This is an unofficial description for this program. For official information check the Academic Catalog.

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.

Program Learning Outcomes:

Students in the program will be expected to:

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; 

Evaluate the true consequences of making false positive or false negative decisions. 

Demonstrate proficiency with leading open-source analytics coding software such as R and Python, as well as commercial platforms;

Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms including k-means clustering, Kohonen 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/STAT 201, or STAT 104/STAT 453, or STAT 215/STAT 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 Official Certificate in Data Science, and (3) specify whether and how the stat course prerequisite was met. 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.

Total Credit Hours: 20

Course Requirements

Total Credit Hours: 20-22

More information can be found at:

Admissions Contact

Graduate Recruitment & Admissions

Academic Department

Sally Lesik
Professor&Assistant Chair
Mathematical Sciences
Marcus White Hall

Academic Department