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Applied Statistics

1
Year to Earn Your Masters
95%
CSU Online Grad Students have Plans Related to their Career
93%
Grad Students would choose CSU Online Again
$447M
Investment in research in FY21
Degree Overview Open Accordion

Mastering applied statistics goes beyond simply collecting, organizing, and analyzing data. It also involves deriving meaningful insights from that information, and communicating those insights effectively to people without statistical backgrounds. By learning to tell stories using data analysis, statisticians can have an integral voice within their organizations when the conversation turns to interpreting findings and taking action.

Earn your master's in statistics online

Prepare to meet the growing demands of today's data-dominated employment landscape, and become more equipped to make an impact in nearly any industry, in nearly any situation where people need answers. CSU's online statistics master's program helps you learn to ask the right questions, so you can accurately design and implement statistical procedures, and produce precise and meaningful results.

Gain the skills to turn data into actionable information

CSU's online master's in statistics prepares you to confidently measure, control, and communicate uncertainty. You will also gain the tools to effectively present findings to help guide organizational, scientific, and societal advances.

Hone your analytical skills and become an essential resource to meet industry needs. As a graduate student in the online statistics program, you will:

  • Develop a breadth of tools to help you design effective studies, make inferences from raw data, and translate those inferences into meaningful contexts.
  • Learn to use graphics and data visualization to communicate actionable information to those without statistical backgrounds to help organizations drive important decisions.

Choose from two areas of specialization

Students in this program can choose to specialize in either statistical science or data science. Both of these specializations share a set of common coursework and also share some of the same electives. However, students will also complete coursework unique to their chosen area of focus.

An accredited graduate degree program for practicing statisticians

CSU's Master of Applied Statistics is designed to enable students to start working as practicing statisticians in industry, government, or academia immediately after graduation. Taught by the same faculty, and featuring the same curriculum as the on-campus program, you earn the same regionally accredited degree as on-campus students. Considered a terminal degree, this program is intended for those who plan to pursue a career outside of academia, rather than pursue a Ph.D.

Data Science Specialization Open Accordion

Rapid advances in our capacity to acquire and store data have produced an urgent demand in business, industry, and government for capable data scientists who can extract useful information from large data sets. This specialization within the online M.A.S. degree incorporates data exploration and analysis techniques with a business focus to complement the statistical foundations of the program.

The field of data science primarily deals with the collection, storage, organization, analysis, interpretation, and visualization of large volumes of data. Data scientists use interdisciplinary techniques to provide answers to complex questions. The job of a data scientist may include using scientific computing techniques, machine learning, analytics, and modeling tools to assess trends, patterns, and behaviors or forecast potential outcomes. Statistical inference and machine learning are foundational to the field of data science and empower researchers to extract specific information from massive data sets.

CSU's online Master of Applied Statistics – Data Science Specialization emphasizes the practical and business-related applications of statistical analysis and data science. Be ready to start working as a data scientist in business, industry, or government immediately after graduation. During the program, you will receive a strong background in applied statistical procedures, analytics, data visualization, and statistical computing.

A minimum of 30 credits are required to complete this program.

Note: Prior to beginning courses in the fall semester, students are required to successfully complete the following two noncredit courses during the summer:

Required Courses

Note: Students are required to successfully complete three credits from the following courses:

Electives

Statistical Science Specialization Open Accordion

The ability to understand and make inferences from various types of data is an invaluable skill in many different industries. Practicing statisticians are adept at designing experiments, studies, and surveys, and efficiently analyzing the data obtained from them. In addition to drawing meaningful insights from large pools of data, statisticians can also implement various methods for predicting outcomes based on quantitative information.

The Master of Applied Statistics – Statistical Science Specialization emphasizes practical methods in statistics, focusing on real-world applications, rather than theory. Throughout the program, you will acquire a background in applied statistical procedures, data visualization, and statistical computing, gaining the skills to practice as an applied statistician in industry or government upon graduation.

A minimum of 30 credits are required to complete this program.

