Earn your BS in Data Science
The BS in Data Science produces graduates with the sophisticated analytical and computational skills required to thrive in a quantitative world where new problems are encountered at an ever-increasing rate. The major emphasizes the statistical/probabilistic and algorithmic methods that underlie the preparation, analysis, and communication of complex data. With focus on technical foundations, the data science program promotes skills useful for creating and implementing new or special-purpose analysis and visualization tools. It also promotes a fundamental understanding of how to best handle uncertainty when making data-driven decisions.
Students develop data preparation skills including writing software to obtain, extract, merge, clean, and/or transform the raw data. Analysis or information extraction methods include machine and statistical learning, statistical modeling and inference, and algorithm efficiency analysis. Data visualization, report writing, and oral presentations are important communication methods. The major includes two capstone courses that emphasize communication, ethics, and teamwork.
The Department of Statistics and Actuarial Science and the Department of Computer Science collaborate to offer the major in data science. The BS in Data Science is administered by the Department of Statistics and Actuarial Science.
Learning outcomes
Communication skills
Graduates will be able to:
- Clearly justify and communicate study results to a nontechnical audience;
- Write accurate and meaningful reports that describe the statistical and computational analyses and summarize important findings; and
- Work effectively as part of a team to address substantive questions that can be handled using statistical and computational methods.
Data curation skills
Graduates will be able to:
- Understand issues associated with data collection, management, provenance, storage, merging, sharing, and preparation;
- Work with multiple-source, multiple-format data;
- Investigate the quality of the data; and
- Have a basic understanding of ethical and confidentiality issues associated with data collection, storage, merging, and sharing
Mathematical skills
Graduates will:
- Have a firm grasp of the mathematical tools underlying statistical and computational methods which are primarily based on ideas in calculus, linear algebra, and discrete mathematics, including distribution theory, uncertainty quantification (e.g., probability theory), the probabilistic basis of formal statistical inference, models, and algorithms, and combinatorial analysis and recursion, which are used for algorithmic analysis, design, and for distribution theory.
Computational skills
Graduates will be able to:
- Use critical thinking skills to translate substantive questions into well-defined computational problems and choose appropriate computational techniques for a given problem;
- Understand the foundational software skills and associated algorithmic and computational problem-solving methods used in computer science;
- Be proficient in computational methods for collecting, managing, storing, preparing, sharing, and describing data numerically and graphically from a variety of sources to design and carry out basic simulation studies; and
- Use professional statistical software and understand the principles of programming and algorithmic problem solving that underlie these packages.
Statistical/probabilistic skills
Graduates will be able to:
- Use critical thinking skills to translate substantive questions into well-defined statistical or probability problems and choose the appropriate graphical or numerical descriptive and/or inferential statistical techniques for a given problem;
- Understand the importance of, and issues related to, the choice of the study design, such as designed experiment vs. probability sample vs. convenience sample, used to produce data;
- Understand that uncertainty, variability, and randomness play significant roles in data-driven decision making;
- Understand how to measure and display uncertainty, the effect of randomness, confidence/credibility, and the likelihood of incorrect inferences;
- Understand and be able to explain common misperceptions, paradoxes, and fallacies of probability and statistics; and
- Understand basic regression, prediction, simulation, and visualization methods.
Requirements and program planning
The Bachelor of Science with a major in data science requires a minimum of 120 semester hours, including at least 59 semester hours of work for the major. Students must maintain a grade-point average (GPA) of at least 2.00 in all courses for the major and in all UI courses for the major. They also must complete the College of Liberal Arts and Sciences GE CLAS Core.
Data science majors may not earn a major or minor in computer science or statistics, a major in computer science and engineering, the Certificate in Large Data Analysis, or the Certificate in Social Science Analytics.
The BS with a major in data science requires the following course work:
| Course type | Semester hours |
|---|---|
| Prerequisite courses | 12-16 |
| Core courses | 26 |
| Advanced courses | 9 |
| Advanced electives | 9 |
| Capstone courses | 4 |
| Total hours | 59-63 |
Courses
Choose one of the following sequences:
| MATH:1550 | Engineering Mathematics I: Single Variable Calculus | 4 |
| MATH:1560 | Engineering Mathematics II: Multivariable Calculus | 4 |
| MATH:2700 | Introduction to Linear Algebra | 4 |
or
| MATH:1850 | Calculus I | 4 |
| MATH:1860 | Calculus II | 4 |
| MATH:2700 | Introduction to Linear Algebra | 4 |
| MATH:2850 | Calculus III | 4 |
| Course number | Course name | Semester hours |
|---|---|---|
| CS:1210 | Computer Science I: Fundamentals | 4 |
| CS:2210 | Discrete Structures | 3 |
| CS:2230 | Computer Science II: Data Structures | 4 |
| CS:3330 | Algorithms | 3 |
| DATA:3200/STAT:3200 | Applied Linear Regression | 3 |
| STAT:2010 | Statistical Methods and Computing | 3 |
| STAT:3100/IGPI:3100 | Introduction to Mathematical Statistics I | 3 |
| STAT:3101/IGPI:3101 | Introduction to Mathematical Statistics II | 3 |
Both of these advanced courses:
| CS:4400 | Database Systems | 3 |
| DATA:4580 / STAT:4580 / IGPI:4580 | Data Visualization and Data Technologies | 3 |
And choose one of the following advanced courses:
| CS:5430 | Machine Learning | 3 |
| DATA:4540 / STAT:4540 / IGPI:4540 | Statistical Learning | 3 |
Three of these, with at least one computer science course and one statistics course:
| CS:4440 | Web Mining | 3 |
| CS:4470 | Health Data Analytics | 3 |
| CS:4510 | Human-Computer Interaction for Computer Science | 3 |
| CS:4630 | Mobile Computing | 3 |
| CS:4700 / Math:4860 | High Performance and Parallel Computing | 3 |
| CS:5630 | Cloud Computing Technology | 3 |
| ACTS:6220 / DATA:6220 | Predictive Analytics | 3 |
| STAT:3210 | Experimental Design and Analysis | 3 |
| STAT:4520 / IGPI:4522 / IGPI:4520 | Bayesian Statistics | 3 |
| STAT:4560 | Statistics for Risk Modeling | 3 |
| STAT:5810 / BIOS:5310 / IGPI:5310 | Research Data Management | 3 |
Other advanced computer science or statistics courses approved by advisor.
| Course number | Course name | Semester hours |
|---|---|---|
| STAT:4880 | Data Science Creative Component | 1 |
| STAT:4890 | Data Science Practicum | 3 |
Create your academic path
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Questions?
Sanvesh Srivastava
BS/MS in Business Analytics
Students majoring in data science who are interested in earning a master's degree in business analytics with a career subprogram may apply to the combined BS/MS program offered by the College of Liberal Arts and Sciences and the Tippie College of Business. The program enables students to begin the study of business analytics before they complete their bachelor's degree. Students are able to complete both degrees in five years rather than six.
Separate application to each degree program is required. Applicants must be admitted to both programs before they may be admitted to the combined degree program.
BS/MS in Finance
Students majoring in data science who are interested in earning a master's degree in finance may apply to the combined BS/MS program offered by the College of Liberal Arts and Sciences and the Tippie College of Business. The program enables students to begin the study of finance before they complete their bachelor's degree. Students are able to complete both degrees in five years rather than six.
Separate application to each degree program is required. Applicants must be admitted to both programs before they may be admitted to the combined degree program.