College of Liberal Arts & Sciences
Data Science
Program Overview
The B.S. 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 everincreasing 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 specialpurpose analysis and visualization tools. It also promotes a fundamental understanding of how to best handle uncertainty when making datadriven 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 B.S. in data science is administered by the Department of Statistics and Actuarial Science.
Flow Chart Printable Sample Schedule
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 multiplesource, multipleformat 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 welldefined computational problems and choose appropriate computational techniques for a given problem;
 understand the foundational software skills and associated algorithmic and computational problemsolving 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/Probablilistic Skills
Graduates will be able to:
 use critical thinking skills to translate substantive questions into welldefined 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 datadriven 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.
Requirements
The Bachelor of Science with a major in data science requires a minimum of 120 s.h., including at least 59 s.h. of work for the major. Students must maintain a g.p.a. 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 B.S. with a major in data science requires the following course work.
Prerequisite Courses 
1216 
Core Courses 
26 
Advanced Courses 
9 
Advanced Electives 
9 
Capstone Courses 
3 
Total Hours 
5963 
Prerequisite Courses
Students choose one of the following sequences.
These: 


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 these: 


MATH:1850 
Calculus I 
4 
MATH:1860 
Calculus II 
4 
MATH:2700 
Introduction to Linear Algebra 
4 
MATH:2850 
Calculus III 
4 
Core Courses
All of these: 


CS:1210 
Computer Science I: Fundamentals 
4 
CS:2210 
Discrete Structures 
3 
CS:2230 
Computer Science II: Data Structures 
4 
CS:3330 
Algorithms 
3 
STAT:2010 
Statistical Methods and Computing 
3 
STAT:3100 
Introduction to Mathematical Statistics I 
3 
STAT:3101 
Introduction to Mathematical Statistics II 
3 
STAT:3200 
Applied Linear Regression 
3 
Advanced Courses
Both of these: 


CS:4400 
Database Systems 
3 
STAT:4580 
Data Visualization and Data Technologies 
3 
One of these: 


CS:5430 
Machine Learning 
3 
STAT:4540 
Statistical Learning 
3 
Advanced Electives
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:4700 
High Performance and Parallel Computing 
3 
CS:5630 
Cloud Computing Technology 
3 
STAT:3210 
Experimental Design and Analysis 
3 
STAT:4520 
Bayesian Statistics 
3 
STAT:4560 
Statistics for Risk Modeling 
3 
STAT:5810 
Research Data Management 
3 
Other advanced computer science or statistics courses approved by advisor 

Capstone Courses
Both of these: 


STAT:4880 
Data Science Creative Component 
1 
STAT:4890 
Data Science Practicum 
2 
Honors in the Major
Students majoring in data science have the opportunity to graduate with honors in the major. They must must maintain a g.p.a. of at least 3.40 in their major and a cumulative University of Iowa g.p.a. 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.
FourYear Graduation Plan
The FourYear Graduation Plan is not available to students majoring in data science. Students work with their advisors on individual graduation plans.
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 datadriven business model. Starting just over 20 years ago as an online book seller 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 datadriven 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 then to apply their understanding of both 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.
Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources.