Master of Science Data Science and Analytics with a Concentration in Engineering and Big Data Analysis (MS)

Sampath Jayarathna, Graduate Program Director
Krishnanand Kaipa, Engineering & Big Data Analytics Concentration Coordinator. 

Engineering & Big Data Analytics Concentration

The purpose of this concentration is to provide students with a thorough understanding of the methods and technologies to handle big data and to instill engineering problem-solving skills rooted in big data solutions. It will further prepare them to become professionals trained in advanced data analytics, with the ability to transform large streams of multiple data sources into understandable and actionable information for the purpose of making decisions. The coursework (12 credits) will enable students to learn and practice the following competencies: data collection, data storage, processing and analyzing data, reporting statistics and patterns, drawing conclusions and insights and making actionable recommendations.

Admission

The requirements for admission to the Master of Science in Data Science and Analytics are as follows:

1. A baccalaureate degree in computer science, electrical and/or computer engineering, mathematics, statistics, information system & technology, or a related field from a regionally-accredited institution or an equivalent institution outside the U.S.; students holding a bachelor's degree in an unrelated field will need competency in topics related to basic statistics and computer science.
2. GRE scores with a 50% or better attainment on quantitative reasoning (or waiver)
3. Current scores on the Test of English as a Foreign Language (TOEFL) of at least 230 on the computer-based TOEFL or 79 on the TOEFL iBT, or IELTS 6.5 overall.

Students with previously completed work at a regionally-accredited institution may submit a request for a maximum of 12 elective graduate credit hours to be transferred into the program. If approved by the admission committee, it will be added to the transcript.

Curriculum Requirements

The program requires 30 credit hours. The curriculum includes two concentrations: computational data analytics and, business intelligence and analytics. A capstone project is required.

Data Science & Analytics Core

Course List

Code	Title	Credit Hours
Core Requirements
DASC/CS 620	Introduction to Data Science and Analytics	3
CS 624	Data Analytics and Big Data	3
CS 625	Data Visualization	3
STAT 603	Statistical/Probability Models for Data Science and Analytics	3
STAT 604	Statistical Tools for Data Science and Analytics	3
Total Credit Hours for Concentration		12
Capstone Course		3
Total Credit Hours		30

Engineering & Big Data Analytics Concentration

Course List

Code	Title	Credit Hours
Select two of the following:		6
ENMA 754	Big Data Fundamentals
MSIM 715	High Performance Computing and Simulations
ECE 607	Machine Learning I
Select two of the following:		6
ECE 784	Computer Vision
MSIM 695	Topics in Modeling and Simulation
MSIM 574	Transportation Data Analytics
MAE 740	Autonomous and Robotic Systems Analysis and Control
CEE 722	Cluster Parallel Computing
ECE 651	Statistical Analysis and Simulation
ECE 780	Machine Learning II
Total Credit Hours		12