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
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
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 versus probability sample versus 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.
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.
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.
Program Requirements
The Bachelor of Science with a major in data science requires a minimum of 120 s.h., including at least 60 s.h. of work for the major. Students must maintain a grade-point average 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, or the Certificate in Social Science Analytics.
The BS with a major in data science requires the following coursework.
| requirements | Semester hours |
|---|---|
| Prerequisite Courses | 16 |
| Core courses | 36 |
| Advanced Electives | 9 |
| Capstone Course | 3 |
Courses
Prerequisite Courses
| Course # | Title | Hours |
|---|---|---|
| MATH:1850 | Calculus I | 4 |
| MATH:1860 | Calculus II | 4 |
| MATH:2700 | Introduction to Linear Algebra | 4 |
| MATH:2850 | Calculus III | 4 |
Core courses
| Course # | title | Hours |
|---|---|---|
| DATA:3200/ IGPI:3200/ ISE:3760/ STAT:3200 | Applied Linear Regression | 3 |
| DATA:4580/ IGPI:4580/ STAT:4580 | Data Visualization and Data Technologies | 3 |
| CS:1210 | Computer Science I: Fundamentals | 4 |
| CS:2210 | Discrete Structures | 3 |
| CS:2230 | Computer Science II: Data Structures | 4 |
| CS:3330 | Algorithms | 3 |
| CS:4400 | Database Systems | 3 |
| STAT:2010 | Statistical Methods and Computing | 3 |
| STAT:3100/ IGPI:3100 | Introduction to Mathematical Statistics I | 4 |
| STAT:3101/ IGPI:3101 | Introduction to Mathematical Statistics II | 3 |
| One of these: | ||
| DATA:4540/ BAIS:4540/ IGPI:4540/ STAT:4540 | Statistical Learning | 3 |
| CS:5430 | Machine Learning | 3 |
Advanced Electives
9 s.h. from these, with at least one computer science course (prefix CS) and one statistics course (prefix STAT):
| course # | Title | Hours |
|---|---|---|
| DATA:4600/ STAT:4600 | Causal Inference for Data Science | 3 |
| DATA:4610 | Data Acquisition and Management | 3 |
| DATA:4620 | Text Data Analysis | 3 |
| DATA:4750/ STAT:4750 | Probabilistic Statistical Learning | 3 |
| DATA:4880 | Data Science Creative Component | 1 |
| DATA:6200/ ACTS:6200/ STAT:6200 | Predictive Analytics | 3 |
| BIOS:4510 | Data Science Foundations in R | 2 |
| CS:4420 | Artificial Intelligence | 3 |
| 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 |
| MATH:4840 | Mathematics of Machine Learning | 3 |
| STAT:3210 | Experimental Design and Analysis | 3 |
| STAT:4520/ IGPI:4522/ PSQF:4520 | Bayesian Statistics | 3 |
| STAT:4560 | Statistics for Risk Modeling I | 3 |
| Other advanced computer science or statistics courses approved by advisor |
Capstone Course
| Course # | title | hours |
|---|---|---|
| DATA:4890 | Data Science Practicum | 3 |
Undergraduate to Graduate (U2G) Program
Bachelor of Science students in data science may pair their degree with an Undergraduate to Graduate (U2G) program, which allows a student to earn a bachelor's and master's degree in five years of study. See the Undergraduate to Graduate website for available programs.
Honors in the Major
Students majoring in data science have the opportunity to graduate with honors in the major. They must maintain a grade-point average (GPA) of at least 3.67 in their major and a cumulative University of Iowa GPA of at least 3.33. Students must complete an honors thesis.
Students are responsible for finding a faculty member willing to supervise their honors project. The faculty member must approve the proposed project and a timetable for the work. Credit for thesis work must be earned in either CS:3990 Honors in Computer Science or Informatics for work supervised by a computer science faculty member or an honors course supervised by a statistics and actuarial science faculty member.
Honors in data science also satisfies the 12 s.h. experiential learning requirement for University of Iowa honors students.
University of Iowa Honors Program
In addition to honors in the major, students have opportunities for honors study and activities through membership in the University of Iowa Honors Program. Visit Honors at Iowa to learn about the university's honors program.
Membership in the UI Honors Program is not required to earn honors in the data science major.
Career Advancement
Today, nearly every business, government, social media platform, and educational institution collects and analyzes data about its users, logistics and operations, and media presence, in the hope of extracting valuable insights and utilizing the resulting efficiencies.
As an example, Amazon is the company most closely identified with a data-driven business model. Starting just over 25 years ago as an online bookseller with a relatively crude crowdsourced book review platform and simple recommender system technology, it was subsequently augmented with extensive tracking of customer page views, advertising hits, data about prior purchases, and an aggressive emphasis on data-driven operational efficiencies. Amazon has become the major player in U.S. retail and a prime example of the strategic value of big data.
Data science graduates may pursue careers as data scientists. This position allows them to apply their understanding of statistics, as well as algorithm and software design, to create and develop the next generation of data analysis tools.
The Pomerantz Career Center offers multiple resources to help students find internships and jobs.
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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.