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 Master of Science program in data science requires 30 s.h. of graduate credit. Students must maintain a g.p.a. of at least 3.00 in all work toward the degree and in additional relevant course work.

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.

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 Department of Statistics and Actuarial Science, Computer Science, Business Analytics, and Biostatistics to enhance their skill set, based on their interests and career goals.

The M.S. with a major in data science requires the following coursework.

All of these:
DATA:4750Probabilistic Statistical Learning3
DATA:5890M.S. Data Science Practicum2
STAT:3120/DATA:3120/IGPI:3120Probability and Statistics4
STAT:3200/IGPI:3200/ISE:3760Applied Linear Regression3
STAT:4540/BAIS:4540/IGPI:4540Statistical Learning3
STAT:4580/IGPI:4580Data Visualization and Data Technologies3
STAT:5400/IGPI:5400Computing in Statistics3
At least 9 s.h. from these:
STAT:3210Experimental Design and Analysis3
STAT:4520/IGPI:4522/PSQF:4520Bayesian Statistics3
STAT:4560Statistics for Risk Modeling I3
STAT:5810/BIOS:5310/IGPI:5310Research Data Management3
STAT:6530/IGPI:6530Environmental and Spatial Statistics3
STAT:6550/BIOS:6310/IGPI:6310Introductory Longitudinal Data Analysis3
STAT:6560Applied Time Series Analysis3
BAIS:6100Text Analytics3
BAIS:6130Applied Optimization3
BAIS:6210Data Leadership and Management3
BIOS:6720Statistical Machine Learning for Biomedical and Public Health Data3
CS:4310Design and Implementation of Algorithms3
CS:4400Database Systems3
CS:4470Health Data Analytics3
CS:5110/IGPI:5110Introduction to Informatics3
CS:5430Machine Learning3
CS:5630Cloud Computing Technology3

Applicants must meet the admission requirements of the Graduate College; see the Manual of Rules and Regulations on the Graduate College website.

The program prepare​s​ graduates for careers in academe​, 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, M.S.

Plan of Study Grid (Manual)
Academic Career
Any SemesterHours
30 s.h. must be graduate level coursework; graduate transfer credits allowed upon approval. More information is included in the General Catalog and on department website. a
 Hours0
First Year
Fall
STAT:3120 Probability and Statistics 4
STAT:3200 Applied Linear Regression 3
STAT:4540 Statistical Learning 3
 Hours10
Spring
DATA:4750 Probabilistic Statistical Learning 3
STAT:4580 Data Visualization and Data Technologies 3
STAT:5400 Computing in Statistics 3
 Hours9
Second Year
Fall
Elective course b 3
Elective course b 3
Elective course b 3
 Hours9
Spring
DATA:5890 M.S. Data Science Practicum 2
Final Exam c
 Hours2
 Total Hours30
a
Students must complete specific requirements in the University of Iowa Graduate College after program admission. Refer to the Graduate College website and the Manual of Rules and Regulations for more information.
b
Work with faculty advisor to select appropriate coursework.
c
Completion of capstone project.