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 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-505	Programming 1: Python	3
DSI-506	Programming 1: R	3

Select one of the following

DSI-507	Programming 2: Python	3
DSI-508	Programming 2: R	3

Required

DSI-530

SQL - Introduction to Database Queries

3

Select one of the following

DSI-601	Predictive Analytics 1 - Machine Learning Tools - with Python	3
DSI-604	Predictive Analytics 1 - Machine Learning Tools - with R	3

Select one of the following

DSI-602	Predictive Analytics 2 - Neural Nets and Regression - with Python	3
DSI-605	Predictive Analytics 2 - Neural Nets and Regression - with R	3

Select one of the following

DSI-603	Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules - with Python	3
DSI-606	Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules - with R	3

Required

DSI-622	Interactive Data Visualization	3
DSI-700	Applied Predictive Analytics	3

II. Electives (12 Credits)

DSI-509	Natural Language Processing I	3
DSI-510	Forecasting Analytics	3
DSI-511	Introduction to Network Analysis	3
DSI-608	R Programming Intermediate	3
DSI-610	Optimization - Linear Programming	3
DSI-611	Natural Language Processing II	3
DSI-613	Anomaly Detection	3
DSI-614	Customer Analytics in R	3
DSI-621	Integer and Nonlinear Programming and Network Flow	3
DSI-623	Regression Analysis	3
DSI-625	Risk Simulation and Queuing	3
DSI-640	Spatial 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 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-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 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-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.