Note: Prior to beginning courses in the fall semester, students are required to successfully complete the following two noncredit courses during the summer:

Required Courses

Electives

Faculty Open Accordion
Kirsten Eilertson – Associate Professor and Director of MAS
  • B.A., Math, St. Olaf College
  • Ph.D., Statistics, Cornell University

Kirsten Eilertson is an applied statistician and splits her time between collaborative research, consulting, and teaching. As an applied statistician, she has worked with scientists in a variety of fields from evolutionary biology, behavioral neuroscience, biomechanics, functional genomics, and most recently quantitative epidemiology. Her research involves the application of statistical modeling to address specific challenges. Eilertson is a member of the Vaccine Impact Modelling Consortium. Her work has contributed to the development of a novel state-space model for estimating measles disease burden at the country level. The primary aim of this work is to improve understanding of the impact on public health measures.


Jay Breidt – Professor
  • Ph.D., Statistics, Colorado State University
  • M.S., Statistics, Colorado State University
  • B.A., Mathematics and English Literature, The College of Idaho

Jay Breidt has expertise in survey sampling, time series, nonparametric regression, and uncertainty quantification for complex scientific models. He has an extensive record of refereed publications and has presented over 130 invited short courses, conference talks, and academic seminars. Since 1991, his research has been supported continuously by a variety of agencies including the National Science Foundation, National Institutes of Health, Department of Homeland Security, EPA, US Forest Service, and NASA. Breidt is the Reviews Editor for the Journal of the American Statistical Association and The American Statistician. He serves on the U.S. Bureau of the Census Scientific Advisory Committee and has served on six review committees for the National Academy of Sciences. Breidt has received numerous honors, including recognition with a national prize in environmental statistics, elected membership in the International Statistical Institute, and elected fellowship in the American Statistical Association and the Institute of Mathematical Statistics.


Daniel Cooley – Professor
  • Ph.D., Applied Mathematics, University of Colorado
  • M.S., Applied Mathematics, University of Colorado
  • B.A., Mathematics, University of Colorado

Cooley’s research is primarily in extreme value analysis, which aims to characterize the tail of the distribution. Specifically, his research has focused on describing and modeling extremal dependence both in the multivariate and spatial cases. Much of his research is motivated by quantifying risk associated with extreme weather events and Cooley has collaborated with atmospheric scientists at institutions such as the National Center for Atmospheric Research and the Berkeley National Labs.


Bailey Fosdick – Assistant Professor
  • Postdoctoral Fellow, Statistical and Applied Mathematical Sciences Institute
  • Ph.D., Statistics, University of Washington
  • B.S., Mathematics, Colorado State University

Dr. Fosdick's primary research interests lie in the development of statistical methods for analyzing network data, with particular attention to applications in ecology and the social sciences. She also studies covariance models for multiway data, Bayesian statistics, and methods for survey analysis.


Jennifer Hoeting – Professor
  • Ph.D., Statistics, University of Washington
  • M.S., Statistics, University of Washington
  • B.S., Statistics and Psychology, University of Michigan

Hoeting's research is focused in the areas of Bayesian statistics, spatial statistics, and model selection and uncertainty. Much of her research is aimed at developing new statistical methods to address problems in ecology. Hoeting is the founding editor of the journal Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO). Hoeting's book, Computational Statistics (co-authored by Geof Givens), has been adopted as a textbook at top universities in the US and in over 25 other countries. Hoeting has been advisor to more than 30 Ph.D. and M.S. students. Hoeting is an elected Fellow of the American Statistical Association (ASA) and received the Distinguished Achievement Medal from the ASA’s Section on Statistics and the Environment. In 2015, she was named Professor Laureate of the College of Natural Sciences at Colorado State University.


Piotr Kokoszka – Professor

Kokoszka’s research is concerned with statistical modeling and inference. Areas of current interest include: functional data analysis, time series and extreme value theory. Kokoszka serves on several editorial boards, including Journal of the American Statistical Association, Journal of Multivariate Analysis, Scandinavian Journal of Statistics, and Journal of Time Series Analysis. He is a fellow of the Institute of Mathematical Statistics.


Mary Meyer – Professor
  • Ph.D., Statistics, University of Michigan

Meyer’s main area of research is estimation and inference in statistical models with inequality constraints. This includes non-parametric function estimation using constrained regression splines, density and hazard function estimation with shape constraints, models with order restrictions. Meyer has been published in STATISTICAL SCIENCE, the Journal of the Royal Statistical Society, and the Canadian Journal of Statistics.


Aaron Nielsen – Assistant Professor
  • Ph.D., Applied Mathematics, University of Colorado - Denver
  • M.S., Statistics, Colorado State University
  • M.S., Electrical Engineering, University of Colorado - Boulder

Nielsen currently serves as an assistant professor in CSU’s Department of Statistics, specializing in statistics education. He has instructed STAT 201, 204, 301, 315, 460, 472, and 581A4 and STAA 574, in addition to several Math and Stat courses at the University of Colorado.


