20-21 Graduate Catalog

Contents
Catalog Links

Master of Science in Data Science and Analytics

Program Overview

Return to the Heavin School of Arts and Sciences Homepage

Thomas Edison State University (TESU) has partnered with The Institute of Statistics Education at Statistics.com to offer a Master of Science in Data Science and Analytics (MSDSA) degree program that is delivered completely online and is structured around the unique needs of working adults.

The program will provide students with graduate-level theoretical knowledge, applied skills and the ability to derive value from data in real world decision making. Data science is an emerging interdisciplinary field that incorporates computer science, statistics, and mathematical modeling with applications in business, government, the life sciences and social sciences. The rapid emergence of related disciplines provides a unique opportunity for students to be part of a data science transformation over the next decade.

Download our Degree Program Profile Sheet for an overview of our MS in Data Science and Analytics program.

Note: This degree program, including all required course work, is not eligible for federal financial aid.

Credit Distribution

I. Core Courses (24 Credits)

Select one of the following

DSI-505Programming 1: Python

3

-
Or
DSI-506Programming 1: R

3

Select one of the following

DSI-507Programming 2: Python

3

-
Or
DSI-508Programming 2: R

3

Required

DSI-530SQL - Introduction to Database Queries

3

Select one of the following

DSI-601Predictive Analytics 1 - Machine Learning Tools - with Python

3

-
Or
DSI-604Predictive Analytics 1 - Machine Learning Tools - with R

3

Select one of the following

DSI-602Predictive Analytics 2 - Neural Nets and Regression - with Python

3

-
Or
DSI-605Predictive Analytics 2 - Neural Nets and Regression - with R

3

Select one of the following

DSI-603Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules - with Python

3

-
Or
DSI-606Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules - with R

3

Required

DSI-622Interactive Data Visualization

3

DSI-700Applied Predictive Analytics

3

II. Electives (12 Credits)

DSI-509Natural Language Processing I

3

DSI-510Forecasting Analytics

3

DSI-511Introduction to Network Analysis

3

DSI-608R Programming Intermediate

3

DSI-610Optimization - Linear Programming

3

DSI-611Natural Language Processing II

3

DSI-613Anomaly Detection

3

DSI-614Customer Analytics in R

3

DSI-621Integer and Nonlinear Programming and Network Flow

3

DSI-623Regression Analysis

3

DSI-625Risk Simulation and Queuing

3

DSI-640Spatial Statistics with Geographic Information Systems

3

Total Credit Hours: 36

Program Timeline

The MS in Data Science and Analytics program follows a curricular structure that is sequential. Much of the coursework in this program builds upon knowledge gained in previous courses in the sequence. Students are advised to enroll in required coursework following the below sequence.

Sequence Course Description Advisement
1 DSI-505 or DSI-506 Programming I None 
2 DSI-507 or DSI-508 Programming II  Programming I 
3 DSI-530  Database Queries None  
4 DSI-601 or DSI-604 Predictive Analytics I Programming I and II
5  DSI-602 or DSI-605 Predictive Analytics II Predictive Analytics I 
6 DSI-603 or DSI-606 Predictive Analytics III
 Predictive Analytics II
7 DSI-622  Data Visualization  None  
8 DSI-700 Capstone  All above courses

 

Electives 

Some electives in this program are sequential and carry an advisory that students complete specific coursework prior to enrolling. Some electives in this program are specific to the track a student chooses (R or Python). The “core” refers to the required programming and predictive analytics courses in the above sequence.

Course  Advisement  Track
DSI-509: Natural Language Processing I Core  Python 
DSI-611: Natural Language Processing II None  Either 
DSI-613: Anomaly Detection Core   Either  
DSI-510: Forecasting Analytics  None  R 
DSI-614: Customer Analytics in R  DSI-506  R 
DSI-640: Spatial Statistics   None  R 
DSI-511: Network Analysis   None  Either  
DSI-608: R Programming Intermediate DSI-506 and DSI-508 R 
DSI-610: Optimization - Linear   None
 Either  
DSI-625: Risk Simulation and Q  None  Either  
DSI-621: Integer and Nonlinear Programming DSI-610  Either  

 

Learning Outcomes

Graduates of the Master of Science in Data Science and Analytics degree program will be able to:

  • identify appropriate statistical and machine learning methods to gain value from data, especially Big Data, in different business and organizational contexts;
  • use software or programming languages to develop statistical and machine learning models to gain insight from data and make predictions;
  • interpret the results of statistical and machine learning models;
  • apply software or programming languages to explore relationships in data, and prepare data for analysis; and
  • specify the decisions (including automated decisions) that should result from the analytic methods.