Statistical Data Science Major

The new Bachelor of Science (BS) in Statistical Data Science will give you a strong foundation in statistics, programming, and mathematics, as well as the flexibility to tailor your degree to your interests.

Majors take 36 credits of coursework, where they learn core skills and how to apply those skills to a domain area of their choice. They also take advantage of research and internship opportunities.

Program Overview

Statistical data science majors are trained in basic and advanced data analysis ranging from introductory statistics and calculus to statistical machine learning and linear algebra. Students learn how to use data ethically; the importance and effectiveness of visualization in communicating results; and programming skills in both R and Python. They also dive deep into an area of interest that forms the basis of their culminating capstone project.

To complete the BS in Statistical Data Science, students are required to only take one sequence of lab courses, along with an additional science course that does not need to be a lab course. This differs from most BS degrees in the College of Liberal Arts and Sciences, which require two sequences of lab courses along with an additional lab course.

Graduates of the major can expect to find work in almost all occupational realms. Alumni are prepared to serve in a number of roles in which they will operate and design analytical systems, prepare data, coordinate analysis, visualize output, and support data-driven decision making.

Major Requirements

Admitted students are required to take 36 major credits that include courses in five core areas; a nine-credit domain sequence; an introduction to data science course; and a capstone course. Students can meet the Writing in the Major requirement in a statistics W course or a capstone W course. Majors must maintain a 3.2 cumulative GPA.

The five core areas provide you with the foundation necessary to work as a data scientist in any field or professional area. They include:

  • Computer programming and data management.
  • Basic data analysis.
  • Advanced analysis.
  • Data visualization.
  • Data ethics.

A domain sequence is a sequence of courses designed to help you apply core data science skills to a professional area that interests you most. The statistical data science major domain sequences include:

  • Advanced Statistics.
  • American Political Representation.
  • Biological Data Science.
  • Financial Analysis.
  • Marine Science.
  • Population Dynamics.

 

Download the Statistical Data Science Plan of Study.

Applying to the Major

Students interested in the BS in Statistical Data Science must apply for admission to the program. Applicants must meet the prerequisite course requirements below.

Spring 2025 Deadline: Friday, Sept. 20, 2024
Fall 2025 Deadline: Friday, Feb. 14, 2025

The application for the major is open year-round. We will notify students of their acceptance to the major prior to course registration.

Courses

Prerequisite Course Requirements

In order to apply to the BS in Statistical Data Science, students must have:

  • A GPA of 3.2 or higher in the following classes: MATH 1132Q, STAT 1000Q/1100Q*, and an introductory programming course (CSE 1010, CSE 1729, or STAT 2255).
  • Completed at least 24 credits, 15 of which must be at UConn, with a cumulative GPA of 3.2 or higher.

Students not yet eligible to apply may declare a pre-statistical data science major. Pre-majors must formally apply to the BS in Statistical Data Science when they are eligible. They will be assigned an advisor in the meantime who will help them navigate course selections to work toward the statistical data science major.

*For students who are ineligible to take STAT 1000Q/1100Q because they have already taken a 2000+ level STAT course, STAT 2215Q can be taken (with permission number) as a substitute for STAT 1000Q/1100Q after having successfully completed STAT 3025Q or STAT 3445.

Major Course Requirements

Students admitted to the statistical data science major are required to take a total of 36 credits consisting of:

  • One or more courses in each of the Core Area Requirements (full list below).
  • A nine-credit domain sequence (full list below).
  • STAT 3255. Introduction to Data Science.
  • STAT 4915. Data Science in Action (capstone) and STAT 4916W (information literacy competency and writing in the major requirement).

Note: Students completing a Biological Data Science domain may take any of the following to meet the capstone and W requirement: (i) STAT 4915 / STAT4916W, (ii) EEB 4896W, or (iii) MCB 4897W. Credits in EEB 4896W cannot simultaneously count toward both an Honors thesis in EEB and a Data Science capstone.

Core Area Requirements

  • Programming and Data Management: STAT 2255* or ECON 3322 (one course, 3 credits).
  • Basic Data Analysis: STAT 3025Q or STAT 3375Q** or MATH 3160; and STAT 3215Q (two courses, 6 credits).
  • Data Ethics: PHIL 3202 (one course, 3 credits).
  • Data Visualization: STAT 3675Q** or GEOG 3510 or EEB 4100*** (one course, 3-4 credits).
  • Advanced Analysis: MATH 2210Q and STAT 4255 (two courses, 6 credits).