Ben Shaby – Associate Professor
  • Ph.D., Statistics, Cornell University
  • B.S., Mathematical and Computational Science, Stanford University
  • A.B., English, Stanford University

Shaby develops statistical theory and methods to study extreme weather events and high-throughput biological experiments. He works with climate scientists, hydrologists, and wildfire scientists in academia and government to understand and mitigate the risks associated with rare, high-impact events. Shaby was the recipient of a National Science Foundation CAREER award in 2018 and the American Statistical Association Section on Statistics and the Environment's Early Career Investigator Award in 2016. He completed his Ph.D. at Cornell University in 2009 and held postdoctoral appointments at Duke University and UC Berkeley.


Ben Sharp – Assistant Professor



Julia Sharp – Associate Professor
  • Ph.D., Statistics, Montana State University
  • M.S., Statistics, Montana State University
  • B.S., Mathematics, University of Evansville

Dr. Sharp is an applied statistician with extensive collaborative experience across multiple domains. Her statistics research arises from collaborative projects and includes experimental design, generalized linear mixed models, and categorical data analysis.


Haonan Wang – Professor
  • B.S., Nankai University
  • Ph.D., University of North Carolina - Chapel Hill

Wang’s research includes object-oriented data analysis, functional dynamic modeling, and spatial statistics. His primary research centers on developing new mathematical and statistical methods to solve problems from various scientific research fields. Object oriented data, such as tree-structured data, random graphs, manifold data, and curve data, are frequently collected. In object-oriented data analysis, many problems were motivated by data from neuro-image, which can be represented by a set of tree-structured objects. The fundamental properties of the topological structures were studied. Wang is particularly interested in the problem of modeling tree-structured data to explain the relationship between tree-structured covariates and numerical response, and/or between numerical covariates and tree-structured response. Recently, his research interest centers on the development of novel statistical learning tools for analyzing big data collected from various networks.


Zach Weller – Assistant Professor
  • Ph.D., Statistics, Colorado State University
  • B.A., Mathematics, Concordia College (MN)

Weller works as a statistical consultant in the Graybill Statistics Laboratory. He is broadly interested in applied statistics for solving problems in and related to ecology, environmental health, energy, biology, wildlife management, animal science, and the environment. Weller is also interested in scientific communication, the intersection of science and policy, and statistics education.


Ander Wilson – Assistant Professor
  • Postdoctoral Fellowship, Biostatistics, Harvard T. H. Chan School of Public Health
  • Ph.D., Statistics, North Carolina State University
  • B.A., Mathematics, University of Vermont

Dr. Wilson's research focuses on the development of statistical methodology for the analysis of complex environmental health data with the motivation of better understanding how exposure to air pollution and environmental chemicals influences human health.


Wen Zhou – Assistant Professor
  • Ph.D. in Statistics, Iowa State University
  • Ph.D. in Applied Mathematics, Iowa State University

Dr. Zhou's primary research interests focus on high dimensional inference and statistical machine learning, with particular applications to genomics, genetics, integrative analysis of omics type data, and structural biology. He also pursue researches on graphical modeling, system biology, optimization, and game theory.

Career Opportunities Open Accordion

Statistics and data science are not only rapidly growing fields, but can also be quite lucrative career paths. According to PayScale, the median annual salary for statisticians as of 2018 is $71,804 and the average for IT data scientists is $90,797, not including additional compensation such as bonuses and profit sharing. Jobs for mathematicians and statisticians are also expected to increase by approximately 33% between 2016 and 2026, according to the US Bureau of Labor Statistics, significantly faster than the average growth rate for all occupations.

Not surprisingly, the ability to collect and effectively analyze large sets of data is a highly valued skill in nearly every major industry, including:

  • Industrial agriculture
  • Business
  • Banking and finance
  • Insurance
  • Healthcare
  • Sports and entertainment
  • Government
  • Nonprofit organizations

According to the American Statistical Association (ASA), trained analysts who can help make informed, data-driven decisions are becoming increasingly sought-after employees. Organizations of all sizes are seeking professionals with the ability to turn vast data sets into actionable information. Additionally, technological advances have allowed data processing to become more efficient and less expensive, making analysis of large, related data sets possible. These advancements, known as Big Data, involve new techniques related to the capture, curation, storage, search, sharing, transfer, analysis, and visualization of data and analysis.

