Computational Data Science (B.S.)

Curriculum

Courses

1. University Requirements: (23-24 Credit (cr))

• Writing, Rhetoric and American Cultures (WRA) 4 cr
• Integrative Studies in Humanities (IAH)
  IAH 201-210 and IAH 211 or > 8 cr
• Integrative Studies in Social Sciences (ISS)
  ISS 2XX and ISS 3XX 8 cr
• Bioscience (See 3A Below)

2. College Requirements: (28 cr)

• *EGR 100 Introduction to Engineering Design 2 cr
• *CMSE 202 Computational Modeling & Data Analysis II 4 cr
• *MTH 132 Calculus I 3 cr
• *MTH 133 Calculus II 4 cr
• MTH 234 Multivariable Calculus 4 cr
• *PHY 183 Physics for Scientists & Engineers I 4 cr
• PHY 184 Physics for Scientists & Engineers II 4 cr

*College Admission Requirement

3. Major Requirements: (60-63 cr)

a. Bioscience (4-6 cr)
Select one course from Group 1 and one course from Group 2.

Group 1

• **BS 161 Cell and Molecular Biology 3 cr
• ENT 205 Pests, Society, & the Environment 3 cr
• IBIO 150 Integrating Biology: From DNA to Populations 3 cr
• MGI 141 Introductory Human Genetics 3 cr
• MGI 201 Fundamentals of Microbiology 3 cr
• PLB 105 Plant Biology 3 cr
• PSL 250 Introductory Physiology 4 cr

Group 2

• BS 171 Cell and Molecular Biology Laboratory 2 cr
• **CEM 161 Chemistry Laboratory I 1 cr
• CEM 162 Chemistry Laboratory II 1 cr
• PHY 191 Physics Laboratory for Scientists I 1 cr
• PHY 192 Physics Laboratory for Scientists II 1 cr
• PLB 106 Plant Biology Laboratory 1 cr

**These courses may have prerequisites, which are not otherwise required in the program. Students should check course descriptions to ensure they are aware of prerequisites.

b. All of the following course: (45 cr)

• CMSE 201 Computational Modelling and Data Analysis I 4 cr
• CMSE 381 Fundamentals of Data Sci Methods 4 cr
• CMSE 382 Optimization Methods in Data Sci 4 cr
• CMSE 495 Experiential Learning in Data Sci (W) 4 cr
• CSE 232 Introduction to Programming II 4 cr
• CSE 300 Social, Ethical, and Professional Issues in Computing 1 cr
• CSE 331 Algorithms and Data Structures 3 cr
• CSE 380 Data Management and the Cloud 4 cr
• CSE 404 Intro to Machine Learning 3 cr
• CSE 482 Big Data Analysis 3 cr
• CSE 480 Database Systems 3 cr
• MTH 314 Matrix Algebra with Computational Applications 3 cr
• STT 180 Introduction to Data Science 4 cr
• STT 380 Probability and Stats for Data Sci 4 cr

c. Five courses selected from the following: (15-18 cr)

• ***CMSE 401 Methods for Parallel Computing 4 cr
• CMSE 402 Data Visualization Principles & Techniques 3 cr
• CSE 335 Software Engineering I 4 cr
• CSE 402 Biometrics and Pattern Recognition 3 cr
• ***CSE 415 Introduction to Parallel Computing 3 cr
• CSE 431 Algorithm Engineering 3 cr
• CSE 434 Autonomous Vehicles 3 cr
• CSE 440 Artificial Intelligence 3 cr
• CSE 445 AI Agents 3 cr
• CSE 471 Media Processing and Multimedia Computing 3 cr
• CSE 472 Computer Graphics 3 cr
• CSE 475 Human-Computer Interaction 3 cr
• CSE 480 Database Systems 3 cr
• CSE 492 Selected Topics in Data Science 3 cr
• MTH 451 Numerical Analysis I 3 cr
• MTH 468 Predictive Analysis 3 cr
• STT 464 Statistics for Biologists 3 cr
• STT 465 Bayesian Statistical Methods 3 cr

*** Both CSE 415 and CMSE 401 may not be used to fulfill requirements c.

Concentration in Computational Data Science

The Department offers the following concentration to students wanting an area of specialization in their degree. The concentration is available to, but not required of, any student enrolled in the Bachelor of Science degree program in Computational Data Science. NOTE: Completing the Bachelor of Science degree in Computational Data Science with a concentration may require more than 120 credits. Upon completion of the required courses for a concentration, certification will appear on the student’s official transcript.