 

Please note:

  • *Students who have already taken CSE 2050 or CSE 2500, and hence are ineligible to take STAT 2255, should contact the statistics staff advisor to discuss course options.
  • **Students completing a statistics domain must take STAT 3375Q and STAT 3675Q to meet these requirements.
  • ***EEB 4100 is recommended for students completing the Biological Data Science domain.

Domain Sequences and Requirements

Students must take at least three courses (9 credits) from one of the following domain areas. After completing the skill and domain area training, students will conduct a final research project which applies all the core data science skills to a practical problem in or related to their domain area.

Advanced Statistics
STAT 3445 and two of the following: STAT 3515Q, STAT 4625, STAT 4825, STAT 4845, STAT 4190.

Please note: At least and no more than three credits of STAT 4190 may count toward the major and must be pre-approved by the Department of Statistics for adequate data science content.

American Political Representation
Three of the following: POLS 2607, POLS 3608W, POLS 3612, POLS 3617, POLS 3618, POLS 3625.

Biological Data Science
Three of the following: EEB 3899‡, EEB 5050, EEB 5300, EEB 5348, EEB 5349, MCB 3421, MCB 3637, MCB 4008, MCB 4009, MCB 4014, MCB 5430, MCB 5472, MCB 5631, MCB 4896‡ .

Please note:

  • Students can choose any three courses‡ from the list above based on availability, however, interested students might consider choosing subsets of courses from the list above that align with established sub-areas:
    • Genome sequencing and analysis: EEB 5300, MCB 3637, MCB 5430
    • Phylogenetics and evolution: EEB 5348, EEB 5349, MCB 3421, MCB 5472
    • Ecological analyses: EEB 5050, EEB 5348, MCB 5631
    • Molecular structure and function: MCB 4008, MCB 4009, MCB 4014
  • ‡ Only 3 credits of either EEB 3899 or MCB 4896 can count toward the major, and these credits cannot simultaneously count towards another major or degree.

Financial Analysis
Three of the following: ECON 3313, ECON 3315, ECON 3413, ECON 4323.

Marine Science
Three of the following: MARN 3001, MARN 3002, MARN 3014, MARN 4001, MARN 4210Q.

Population Dynamics
Three of the following: SOCI 2110(W), SOCI 2651(W), SOC 2660(W), SOCI 2820(W), SOCI 2901(W), SOCI 3971(W).

Domain Descriptions

Advanced Statistics

Faculty Advisors: Elizabeth Schifano, Department of Statistics and Haim Bar, Department of Statistics

The Advanced Statistics domain provides students with more advanced statistical and data scientific tools applicable to a wide range of disciplines.  Students in Advanced Statistics take one additional methodological course to more fully appreciate data-driven decision making in the context of statistical inference, whereas the remaining two courses have more applied emphasis in terms of experimental design, biostatistics, modeling of dependent (time-series and/or spatial) data, and/or internship experience relating to data science.  Graduates from Advanced Statistics will be well-prepared to tackle a diverse range of a data types and analytic problems in the realms of business and marketing, sports analytics, medicine and public health, government, and beyond.

American Political Representation
Faculty Advisor: Jason Byers, Department of Political Science

Political representation is at the core of democratic governance. It includes the multifaceted interests, incentives, opinions, and relationships among the public, politicians, and campaigns central to elections and policies. In the United States, it is also structured by the layered and complicated institutions of the federal, state, and local governments.   

The study of American political representation necessitates extensive theoretical and contextual skills. Contemporary American politics students also need empirical training. Understanding hypothesis development, research design, data collection, coding, analysis, and presentation is essential to understanding and contributing to knowledge about American politics. This domain within the Applied Data Analysis Major provides the insights and skills necessary for students to address some of the most important questions confronting the United States.

Biological Data Science

Faculty Advisors: Jonathan Klassen, Department of Molecular and Cell Biology and Yaowu Yuan, Department of Ecology and Evolutionary Biology

Recent technological advances have made large datasets widespread throughout the biological sciences. Although the types of input data differ, their analysis shares a common logic based on the scientific method and frequently uses an overlapping suite of analytic and visualization tools. This is reflected by the many computational courses available in the Biological Data Science Domain, which include course subsets centered around a particular data and analysis types that can be readily intermixed and combined with independent research projects. Graduates from the Biological Data Science will be well-prepared to analyze similar datasets while supporting research in the biotechnology and pharmaceutical industries, as well as in the academic, government, and not-for-profit sectors.

