Data Science

MASTER OF SCIENCE IN 
DATA SCIENCE

Purdue’s innovative Master of Science in Data Science (MSDS) is an accessible, skills-focused master’s designed to meet the needs of professionals who have some background in data science and want to accelerate their expertise. This technical degree exposes students to coursework in in-demand areas like data visualization, machine learning, data mining, data analysis, communication and more. Applicable to many different fields and career paths, this master’s empowers professionals to harness the power of data and push their careers to new heights. 

Ready to dive into the world of Data Science?

Data Science

Build In-Demand Data Skills Through Hands-On Learning

  • Build technical skills in data science by completing hands-on projects with real-world applications. 
  • Gain expertise in in-demand areas like machine learning, data visualization, data mining, project management and more. 
  • Prepare yourself for a career in data science or learn to incorporate data skills into your current role.  
  •  Develop an understanding of where the data field is heading and master state-of-the-art trends and techniques. 

Career Outlook

Get your career started by working in one of the following roles: Data Scientist, Software Developer, Machine Learning Engineer, Data Analyst, Quantitative Analyst, Data Engineer, and more!  
  
Purdue University’s rigorous online programs allow you to earn a prestigious Purdue degree anytime and from anywhere. These programs give you access to outstanding faculty and top-quality curriculum in a convenient, flexible format to move your career and the world forward.

60+ Industries in Demand
$124K Median Average Salary
36,958 Unique Job Postings
60+ Industries in Demand
$124K Median Average Salary
36,958 Unique Job Postings

Source: LightcastTM (2023). Unique job postings for July 2022-2023. Projected growth for years 2023-2033.

Master of Science in Data Science

Curriculum Overview

The Master of Science in Data Science requires 30 credit hours with 18 required credit hours within the graduate school and 12 credit hours can be taken from selected focus areas or elective hours detailed below.  

18 credit hours are required among the courses below.  

  • Grad 50500 Foundations in Data Science – 3 Credit Hours 
  • Grad 50600 Big Data Tools and Technologies Courses – 3 Credit Hours 
  • Grad 50700 Cross Domain Data Communication and Visualization – 3 Credit hours 
  • Grad 50800 – Data Analytics – 3 Credit Hours 
  • Grad 50900 – Applied Machine Learning: From Foundations to Latest – 3 Credit hours 
  • Grad 58900 – Capstone 

Remaining credit hours can be used for a focus area below if desired. Plese review the focus areas below. 

  • Applied Statistics 
  • IT Business Analysis 
  • Managing Information Technologies 
  • Spatial Data Science 

Technical/Professional Electives (Courses vary between 1-3 Credit Hours) 
** Course list is subject to change  

  • ABE 65100 – Environmental Informatics  
  • AGEC 68700 – Problem Solving and Project Management for Decision Makers  
  • AGRY 54500 – Remote Sensing of Land Resources  
  • ASM 54000 – Geographic Information System Application  
  • CE 59700 – Data Science for Smart Cities   
  • CNIT 57000 – IT Data Analytics  
  • CNIT 58100 – Enterprise Data Management  
  • CNIT 58100 – Information Security Governance 
  • COM 60311 – Seminar in Crisis Communication 
  • CS 50023 – Data Engineering  
  • CS 50025 – Foundations of Decision Making 
  • CS 57700 – Natural Language Processing 
  • ECE 56900 – Introduction to Robotic Systems  
  • ECE 59500 – Computer Vision for Embedded Systems 
  • ECE 59500 – Natural Language Processing  
  • ECON 57700 – Quantitative Economics and Python  
  • EDPS 53100 – Introduction to Measurement and Instrument Design 
  • FNR 58700 – Advanced Spatial Ecology and GIS   
  • ILS 69500 – Computational Text Analysis 
  • MATH 51100 – Linear Algebra with Applications  
  • MGMT 52500 – Marketing Analytics  
  • MGMT 52600 – Project Management  
  • MGMT 56800 – Supply Chain Analytics 
  • MGMT 58600 – Python Programming  
  • MGMT 59000 – R for Analytics  
  • STAT 50100 – Experimental Statistics I   
  • STAT 50600 – Statistical Programming and Data Management  
  • STAT 51200 – Applied Regression Analysis  
  • STAT 51700 – Statistical Inference  
  • STAT 52600 – Advanced Statistical Methodology    
  • STAT 52700 – Intro to Computing for Statistics
  • STAT 58200 – Stat Consulting & Collaboration   

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You are not alone in taking your next giant leap. Get your questions answered, receive application help, or plan your degree journey by speaking with an enrollment counselor. Request more information today.