by the Gallogly College of Engineering (i.e., C S and/or DSA courses).

The certificate courses require pre-requisites in Linear Algebra and Calculus, Coding, and basic Statistics.  

Lists of courses approved for each of these categories are maintained by the Data Scholarship Program.

A graduate  certificate  is not a graduate  degree . A graduate degree represents a program of independent inquiry beyond the depth of coursework alone, while a graduate certificate represents a set of courses only.

• All courses must be taken at OU. No transfer credit will apply.
• No course substitutions are permitted for graduate certificates.
• Coursework applied to a graduate certificate cannot be more than five years old as of the semester the graduate certificate is awarded.
• Students must earn a grade point average of 3.00 or higher on all coursework applied to the graduate certificate.

Print Options

Send Page to Printer

Print this page.

Download Page (PDF)

The PDF will include all information unique to this page.

Catalog PDF

A PDF of the entire catalog.

Augusta National course preview: These stats define one of golf's most prestigious courses

The 2024 Masters is nearly upon us. We are officially less than a week away from one of golf's most prestigious tournaments on one of golf's most celebrated courses. Augusta National Golf Club has hosted The Masters every year since 1934. The course has witnessed many magical moments, historic victories, and heartbreaking defeats in that time. And although the course has changed drastically in the years since, it has never failed to dazzle spectators, serving as the apple of many a fan's eye come springtime.

However, the course isn't just a beauty to behold, it's also an incredibly difficult course. Of course, any club as historic as Augusta National is bound to have a few insane stories to go with it. That said, Augusta has some of the craziest tales and statistics of all.

Here are some must-know facts about the course heading into the 2024 Masters Tournament.

The Masters 2024: Tiger Woods' ankle has 'zero mobility,' Notah Begay says

What is Augusta's slope rating?

A slope rating is a score given to a course to determine its difficulty. The higher the rating, the more difficult the course. Augusta National does not have an official slope rating, but panelists have labeled it anywhere between 135 and 144. For context, the hardest courses have a slope rating of 155.

How many yards is Augusta National?

Augusta National is a grand total of 7,545 yards . The longest individual hole though is the second, measuring out at 575 yards. The shortest hole is the 12th, measuring at a measly 155 yards.

What is the course's most difficult hole?

The 11th hole is widely-considered the most difficult at Augusta. A 505-yard, Par 4, the 11th hole saw players average 4.31 strokes between 1942 and 2016.

How many hole-in-ones have been hit in Masters history?

There have been 34 hole-in-ones in Masters history. The first came in 1934 from Ross Somerville, who smoked an ace from 145 yards out on the 16th hole. Shockingly, Somerville was an amateur at the time he made that hole-in-one. The most recent hole-in-one came in 2022 from Stewart Cink on the same hole, using an 8-iron to hit the cup 166 yards away.

Who has the most Masters wins in history?

Jack Nicklaus holds the record for most green jackets won with six. Woods is sitting at five currently, and the only other golfer with at least four is Arnold Palmer.

How to watch The Masters 2024:

• TV:  ESPN, CBS, CBS Sports Network
• Stream: Paramount+,   Fubo , ESPN+

Coverage for The Masters will begin at 3 p.m. ET on Thursday, April 11.

Watch The Masters: Stream the 2024 Masters with a Fubo subscription

The Masters 2024: An early look at the top 10 betting favorites for the 2024 Masters