Software Engineering

To complete a Bachelor of Science degree in Computational Data Science with a software engineering concentration, students must complete the requirements for the bachelor’s degree, including the following:

The following courses (8 cr):

• CSE 335 Software Engineering I 4 cr
• CSE 336 Software Engineering II 4 cr

Other Electives (Variable)

Total Credits Required for Degree 120 cr

The requirements listed above apply to students admitted to the major of Computational Data Science in the Department of Computer Science and Engineering beginning Fall 2026. The Department of Computer Science and Engineering (CSE) constantly reviews program requirements and reserves the right to make changes as necessary. Consequently, each student is strongly encouraged to consult with their advisor to obtain assistance in planning an appropriate schedule of courses.

Sample

First Year

Sophomore Year

Junior Year

Senior Year

Program Objectives 

A graduate of the MSU Computational Data Science Program is prepared to be

• successful in a computing-related profession, or
• successful in graduate study.

To achieve these objectives the department prepares students in the application of fundamental computing principles and software development skills. This preparation includes the design and implementation of systems that solve complex problems. Our graduates will be trained in teamwork, effective communication, professionalism, ethics, and the engagement of learning and applying new ideas and technologies as the field evolves.

Objectives and Outcomes

Computational Data Science Program Objectives

A graduate of the MSU Computational Data Science Program is prepared to be:

• successful in a computing-related profession, or
• successful in graduate study.

To achieve these objectives, the department prepares students in the application of fundamental computing principles and software development skills. This preparation includes the design and implementation of systems that solve complex problems. Our graduates will be trained in teamwork, effective communication, professionalism, ethics, and the engagement of learning and applying new ideas and technologies as the field evolves.

Computational Data Science Student Outcomes

Graduates of the program will have an ability to:

1. Analyze a complex computing problem and apply principles of computing and other relevant disciplines to identify solutions.
2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
3. Communicate effectively in a variety of professional contexts.
4. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
5. Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
6. Apply computer science theory and software development fundamentals to produce computing-based solutions. [CS]

More Info

What Does a Computational Data Scientist Do?

It is the job of the data scientist to bring order to the chaos on terabytes of data. A data scientist extracts meaning from data and searches for hidden models, trains intelligent systems, creates visualizations, identifies patterns and trends, and discovers solutions and opportunities. The Computational Data Science program prepares students for careers applying and developing tools that support the data needs of tomorrow, from the intelligence in self-driving vehicles to business intelligence systems to opportunities not yet foreseen. 

Common tasks that a Computational Data Scientist will perform include:

• Collecting and categorizing large datasets
• Cleaning and validating data to ensure accuracy, completeness, and uniformity
• Identifying patterns and trends
• Devising models and algorithms to uncover hidden meaning
• Forecasting future trends and results
• Training intelligent systems
• Producing summarizations and visualizations of datasets
• Communicating results to stakeholders

Is There a High Demand for Computational Data Scientists?

The opportunities for Data Scientists are vast and rapidly expanding. Starting salaries reflect the current shortage of qualified data professionals. A diverse range of businesses are seeking qualified graduates including:

• Online companies such as Google, Microsoft, Amazon, Twitter, and LinkedIn
• Computer technology companies such as IBM, Apple, and Oracle
• Communication companies such as Verizon and T-Mobile
• Mobile app developers such as Spotify, Shutterfly and Uber
• Service businesses ranging from insurance to finance such as SAP, Bank of America, and Deloitte
• Data science companies such as SAS, Palantir, Pixar, and Teradata
• Product designers and manufacturers such as Intel, General Motors, Nike, Boeing