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
Required Courses
Choose two electives from:
DATA 514 Multivariate Analytics 4 Credits
DATA 521 Introduction to Bioinformatics 4 Credits
DATA 522 Mining Gene and Protein Expression Data 4 Credits
DATA 525 Biomarker Discovery 4 Credits
DATA 531 Text Analytics with Information Retrieval 4 Credits
DATA 532 Text Analytics with Natural Language Processing 4 Credits
DATA 541 Advanced Estimation Methods 4 Credits
DATA 542 Advanced Clustering Methods 4 Credits
DATA 543 Advanced Classification Methods 4 Credits
DATA 551 Predictive Modeling for Insurance Data 4 Credits
DATA 565 Web Data Science 4 Credits
CS 508 Distributed Computing 3 Credits
CS 570 Topics in Artificial Intelligence 3 Credits
CS 580 Topics in Database Systems and Applications 3 Credits
Other graduate-level data science or statistics course(s) may be selected, with approval of program coordinator.
Total Credit Hours: 20-22
More information can be found at: http://web.ccsu.edu/datamining/