Data Science Major

Elena Zheleva, UIC computer science department faculty, with a data visualization

Data science — the ability to organize, process, and derive insight from enormous amounts of information — draws from a variety of skill sets. Data scientists are trained in computing. They’re also experts in statistics. They understand how their work influences the business world, from corporations to government agencies. And they have the ability to take the complex work they do and explain it to all audiences, making them excellent communicators.

To make sure each UIC data science major is prepared to excel at all of these tasks, we tapped into the expertise of three UIC colleges for the program’s core courses: the College of Engineering (home to the computer science department), the College of Liberal Arts and Sciences (which houses math, statistics, and communication), and the College of Business. Two more colleges, the College of Applied Health Sciences and the College of Urban Planning and Policy Administration, contribute to our specialized data-science concentrations.

Together, we will teach you how to do highly important work that is often highly compensated: developing methods to comb through huge repositories of data, unearthing the nuggets of meaningful information that can help businesses, governments, and nonprofit organizations to chart the best course.

One of the great advantages of data science is that you can apply its skills to any field or industry that interests you. Data science approaches are relevant in health care, computing, information technology, urban planning, retail, marketing, manufacturing, transportation, communication, education, insurance, finance, science, security, law enforcement, and more. Data science at UIC is a multidisciplinary major that builds a strong foundation and gives you the option to specialize in a field of your choice (see our nine concentrations below).

The data science major will be outlined in detail in the UIC course catalog. In the meantime, the content below provides an overview.

Wondering about data science?

Data science isn’t a subject most students take in high school, so you might be curious what it’s all about. This one-page PDF overview will help.

Download the PDF

Data science major requirements

Data science majors complete coursework in three major categories:

General education courses are designed to make you a well-rounded, well-informed graduate across disciplines outside of data science. These requirements total 37 credit hours:

  • ENGL 160 Academic Writing I: Writing in Academic and Public Contexts (3 credits)
  • ENGL 161 Academic Writing II: Writing for Inquiry and Research (3 credits)
  • Foreign language (8 credits)
  • Understanding the Individual and Society course (3 credits)
  • Understanding U.S. Society course (3 credits)
  • Exploring World Cultures course (3 credits)
  • Understanding the Creative Arts course (3 credits)
  • Understanding the Past course (3 credits)
  • Analyzing the Natural World courses: two classes that include labs (8 credits total)

Data science majors complete the following core courses to develop their foundation in the set of fields that underpin this interdisciplinary major. These core requirements add to 57 credits toward the degree.

  • ENGR 100 Engineering Orientation (no credit)
  • MATH 180 Calculus I (4 credits)
  • MATH 181 Calculus II (4 credits)
  • MATH 210 Calculus III (3 credits)
  • MATH 310 Applied Linear Algebra (3 credits)
  • CS 111 Program Design I (3 credits)
  • CS 141 Program Design II (4 credits)
  • CS 151 Mathematical Foundations of Computing (3 credits) or MCS 361 Discrete Mathematics (3 credits)
  • CS 211 Programming Practicum (2 credits)
  • CS 251 Data Structures (4 credits)
  • CS 377 Communications and Ethical Issues in Computing (3 credits)
  • STAT 382 Statistical Methods and Computing or IDS 462 Statistical Software for Business Applications (3 credits)
  • STAT 385 Elementary Statistical Techniques for Machine Learning and Big Data (3 credits)
  • STAT 481 Applied Statistical Methods II (3 credits)
  • IDS 312 Business Project Management (3 credits)
  • IDS 435 Optimization Models and Methods (3 credits)
  • CS 418 Introduction to Data Science or IDS 472 Business Data Mining (3 credits)
  • CS 480 Database Systems or IDS 410 Business Database Technology (3 credits)

Plus one course chosen from the three below:

  • STAT 381 Applied Statistical Methods I (3 credits)
  • IE 342 Probability and Statistics for Engineers (3 credits)
  • ECE 341 Probability and Random Processes for Engineers (3 credits)

All data science majors must choose and complete one of nine concentrations. See the “Concentrations” section on this page for more information on the courses for each one.

Data science concentrations

The nine concentrations offered to data science majors are made up of courses that come from five UIC colleges: engineering, liberal arts and sciences, business, applied health sciences, and urban planning and policy administration. Some concentrations take all their courses from one college, while other concentrations involve a mixture of courses from different sources. Expand the sections below to see the requirements for each one.

Advice from our faculty

Elena Zheleva

Why consider the data science major? Do you want to help your community, improve healthcare, or design the next generation of AI-powered systems? UIC’s data science curriculum will equip you with the statistical and computational background necessary to tackle the most pressing problems of our times. The best part is that the problems you choose to work on can be as diverse as your background and interests.

Elena Zheleva, PhD  |  Assistant Professor, Computer Science
Zizi Papacharissi

Data science is code speak for the future. It is a way for understanding how technologies reproduce and reinforce social injustices and, at the same time, re-deploying technologies to help reimagine our world. Data organize our world in categories that underscore how we connect as human beings but also how we may drift apart, fostering cultures of marginalization, misinformation, and algorithmic bias.

If you want to unlock technology and use it to make the world better, this is the degree for you.

Zizi Papacharissi, PhD  |  Professor and Department Head, Communication; Professor, Political Science
Gonzalo Bello

In the past decade, advances in the way data can be generated, collected, stored, and disseminated have resulted in an exponential growth of data. This has led to an increasing demand for skilled professionals in the field of data science who can analyze and extract insights from this data. UIC’s new data science major will provide students with a solid foundation in computer science, information and decision sciences, and statistics, and will position them to succeed in this rapidly-growing field.

Gonzalo A. Bello  |  Clinical Assistant Professor, Computer Science

Student learning objectives: Data science major

If you choose to major in data science at UIC, what can you expect by the time you have finished the program? Our faculty compiled the list below to give you a sense of the competencies we expect students to possess when they have completed all the requirements.

By the time they graduate, data science students will:

  1. Be able to analyze a complex data analysis problem and to apply principles of computing, statistics, and mathematics to identify solutions
  2. Be able to design, implement, and evaluate a computing-based solution to meet a given set of requirements in the context of data science
  3. Be able to function effectively on teams to accomplish a common goal
  4. Be able to carry out statistical analysis of data
  5. Be able to execute statistical analyses with professional statistical software
  6. Be able to explain how data science projects contribute to the strategic goals of organizations
  7. Either have domain knowledge and be able to apply it in an area of application of data science, or have advanced knowledge of at least one of software development, computing theory, or statistical theory, and be able to apply it to solve data science problems

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