Learning Outcomes
Graduates will be able to:
- understand the fundamental concepts in probability and statistics that underlie commonly used data science algorithms;
- write efficient Python and R codes for data processing and data wrangling (data storage, access, and management) and computing for data analysis and modeling;
- use visualization techniques to display salient data features;
- use data technologies to process complex data;
- correctly and effectively implement appropriate algorithms for learning with data;
- identify and criticize inappropriate/unethical uses of data and/or algorithms;
- acquire effective communication skills for disseminating findings; and
- work with data stakeholders to help collect and analyze data.
The program aims to train the next generation of data scientists with the analytical and technical skills to explore, formulate, and solve complex data-driven problems in science, industry, business, and government. The program focuses on the theory, methodology, application, and ethics for working with and learning from data. Students acquire the abilities to develop and implement new or special purpose analysis and visualization tools, and a fundamental understanding of how to quantify uncertainty in data-driven decision-making.
The Master of Science program in data science requires 30 s.h. of graduate credit. Students must maintain a grade-point average of at least 3.00 in all work toward the degree and in additional relevant course work.
Coursework includes core courses covering the fundamentals of data science including probability and statistics; data storage, access, and management; and data visualization, exploration, modeling, analysis, and uncertainty quantification. Students acquire hands-on experience in solving real-world problems, communication skills, and data ethics via a required capstone project. Students choose electives from a wide variety of courses on specialized data science topics offered by the departments of Statistics and Actuarial Science, Computer Science, Business Analytics, and Biostatistics to enhance their skill sets based on their interests and career goals.
The MS with a major in data science requires the following coursework.
Applicants must meet the admission requirements of the Graduate College; see the Manual of Rules and Regulations on the Graduate College website.
The program prepares graduates for careers in academia, industry, business, or government that involve data visualization and modeling, managing reproducible data analysis workflows, and collaborating and communicating with scientists and other data stakeholders.
Sample Plan of Study
Sample plans represent one way to complete a program of study. Actual course selection and sequence will vary and should be discussed with an academic advisor. For additional sample plans, see MyUI.
Data Science, MS
Plan of Study Grid (Manual)
Academic Career |
Any Semester |
a |
|
| Hours | 0 |
First Year |
Fall |
STAT:3120 |
Probability and Statistics |
4 |
STAT:3200 |
Applied Linear Regression |
3 |
STAT:4540 |
Statistical Learning |
3 |
| Hours | 10 |
Spring |
DATA:4750 |
Probabilistic Statistical Learning |
3 |
STAT:4580 |
Data Visualization and Data Technologies |
3 |
STAT:5400 |
Computing in Statistics |
3 |
| Hours | 9 |
Second Year |
Fall |
b |
3 |
b |
3 |
b |
3 |
| Hours | 9 |
Spring |
DATA:5890 |
MS Data Science Practicum |
2 |
c |
|
| Hours | 2 |
| Total Hours | 30 |