Statistical Data Science (BS)
The Statistical Data Science major gives students a broad training in the following core areas of data science:
- computer programming and data management
- basic and advanced data analysis
- data visualization
- data ethics
Students with this major obtain a Bachelor of Science (B.S.) degree. The major can be tailored for a student’s interest in a domain concentration.
Admission
Students with this major obtain a Bachelor of Science (B.S.) degree. The major can be tailored for a student’s interest in a domain concentration.
In order to apply to the Statistical Data Science major, students must have:
- A GPA of 3.2 or higher in the following classes: MATH 1132Q Calculus II, STAT 1000Q Introduction to Statistics I/STAT 1100Q Elementary Concepts of Statistics, and an introductory programming course (CSE 1010 Introduction to Computing for Engineers, CSE 1729 Introduction to Principles of Programming, or STAT 2255 Statistical Programming).
- completed at least 24 credits, 15 of which must be at the University of Connecticut, with a cumulative GPA of 3.2 or higher.
After entry into the majors, students must maintain a 3.2 cumulative GPA.
Location
- Storrs Campus
Modality
- Online
Requirements
Students receiving a B.S. in Statistical Data Science are required to take 36 major credits.
Core Area Requirements
| Course | Title | Credits |
|---|---|---|
| STAT 3255 | Introduction to Data Science | 3 |
| Programming and Data Management | ||
| STAT 2255 | Statistical Programming | 3 |
| or ECON 3322 | Open Source Programming with Python for Economists | |
| Basic Data Analysis | ||
| STAT 3215Q | Applied Linear Regression in Data Science | 3 |
| Select one of the following: | 3 | |
| Statistical Methods | ||
| Introduction to Mathematical Statistics I | ||
| Probability | ||
| Data Ethics | ||
| Select one of the following: | 3 | |
| Data Ethics | ||
| Data Management, Programming, and Privacy | ||
| Data Visualization | ||
| Select at least three credits of the following: | 3-4 | |
| Statistical Computing 1 | ||
| Big Data Science for Biologists 2 | ||
| Cartographic Techniques | ||
| Advanced Analysis | ||
| MATH 2210Q | Applied Linear Algebra | 3 |
| STAT 4255 | Introduction to Statistical Learning | 3 |
| Capstone Course | ||
| STAT 4915 | Data Science in Action 3 | 3 |
| Writing in the Major | ||
| STAT 4916W | Writing in Data Science 3 | 1 |
| Domain Concentration Sequence | ||
| Students must complete one of the nine-credit domain concentration sequences listed below | 9 | |
| Total Credits | 37-38 | |
- 1
Students completing a Statistics domain concentration must take STAT 3375Q Introduction to Mathematical Statistics I and STAT 3675Q Statistical Computing to meet these requirements.
- 2
Recommended for students completing the Biological Data Science domain concentration.
- 3
Students completing a Biological Data Science domain concentration may take any of the following to meet the capstone and W requirement:
- STAT 4915 Data Science in Action, STAT 4916W Writing in Data Science,
- EEB 4896W Senior Research Thesis in Ecology and Evolutionary Biology, or
- MCB 4897W Senior Research Thesis or MCB 4997W Senior Honors Research Thesis.
Credits in EEB 4896W Senior Research Thesis in Ecology and Evolutionary Biology cannot simultaneously count towards both an Honors thesis in EEB and a Data Science capstone. Separate thesis requirements are needed in MCB 4997W Senior Honors Research Thesis for students pursuing Honors Scholar or Laureate designations in both Molecular and Cell Biology and Statistical Data Science.
Domain Concentration Sequence
To complete the domain concentration sequence, students must take at least nine credits from one of the following groups:
Advanced Statistics
| Course | Title | Credits |
|---|---|---|
| STAT 3445 | Introduction to Mathematical Statistics II | 3 |
| Select two of the following: | 6 | |
| Design of Experiments | ||
| Introduction to Biostatistics | ||
| Applied Time Series | ||
| Applied Spatio-Temporal Statistics | ||
| Deep Learning | ||
| Field Study Internship 1 | ||
| Special Topics 2 | ||
- 1
At least and no more than three credits of STAT 4190 Field Study Internship may count towards the major and must be pre-approved by the Department of Statistics for adequate data science content.
- 2
STAT 4195 Special Topics may count at most once towards the domain with the consent of the advisor or undergraduate program director dependent on topic.
American Political Representation
| Course | Title | Credits |
|---|---|---|
| Select three of the following: | 9 | |
| American Political Parties | ||
| Politics of Inequality | ||
| The Art, Science, and Business of Political Campaigns | ||
| Electoral Behavior | ||
| American Political Economy | ||
| Public Opinion | ||
Biological Data Science
| Course | Title | Credits |
|---|---|---|
| Select three of the following: 3 | 9-12 | |
| Independent Study 1 | ||
| Gene Expression | ||
| Introduction to Molecular Evolution and Bioinformatics | ||
| Practical Methods in Microbial Genomics | ||
| Special Topics 2 | ||
| Techniques of Biophysical Chemistry | ||
| Structure and Function of Biological Macromolecules | ||
| Structure and Dynamics of Macromolecular Complexes | ||
| Undergraduate Research 1 | ||
| Honors Undergraduate Research 1 | ||
- 1
Only three credits of EEB 3899 Independent Study, MCB 4896 Undergraduate Research, or MCB 4996 Honors Undergraduate Research can count towards the major, and these credits cannot simultaneously count towards another major or degree.
