Data Science Pathway
Organize the Chaos.
The Data Science Pathway offers a blend of theoretical and practical knowledge of Statistics and Computer science, with the goal of preparing students for exciting data-oriented career opportunities in a variety of industries.
Pathway Information
Students will build skills in statistical analysis and software development by carrying out representative workflows of 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.
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:
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 [New]
- prereq: CS 3270
- pre/co-requisites MATH 4203
Courses in red are required for the Data Science Certificate
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
Electives:
- CS 3152 Data Structures and Discrete Math II
- CS 3211 Software Engineering I
- CS 3230 Information Management
- CS 4225 Distributed and Cloud Computing
Contact
Contact Us
Dr. Andy Walter
Director, Center of Interdisciplinary Studies
(678) 839-4070
awalter@westga.edu