New Major! DATA SCIENCE

data

Graduates with the sophisticated analytical and computational skills will thrive in a quantitative world where new problems are encountered.

https://stat.uiowa.edu/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 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.

The B.S. in data science is administered by the Department of Statistics and Actuarial Science. 
For more information stop into 241 Schaeffer Hall 
or email us at data-science@uiowa.edu

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/Probablilistic 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.

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

12-16

Core Courses

26

Advanced Courses

9

Advanced Electives

9

Capstone Courses

3

Total Hours

59-63

 

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.

Four-Year Graduation Plan

The Four-Year 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 data-driven 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 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 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.

 

poster

The University of Iowa

Department of Statistics & Actuarial Science
241 Schaeffer Hall
Iowa City, Iowa 52242-1409