Jobs in the field of applied statistics and data science include:

  • Business analyst or data analyst
  • Research scientist
  • Statistical programmer
  • Quality assurance analyst
  • Machine learning engineer
  • Statistical researcher
The Learning Experience Open Accordion

All courses are delivered online via our learning management system, Canvas. You are not required to 'attend' class at a given time, allowing you to work your studies into your existing schedule. The program can be completed in less than a year for full-time students and less than two years for part-time students. Although the online format of this degree offers flexibility, it still requires the same amount of work and time as an on-campus graduate program. The approximate time commitment per week for any 8-week course is 6 hours per credit, for a total of 12 hours for a 2-credit course. The total time may vary per student, but please plan for the minimum recommended time when taking the Subterm courses for the MAS Program.

In your courses you will:

  • View recorded campus-based lectures online
  • Download and review lecture notes
  • Complete assigned and optional readings
  • Communicate and exchange ideas with instructors and fellow students through chat rooms, threaded discussions, and email
  • Use applications, such as R or SAS, to perform statistical analysis assignments
  • Complete individual projects and collaborate on group projects
  • Study for and complete exams
  • Write course–related papers

Your interactions with faculty depend on the specific course, but generally, your instructors have online or telephone office hours and are available by email or course discussion boards.

More information about online learning »

Hear from Our Students Open Accordion

What do students have to say about our applied statistics master's degree program? Get a glimpse of their experiences, hear what they think about CSU's faculty and curriculum, and discover how online education is impacting people just like you.

Tracy Wang

See how Tracy is using what she's learned in CSU's online applied statistics program to create a more environmentally sustainable lifestyle.


Ed Fancher

See how the online applied statistics program has helped Ed improve at his job and continue to be able to solve problems that occur in his everyday work.


Why Choose CSU? Open Accordion

As a student in CSU's online applied statistics master's program, you receive the same education, learn from the same faculty, and earn the same regionally accredited degree as students on campus. Additionally, you can expect:

  • The Ability to Graduate in One Year: With a unique eight-week subterm structure that culminates into a six-week capstone consulting experience, full-time students can graduate in less than a year. Part-time students have the flexibility to complete the program in two to four years.
  • The Skills to Turn Data into Actionable Information: Utilize statistical techniques learned in our program to derive information from raw data. Through graphics and data visualization, communicate actionable information to those without statistical backgrounds to help organizations drive important decisions.
  • A Degree That Applies to a Variety of Industries: Our program focuses on the practical application of statistics, equipping you with the skills necessary to be applied across industries in the private and public sectors, including finance, retail, banking, insurance, sports, health care, gaming and entertainment, nonprofit organizations, and government.
  • Training to Meet Growing Industry Demand: Nearly every industry is in need of individuals who can analyze and make sense of large, related data sets. The Bureau of Labor Statistics estimates that employment of statisticians will grow 27% from 2012-2022, a much faster rate than the average for all occupations.
  • A Well-Rounded Education: Our core curriculum provides you with foundational coursework in linear models and methods, mathematical statistics, and probability.You will also study specialized topics in courses that cover a wide range of applied statistical models and methods in subjects such as spatial statistics, Bayesian statistics, data visualization, and time series analysis.
  • Instruction from Expert Faculty: Gain the most up-to-date information in industry trends and statistical techniques from faculty who incorporate their active research projects and industry expertise into the curriculum. Faculty research interests include environmental monitoring, survey sampling, time series analysis, spatial statistics, big data analytics, and much more.
  • A Hands-On Learning Experience: Through a six-week capstone consulting course, apply techniques you learn in the online classroom directly to the workplace. This practical experience allows for well-rounded training to solve real problems in science or any industry.

Learn more about CSU's rankings and accolades.

How to Apply Open Accordion

Application Deadline

Fall semester April 15

Start your application online and upload materials directly into the online system. You can save your progress and return any time.

Apply Now

1Review Admission Requirements

The Master of Applied Statistics (M.A.S.) requires applicants to have the following:

  • A four-year bachelor's degree from a regionally-accredited university
  • Three semesters of calculus (or at least through multiple integration)
  • A course in linear algebra
  • At least one undergraduate-level statistics course
  • Math Entrance Exam scores or GRE scores

Note that meeting the minimum department standards does not ensure admission to the program. Admission to Colorado State University graduate programs is based on a number of factors, including prior academic and professional experience and your personal statement.

The Master of Applied Statistics is a rigorous program that requires regular application of the skills learned in calculus, linear algebra, and statistics. If it has been more than 10 years since you have taken these math courses (or more than five years if these skills are not utilized on a regular basis in the workplace), please contact the department prior to applying.

2 Prepare Application Materials

Prepare the materials below and upload when you apply online.

  • Three letters of recommendation
    Three professional recommendations are required. You will provide information about your recommenders in the online application. CSU will contact them with instructions and a link to a secure form they will submit on your behalf.

    Letters should be from (1) previous or current college/university instructors, (2) previous or current, immediate professional supervisors, or (3) observers, other than supervisors, who can verify the specific impact of your professional or intellectual expertise. Letters from friends, co-workers, and/or relatives will be considered only as supplements to the required three letters.
  • Resume
  • Personal statement
    Summarize your long-term professional and educational goals and how graduate studies will contribute to both in two pages or less.
  • Math Entrance Exam scores or GRE scores
    It is highly recommended you complete the Math Entrance Exam, but GRE scores will also be accepted. The math entrance exam covers calculus and linear algebra, and applicants are provided free preparation materials by the department. To request preparation materials or to make arrangements to take the exam, please contact Karena Alons at alons@stat.colostate.edu. There is no fee for taking this exam; however, the exam must be taken at an approved university testing center which may charge a fee for service. The exam will be available between January 15th and March 15th each year.
  • Transfer credit
    If you have taken prior master's level coursework in statistics, those credits may be considered for transfer into our M.A.S. program if the grade earned was at least a B, and the course was of the appropriate type and level for our M.A.S. program. Any courses that have been taken to fulfill a requirement for another degree, even as an elective, cannot be allowed for transfer. You should send a catalog course description(s), textbook information, and a list of topics covered and course prerequisites.

3 Complete Online Application

Complete the online graduate application and pay the nonrefundable application processing fee (payable online). As soon as you have completed the required information, please submit your application. Your application will not be reviewed until it is complete and all required materials have been received.

  • Select "Applied Statistics / Data Science (M.A.S.) - Distance" or "Applied Statistics / Statistical Science (M.A.S.) - Distance" when choosing the program of study.

4Request Transcripts

Request one official transcript of all collegiate work completed from all institutions attended. Transcripts from Colorado State University are not required. Transcripts must be received directly from the originating institution to be considered official.

Electronic (preferred):
Digital Transcripts must be submitted by the originating institution using a secure service such as parchment, eScrip-Safe, the National Student Clearinghouse, or e-Quals. Transcripts received via emails are considered unofficial.

Use institution code 4075 for Colorado State University or gradadmissions@colostate.edu if the secure service requires an email address.

Mail (if necessary)
Graduate Admissions
Colorado State University – Office of Admissions
1062 Campus Delivery
Fort Collins, CO 80523-1062

Check Your Application Status

View your application status at any time to ensure your application checklist is complete or to check on updates.

Once your complete application, including supporting materials, is received, the department admission committee will review your application and notify you of their decision.

Information for International Students

Official transcript translation

Ensure official copies include a certified translation into English (only one set of transcripts is required).

Official test scores

Colorado State University requires that proficiency in English language be demonstrated either by the TOEFL, IELTS, or PTE academic tests prior to admission. Please submit one of the official scores from the list below. Tests must be taken within two years prior to admission and submitted directly from the Educational Testing Service (select institution code: 4075).

  • The minimum TOEFL score for admission without condition is 550 (paper-based), or 80 (internet-based).
  • The minimum IELTS score for admission without condition is 6.5.
  • The minimum PTE Academic score for admission without condition is 58.

All applicants who are citizens of countries in which English is not the official language, and who do not have an undergraduate degree from the U.S. must submit TOEFL, IELTS, or PTE scores.

Questions?

We love learning about your goals and answering any questions to help you on your journey.

Nicole Schmidt
Applied Statistics
Student Success Coach
Schedule Time to Talk

Program Details

Delivery
Credits
30
Tuition
$835 per credit
Degree Awarded
Master of Applied Statistics
Time Frame
The degree begins during the summer term and can be completed in 1 year

Application Deadlines

Fall semester
April 15

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