Financial Analysis
Faculty Advisor: Min Seong Kim, Department of Economics

The Financial Analysis Domain provides students with essential skills and knowledge in financial economics and relevant quantitative analysis methods. Students are required to complete three out of the following four courses:

ECON 3313 Elementary Economic Forecasting: This course focuses on economic forecasting for macroeconomics and financial economics. Students learn econometric analysis techniques for time-series data to forecast and predict economic trends and financial market behaviors.

ECON 3315 Financial Econometrics: This course is an Introduction to the mathematical foundations of finance. Topics covered include theoretical reasoning, modeling, useful simplifying approximations, and computing. Students also learn to write basic programs in R.

ECON 3413 Financial Economics: This course explores basic principles used in investment decisions and their applications to pricing financial assets and portfolio management. Students learns asset pricing models including the Capital Asset Pricing Model and Arbitrage Pricing Theory. The courses also covers fixed-income securities, options and futures.

ECON 4323 Convex Optimization with Python: This course introduces methods of convex optimization, including linear, quadratic, and general constrained and unconstrained problems. Students learn how to solve optimization problems in economics and finance applications using Python.

 

By completing the Financial Analysis Domain, students develop a solid foundation in financial theory, quantitative methods, and data analysis techniques essential for careers in finance, investment banking, and financial consulting.

Marine Science
Faculty Advisor: Heidi Dierssen, Marine Sciences

(Coming Soon)

Population Dynamics
Faculty Advisor: Jeremy Pais, Department of Sociology

Population Dynamics examines society through demographic processes. The curriculum provides an overview of the theory, data, and methods used by demographers and other population scientists—like sociologists, public health researchers, labor economists, environmentalists, epidemiologists, and social geographers—to understand how population change through fertility, mortality, and migration impacts our societies. Knowledge of ongoing population dynamics is critical to promoting healthy social institutions that shape everyday living, from doctor’s appointments, consumer products, and classroom experiences to dating and even your vacations or a peaceful walk in the woods.  

 

The coursework for this domain will introduce students to the diversity of data sources used by population scientists and the modern methods used to analyze these data. Data exposure will cover a gambit from traditional sources like Census data and nationally representative longitudinal surveys to more modern applications of administrative data, network data, spatially referenced data, and computationally intensive unstructured data from social media platforms.

Questions to consider when choosing a domain:

 

Would you like to apply data science to a specific area or sector, or would you prefer to be as broad as possible?

What classes have you taken and enjoyed? Is there a domain in those areas? If not, is there one that seems related to those areas?

What are you passionate about and how do you want to apply that in a career?

Additional Degree Options

Double Majors, Dual Degrees, and Minors

Students can enrich their college experience by adding a double major, a dual degree, and/or a minor.

Statistical data science majors who wish to add another major in the College of Liberal Arts and Sciences (CLAS), a major outside of CLAS, or a minor should consult with the statistics staff advisor about which option might work best based on their career goals, interests, and graduation timeline.

Students interested in a major or minor outside of statistics should also meet with that department to get a full understanding of the requirements for the major or minor, and the timeline for graduation.

Students interested in adding statistical data science as a double major can meet with the statistics staff advisor to discuss the major, requirements, and timeline for graduation.

Honors Program

Highly motivated students seeking a more intensive workload have the option of enrolling in the UConn Honors Program, which involves completing challenging coursework and an honors thesis. Learn more about the program.

Academic Advising

Statistical data science majors work with both a staff advisor and faculty advisors throughout their undergraduate careers.

The staff advisor can help you with selecting classes, registration, general education requirements, and more.

Your faculty advisor can help you explore opportunities such as internships, research, and applying to graduate school. Learn more about the role of advising in the Department of Statistics.

Ready to Declare Your Major?

Students must apply for admission to the BS in Statistical Data Science program.

Current and prospective UConn students can indicate their intention to become a major by declaring as a pre-statistical data science major. Pre-majors must formally apply to the BS in Statistical Data Science program when they are eligible.

All eligible students may apply to the major. UConn students do not need to be pre-majors to apply. All applicants will be considered equally during application review.

Apply to the Major

Contact Us

For questions about the BS in Statistical Data Science, please contact the undergraduate program director:

Elizabeth Schifano

Associate Professor of Statistics
elizabeth.schifano@uconn.edu