Undergraduate Degree - Data Science

Program:
Data Science
Program Code:
7154 Data Science
Program Level:
Undergraduate
Award Type:
Bachelor of Science
Start Term:
FS19
College:
Department:


Excerpt from the official Academic Programs Catalog:

Listed below are the approved requirements for the program from the official Academic Programs Catalog.
Students must consult their advisors to learn which specific requirements apply to their degree programs.
Following this section are the requirements as represented in Degree Navigator, MSU's online advising tool. The Degree Navigator section is only available for programs using Degree Navigator for degree certification.

College of Natural Science

Department of Computational Mathematics, Science and Engineering

Undergraduate Programs
Data Science

The Bachelor of Science degree in Data Science is designed to provide students with a strong background in data science using a broad range of computational techniques, practice in statistical thinking, as well as in-depth exposure to topics in data science.

Requirements for the Bachelor of Science Degree in Data Science
  1. The University requirements for bachelor's degrees as described in the Undergraduate Education section of this catalog; 120 credits, including general elective credits, are required for the Bachelor of Science degree in Data Science.
    The University's Tier II writing requirement for the Data Science major is met by completing Computational Mathematics, Science and Engineering 495, referenced in item 3. below.
  2. The requirements of the College of Natural Science for the Bachelor of Science degree.
    1. The following requirements for the major:
      a. One course from each of the following groups (8 or 10 credits):
      (1) CEM 141 General Chemistry 4
      CEM 151 General and Descriptive Chemistry 4
      CEM 181H Honors Chemistry I 4
      LB 171 Principles of Chemistry I 4
      (2) CEM 142 General and Inorganic Chemistry 3
      CEM 152 Principles of Chemistry 3
      CEM 182H Honors Chemistry II 4
      LB 172 Principles of Chemistry II 3
      (3) CEM 161 Chemistry Laboratory I 1
      CEM 185H Honors Chemistry Laboratory I 2
      LB 171L Introductory Chemistry Laboratory I 1
      b. One course from each of the following groups (8 to 10 credits):
      (1) LB 273 Physics I 4
      PHY 173 Studio Physics for Scientists and Engineers I 5
      PHY 183 Physics for Scientists and Engineers I 4
      (2) LB 274 Physics II 4
      PHY 174 Studio Physics for Scientists and Engineers II 5
      PHY 184 Physics for Scientists and Engineers II 4
      c. One course from each of the following groups (14 or 15 credits):
      (1) LB 118 Calculus I 4
      MTH 132 Calculus I 3
      MTH 152H Honors Calculus I 3
      (2) LB 119 Calculus II 4
      MTH 133 Calculus II 4
      MTH 153H Honors Calculus II 4
      (3) LB 220 Calculus III 4
      MTH 234 Multivariable Calculus 4
      MTH 254H Honors Multivariable Calculus 4
      (4) MTH 314 Matrix Algebra with Computational Applications 3
      d. One of the following groups (4 or 6 credits):
      (1) STT 380 Probability and Statistics for Data Science 4
      (2) STT 441 Probability and Statistics I: Probability 3
      STT 442 Probability and Statistics I: Statistics 3
      e. All of the following courses (31 credits):
      CMSE 201 Introduction to Computational Modeling and Data Analysis 4
      CMSE 202 Computational Modeling Tools and Techniques 4
      CMSE 381 Fundamentals of Data Science Methods 4
      CMSE 382 Optimization Methods in Data Science 4
      CMSE 495 Experiential Learning in Data Science 4
      CSE 232 Introduction to Programming II 4
      CSE 331 Algorithms and Data Structures 3
      STT 180 Introduction to Data Science 4
      f. A minimum of 12 credits of approved 400-level courses or above. The following courses are eligible to fulfill this requirement. Other may be substituted with advisor approval.
      CMSE 401 Methods for Parallel Computing 4
      CMSE 402 Data Visualization Principles and Techniques 3
      CMSE 410 Computational Biology and Bioinformatics 3
      CMSE 411 Computational Medicine 3
      CMSE 492 Special Topics in Data Science 1 to 4
      CSE 402 Biometrics and Pattern Recognition 3
      CSE 404 Introduction to Machine Learning 3
      CSE 440 Introduction to Artificial Intelligence 3
      CSE 480 Database Systems 3
      CSE 482 Big Data Analysis 3
      MTH 468 Predictive Analytics 3
      STT 464 Statistics for Biologists 3
      STT 465 Bayesian Statistical Methods 3
      A maximum of 12 credits may count towards the degree for enrollments in CMSE 492 with advisor approval.