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Data Science Pathway

This pathway offers a blend of Statistics and Computer science. Students will build skills in statistical analysis and software development, data exploration, visualization, modeling, and model evaluation and interpretation to solve real-world problems. Students will be exposed to contemporary programming languages and cloud-based technologies that enhance data science and machine learning capabilities.

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Requirements

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Degree Requirements

IDS MAJORS

All IDS majors complete an XIDS course sequence through which they learn interdisciplinary concepts and method, culminating with a capstone project that reflects their intellectual and career interests:

  • XIDS 2000 - Introduction to Interdisciplinary Studies
  • XIDS 3000 - Interdisciplinary Methods
  • XIDS 4000 - Interdisciplinary Capstone

Pathway requirements

Pathway Requirements

Courses in red are required for the Data Science Certificate

Discipline 1 - Mathematics

Foundational 1000/2000-level course (counted in area F):

  • Math 2853 (3 credits)
  • Math 2644 (4 credits)

Major Foundation Courses (6 credits):

  • Math 3003 Transition to Advanced Math
  • Math 4203 Mathematical Probability (prereq: Math 2644)

Major Required Courses (12 credits):

  • Math 4213 Mathematical Statistics (prereq: Math 4203)
  • Math 4803 Analysis of Variance (prereq: Math 4203)
  • Math 4813 Regression Analysis (prereq: Math 4203)
  • Math 4483 Graph Theory (prereq: Math 3003)

Discipline 2 - Computer Science

Foundational 1000/2000-level course (counted in area F):

  • CS 1301 Computer Science I (4 credits) [prereq: Math 1113 (>=C) OR Math 1112 (>= C)]
  • CS 1300 Intro to CS in Python (4 credits) [no prereqs]

Major Foundation Courses (4 credits):

  • CS 1302 Computer Science II (4 credits) [prereq: CS 1301, >= B]

Major Required Courses (13 credits):

  • CS 3270 Intelligent Systems [prereq: CS 1302 (>= B)]
  • CS 3280 Systems Programming [prereq: CS 1302 (>= B)]
  • CS 3151 Data Structures and Discrete Math I [prereq: CS 1302 (>= B)]
  • CS 4725 Foundations of Machine Learning [prereq: CS 3270 & 
    pre/co-requisites MATH 4203]

Suggested Courses

19 credits from other courses (including minors and electives, etc.), but must have at least 9 credits from 3000/4000 levels. Here are some suggestions.

Electives:

  • Math 4013 Numerical Analysis
  • Math 4823 Applied Experimental Design
  • Math 4833 Applied Nonparametric Statistics
  • Math 4843 Introduction to Sampling
  • CS 3152 Data Structures and Discrete Math II 
  • CS 3211 Software Engineering I
  • CS 3230 Information Management
  • CS 4225 Distributed and Cloud Computing

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