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Data Analytics Core Curriculum

The core focuses on principles that are fundamental to all areas of data analytics and consists of courses taken by all majors. In these courses, students investigate the computational, mathematical and statistical foundations of data analytics, and develop critical thinking and communication skills.

Degree Planning Resources

These documents outline the prerequisite structure within the core curriculum and demonstrate how students flow through the required courses for the Data Analytics major. They should be used as guides only. Semester course offerings are subject to change.


Since AU 2022 with NEW GE (GEN)


Before AU 2022 with LEGACY GE (GEL)


Core Educational Objectives

A student graduating with a Bachelor of Science degree with a major in Data Analytics will demonstrate:

  • an understanding of and ability to apply computer science principles relating to data representation, retrieval, programming, and analysis.
  • an understanding of and ability to apply mathematical and statistical models and concepts to detect patterns in data, and to draw inferences and conclusions supported by data.
  • critical thinking skills associated with problem identification, problem solving and decision making, assessing value propositions supported by data, and generating a logical synthesis of information from data.
  • the ability to apply knowledge gained from one area to problems and data in another.
  • the ability to communicate findings and their implications, and to apply them effectively in organizational settings.

Mathematical Pre-requisites

The mathematical pre-requisites for the Data Analytics major are: 

CSE Pre-requisites

Core Requirements

All students in the Data Analytics major must complete the following 51 credit hours worth of core requirements.

  • CSE 2221: Software I, Software Components
  • CSE 2231: Software II, Software Development and Design
  • CSE 2321: Foundations I, Discrete Structures
  • Choose one of the following
    • CSE 2421: Systems I: Introduction to Low-Level Programming and Computer Organization
    • CSE 3430: Overview of Computer Systems for Non-Majors
  • Math 2568: Linear Algebra
  • CSE 3241: Introduction to Database Systems
  • Stat 3201: Introduction to Probability for Data Analytics
  • Stat 3202: Introduction to Statistical Inference for Data Analytics
  • Stat 3301: Statistical Modeling for Discovery I
  • Stat 3302: Statistical Modeling for Discovery II
  • Stat 3303: Bayesian Analysis and Statistical Decision Making
  • ISE 3230: Systems Modeling and Optimization for Analytics
  • Stat 4620: Introduction to Statistical Learning
  • Choose one of the following
    • CSE 5242: Advanced Database Management Systems
    • CSE 3244: Data Management in the Cloud
  • CSE 5243: Introduction to Data Mining
  • Visualization: choose one of the following
    • CSE 5544: Introduction to Scientific Visualization
    • ISE 5760: Visual Analytics and Sensemaking

[pdf] - Some links on this page are to Adobe .pdf files. If you need these files in a more accessible format, please contact data-analytics@osu.edu.