Statistics and Data Science Major & Courses

About the major

The Bachelor of Science in Statistics and Data Science is the college’s newest major. The curriculum is designed to equip students to execute all stages of a data analysis, from data acquisition and exploration to application of statistics and machine learning methods to the creation of data products (e.g., reports, apps and dashboards).  

Throughout the program, students are exposed to the principles of and tools for conducting reproducible data science and are taught to think critically about relevant ethical and legal issues (e.g., data privacy, algorithmic bias and misrepresentation of findings). The program prepares students to enter the workforce directly, or after pursuing specialized graduate training, as statisticians and data scientists or in other roles where training in these fields is excellent preparation.

Learn more about undergraduate studies in SDS


Courses that Statistics and Data Science Majors Take

Get a sense for some of the courses that many of our majors take below. A more complete list for each catalog may be found when you search for your degree option by the year of entry or catalog.

Entry-Level Requirements

Natural Sciences students declare a degree and a major after receiving passing grades in key courses. For this major, these are typically:

  • Math: Differential and Integral Calculus (M 408C)/Sequence, Series and Multivariable Calculus (M 408D)
  • Computer Science: Elements of Computers and Programming (CS 303E) 
  • Statistics and Data Sciences: Introduction to Data Science (SDS 313) and Statistical Thinking (SDS 315)
Breadth Requirement

SDS majors all take at least 12 hours, including at least six upper-division hours, in a single field of study other than SDS from elsewhere at the university.

Examples of Courses

Students take additional courses in SDS and other disciplines, such as:

  • Probability and Statistical Inference
  • Intermediate and Advanced Statistical Methods
  • Practical Machine Learning Statistics  
  • Case Studies in Data Science
  • Elements of Databases 
  • Differential/Integral/Multivariable Calculus
  • Matrices and Matrix Calculations or Linear Algebra and Matrix Theory