The rapidly expanding collection and use of data is driving transformations across broad segments of industry, science, and society. These changes have sparked great demand for individuals with skills in managing and analyzing complex data sets. Such skills are interdisciplinary, involving ideas typically associated with computing, information processing, mathematics, and statistics as well as the development of new methodologies spanning these fields. Our major in Data Science (offered jointly with the Department of Statistics and SCI) offers a program specifically geared to training students to participate in this data revolution.

This undergraduate major allows students to gain critical skill sets that span key areas of mathematics, computing, and statistics, with foundational training providing literacy in four areas (data, algorithmic, mathematical, and statistical) that every student needs to master data science. Students will develop expertise that connects theory to the solution of real-world problems and be able to specialize their studies towards a more specific career focuses. Completing this major will prepare students to work as a data science professional or to pursue graduate study in a direction involving data in a significant way.

## Choosing between Data Science and other Mathematics Majors

Students who graduate with any of the Mathematics majors or the Data Science major will be well qualified for positions in data science. The Data Science major is designed for students whose main passion is working with data, including mathematical, statistical, and computing aspects. The other Mathematics majors are designed for students whose primary interest is in mathematics: its beauty, its elegance, its logical structure, and/or its utility for modeling real-world systems and solving real-world problems.

## Double Majors in Data Science and Math

It is quite possible to complete a double major in Data Science and one of the Mathematics directions. In this case, up to five courses can be chosen to count for both majors. (This is an exception to the usual limit of 8 overlapping credits between Dietrich School majors.)

Students considering this option should choose Math 1180 as their linear algebra class, as this is the required linear algebra course for all math majors.

## Major Requirements

For full details, see the official major sheet.

### Foundation

- CMPINF/STAT 1061 Data Science Foundations
- CMPINF 0401 Intermediate Programming
- CS 0445 Algorithms and Data Structures 1
- MATH 0220 Calculus 1
- MATH 0230 Calculus 2
- MATH 0280/MATH 1180 Intro to Matrices/Linear Algebra
- MATH 0480/CS 0441 Applied Discrete Mathematics
- STAT 1151/STAT 1631 Intro Probability
- STAT 1152/STAT 1632 Intro Mathematical Statistics

### Expertise

- CS 0590 Social Implications of Computing Technology
- CS 1501 Algorithms and Data Structures 2
- CS 1656 Introduction to Data Science
- CS 1675 Introduction to Machine Learning
STAT 1361 Learning and Data Science**or** - MATH 1101 Optimization
- STAT 1261 Principles of Data Science

### Specialization

Students should take 3 courses from one of the following 4 specialty areas:

### Capstone

Students must complete one of the following 3 courses:

- CMPINF 1981 Project Studio
- MATH 1103 BIG Problems
- STAT 1961 Data Science in Action