Data Science M.S.

Program Rationale:

This program is designed for the person who loves data and wants to learn how to uncover actionable results from large data sets using a data scientific framework. Starting with the first course, students will learn data science by applying it on real-world, large data sets, gaining expertise in state-of-the-art data modeling methodologies, so as to prepare them for information-age careers in data science, analytics, data mining, statistics, and actuarial science.

There are five tracks in this program. Four of these provide specialized skills, and the fifth allows a student to sample a variety of data science and computational techniques.

Program Learning Outcomes:

Students in the program will be expected to:

  • Approach data analysis using a scientific approach, that is, through a systematic process that avoids expensive mistakes by assessing and accounting for the true costs of making various errors.
  • Apply data science using a systematic process, by implementing an adaptive, iterative, and phased framework to the process, including the research understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase; 
  • Demonstrate proficiency with leading open-source analytics coding software such as R and Python, as well as commercial platforms, such as IBM/SPSS Modeler;
  • Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms including k-means clustering, classification and regression trees, logistic regression, k-nearest neighbor, multiple regression, and neural networks; and
  • Learn more specialized techniques in bioinformatics, text analytics, algorithms, and other current issues.

 

Admission Requirements:

Students must (1) hold a Bachelor's degree from a regionally accredited institution of higher education, and (2) have a grade of B or better in two applied statistics courses (such as CCSU's STAT 200/201, or STAT 104/453, or STAT 215/216).

A minimum undergraduate GPA of 3.00 on a 4.00 scale (where A is 4.00), or its equivalent, and good standing (3.00 GPA) in all post-baccalaureate course work is required. Conditional admission may be granted to candidates with undergraduate GPAs as low as 2.40. 

In addition to the materials required by the School of Graduate Studies, the following are required:

  • A formal application essay of 500-1000 words that focuses on (1) academic and work history, and (2) reasons for pursuing the Master of Science in Data Science. The essay will also be used to demonstrate a command of the English language.
  • One letter of recommendation, either from the academic or work environment.

The application and all transcripts should be sent to the Graduate Admissions Office.  

Instructions for uploading the essay and submitting the recommendation letters will be found within the graduate online application. 

Course and Capstone Requirements

Core Courses

The following five courses are required of all students.

DATA 511Introduction to Data Science

4

DATA 512Predictive Analytics: Estimation and Clustering

4

DATA 513Predictive Analytics: Classification

4

DATA 514Multivariate Statistics

4

DATA 599Special Project (Plan C)

3

Total Credit Hours:19

Bioinformatics Track

For students selecting the bioinformatics track, the following three classes are required.

DATA 521Introduction to Bioinformatics

4

DATA 522Mining Gene and Protein Expression Data

4

DATA 525Biomarker Discovery

4

Other appropriate graduate courses, with permission of advisor.

Text Analytics Track

For students selecting the text analytics track, there are two required classes and one elective. The latter can be any non-core, 500-level DATA course.

DATA 531Text Analytics with Information Retrieval

4

DATA 532Text Analytics with Natural Language Processing

4

Other appropriate graduate courses, with the permission of the advisor.

Advanced Methods Track

For students selecting the advanced methods track, the following three classes are required.

DATA 541Advanced Estimation Methods

4

DATA 542Advanced Clustering Methods

4

DATA 543Advanced Classification Methods

4

Other appropriate graduate courses, with the permission of the advisor.

Computational Track

CS 508Distributed Computing

3

CS 570Topics in Artificial Intelligence

3

CS 580Topics in Database Systems and Applications

3

and either

CS 463Algorithms

3

or

CS 525Advanced Algorithms

3

Other appropriate CS graduate courses, with the permission of the advisor.

General Data Science Track

Excluding the common core, a total of at least twelve credits of courses from the other tracks and/or electives.

Elective Courses

DATA 551Predictive Modeling for Insurance Data

4

DATA 565Web Data Science

4

STAT 534Applied Categorical Data Analysis

3

Total Credit Hours: 31