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Master of Science in Data Science and Analytics

Program Overview

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Thomas Edison State University (TESU) has partnered with The Institute of Statistics Education at Statistics.com to offer a Master of Science (M.S.) degree in Data Science and Analytics 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 M.S. degree 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-5050Programming 1: Python

3

DSI-5060Programming 1: R

3

Select one of the following

DSI-5070Programming 2: Python

3

DSI-5080Programming 2: R

3

Required

DSI-5300SQL - Introduction to Database Queries

3

Select one of the following

DSI-6010Predictive Analytics 1 - Machine Learning Tools - with Python

3

DSI-6040Predictive Analytics 1 - Machine Learning Tools - with R

3

Select one of the following

DSI-6020Predictive Analytics 2 - Neural Nets and Regression - with Python

3

DSI-6050Predictive Analytics 2 - Neural Nets and Regression - with R

3

Select one of the following

DSI-6030Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules - with Python

3

DSI-6060Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules - with R

3

Required

DSI-6220Interactive Data Visualization

3

DSI-7000Applied Predictive Analytics

3

II. Electives (12 Credits) Select four from the following:

DSI-5090Natural Language Processing I

3

DSI-5100Forecasting Analytics

3

DSI-5110Introduction to Network Analysis

3

DSI-6080R Programming Intermediate

3

DSI-6100Optimization - Linear Programming

3

DSI-6110Natural Language Processing II

3

DSI-6130Anomaly Detection

3

DSI-6140Customer Analytics in R

3

DSI-6210Integer and Nonlinear Programming and Network Flow

3

DSI-6250Risk Simulation and Queuing

3

DSI-6400Spatial Statistics with Geographic Information Systems

3

Total Credit Hours: 36

Program Timeline

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

Sequence Course Description Advisement
1 DSI-5050 or DSI-5060 Programming I None 
2 DSI-5070 or DSI-5080 Programming II  Programming I 
3 DSI-5300  Database Queries None  
4 DSI-6010 or DSI-6040 Predictive Analytics I Programming I and II
5  DSI-6020 or DSI-6050 Predictive Analytics II Predictive Analytics I 
6 DSI-6030 or DSI-6060 Predictive Analytics III
 Predictive Analytics II
7 DSI-6220  Data Visualization  None  
8 DSI-7000 Capstone  All above courses

 

Electives 

Some electives in this program are sequential and carry an advisory that students complete specific course work 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-5090: Natural Language Processing I Core  Python 
DSI-6110: Natural Language Processing II None  Either 
DSI-6130: Anomaly Detection Core   Either  
DSI-5100: Forecasting Analytics  None  R 
DSI-6140: Customer Analytics in R  DSI-5060  R 
DSI-6400: Spatial Statistics   None  R 
DSI-5110: Network Analysis   None  Either  
DSI-6080: R Programming Intermediate DSI-5060 and DSI-5080 R 
DSI-6100: Optimization - Linear   None
 Either  
DSI-6250: Risk Simulation and Q  None  Either  
DSI-6210: Integer and Nonlinear Programming DSI-6100  Either  

 

Learning Outcomes

Graduates of the Master of Science degree in Data Science and Analytics 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.