Official Certificate Program in Data Science

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:

  • 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, 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, and other current issues.

Program Prerequisites:

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 Requirements

Required Courses

DATA 511Introduction to Data Science

4

DATA 512Predictive Analytics: Estimation and Clustering

4

DATA 513Predictive Analytics: Classification

4

Total Credit Hours:12

Choose two electives from:

DATA 514Multivariate Statistics

4

DATA 521Introduction to Bioinformatics

4

DATA 522Mining Gene and Protein Expression Data

4

DATA 525Biomarker Discovery

4

DATA 531Text Analytics with Information Retrieval

4

DATA 532Text Analytics with Natural Language Processing

4

DATA 541Advanced Estimation Methods

4

DATA 542Advanced Clustering Methods

4

DATA 543Advanced Classification Methods

4

DATA 551Predictive Modeling for Insurance Data

4

DATA 565Web Data Science

4

CS 508Distributed Computing

3

CS 570Topics in Artificial Intelligence

3

CS 580Topics in Database Systems and Applications

3

Total Credit Hours:8

Other graduate-level data mining or statistics course(s) may be selected, with approval of program coordinator.

Total Credit Hours: 20

More information can be found at: http://web.ccsu.edu/datamining/