- 2
MCB 3895 Special Topics may count at most once towards the domain with the consent of the Biological Data Science domain advisor dependent on topic.
- 3
The graduate courses listed below may also count towards the Biological Data Science domain: EEB 5050 Fundamentals of Ecological Modeling, EEB 5300 Practical Genomics in Ecology and Evolution, EEB 5348 Population Genetics, EEB 5349 Phylogenetics, MCB 5430 Analysis of Eukaryotic Functional Genomic Data, MCB 5631 Sequence-based Microbial Community Analysis. Prerequisites vary; please consult instructor.
Students can choose any three courses1 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:
| Course | Title | Credits |
|---|---|---|
| Genome Sequencing and Analysis | ||
| MCB 3201 | Gene Expression | 3 |
| MCB 3421 | Introduction to Molecular Evolution and Bioinformatics | 4 |
| MCB 3637 | Practical Methods in Microbial Genomics | 3 |
| Molecular Structure and Function | ||
| MCB 4008 | Techniques of Biophysical Chemistry | 3 |
| MCB 4009 | Structure and Function of Biological Macromolecules | 3 |
| MCB 4014 | Structure and Dynamics of Macromolecular Complexes | 3 |
- 1
Only three credits of EEB 3899 Independent Study, MCB 4896 Undergraduate Research, or MCB 4996 Honors Undergraduate Research can count towards the major, and these credits cannot simultaneously count towards another major or degree.
Financial Analysis
| Course | Title | Credits |
|---|---|---|
| Select three of the following: | 9 | |
| Elementary Economic Forecasting | ||
| Financial Econometrics | ||
| International Finance | ||
| Convex Optimization with Python | ||
Marine Science
| Course | Title | Credits |
|---|---|---|
| Select three of the following: | 9-11 | |
| Marine Sciences and Society | ||
| Foundations of Marine Sciences | ||
| Foundations of Marine Sciences | ||
| Marine Biology | ||
| Measurement and Analysis in Coastal Ecosystems | ||
| Biological Oceanography | ||
| Experimental Design in Marine Ecology | ||
Population Dynamics
| Course | Title | Credits |
|---|---|---|
| Select three of the following: | 9 | |
| Sociology of Education | ||
| Sociology of Families | ||
| Sociology of Health | ||
| Sociological Perspectives on Poverty | ||
| Urban Sociology | ||
| Population in a Changing World | ||
Bachelor of Science Requirements
For a Statistical Data Science major that leads to a Bachelor of Science degree, students must take:
| Course | Title | Credits |
|---|---|---|
| Statistics Course | ||
| Select one of the following: | 4 | |
| Introduction to Statistics I | ||
| Elementary Concepts of Statistics | ||
| MATH Courses | ||
| Selection one of the following MATH sequences: | 8 | |
| Sequence 1 | ||
| Calculus I and Calculus II | ||
| Sequence 2 | ||
| Advanced Calculus I and Advanced Calculus II | ||
| Select one of the following: | 3-4 | |
| Multivariable Calculus | ||
| Applied Linear Algebra | ||
| Elementary Differential Equations | ||
| Science Sequence | ||
| Select a sequence in one of the following that includes laboratory measurements 1 | 8-10 | |
| Biology | ||
| Principles of Biology I | ||
| Principles of Biology II | ||
or BIOL 1110 | Introduction to Botany | |
| Chemistry | ||
| Select one of the following sequences: | ||
| Fundamentals of General Chemistry I and Fundamentals of General Chemistry II and Fundamentals of General Chemistry III | ||
| General Chemistry I and General Chemistry II | ||
| Honors General Chemistry I and Honors General Chemistry II | ||
| Physics | ||
| Select one of the following sequences: | ||
| General Physics I and General Physics II | ||
| General Physics with Calculus I and General Physics with Calculus II | ||
| Physics for Engineers I and Physics for Engineers II | ||
| Fundamentals of Physics I and Fundamentals of Physics II | ||
| Total Credits | 23-26 | |
- 1
For students following the former General Education curriculum, one of these courses may be used to fulfill the CA 3 laboratory science requirement of the University’s General Education requirements. In addition, students must take one other CA 3 course from a different subject area, but it need not be a lab course. For students following the Common Curriculum, one of these courses may be used to fulfill the TOI 6 laboratory science requirement of the University’s Common Curriculum requirements. In addition, students must take one other TOI 6 course from a different subject area, but it need not be a lab course.
University Common Curriculum Requirements
Every student must meet a set of core requirements to earn a baccalaureate degree, in addition to those required by the student's major course of study and other requirements set by the student's school or college. For more information about these requirements, please see Common Curriculum Requirements.
College of Liberal Arts and Sciences Degree Requirements
Students must meet a set of requirements established by the college in addition to the University's Common Curriculum requirements. For more information, see the College of Liberal Arts and Sciences section of this catalog.
Learning Objectives
- Demonstrate proficiency in computer programming and data management, including topics such as data types, control flow, object-oriented programming, algorithmic thinking, efficient implementation of different data structures, control and data abstraction.
- Demonstrate proficiency in expositing for technical and non-technical audiences in writing, including organizing and describing data and analyses at a level appropriate for one’s audience, thinking critically about the results and implications of such analyses, creating informative and effective visualizations, and communicating an analysis pipeline through well-written, reproducible code and report.
- Demonstrate proficiency in understanding data ethics, including systematic approaches to assessing ethical issues; privacy and confidentiality; defining research and the responsibilities associated with conducting ethical research; and implicit and structural biases in data collection and analysis.
- Demonstrate proficiency in the domain concentration.