MAD Lab

Data Science, Master of Science

Computer Science, Mathematics and Science
30 Credits

The Lesley H. and William L. Collins College of Professional Studies
Queens Campus

Overview

Predictive analytics applies powerful statistical and data mining techniques to large data sets in order to generate useful information, identify patterns and trends, and build models to predict future events.   Applications of these techniques are now transforming decision-making throughout business, government, healthcare, and academia.   The demand for professionals knowledgeable in this area is projected to grow rapidly in the coming years.

Graduates of the MS in Data Science program will obtain a variety of skills required to analyze large datasets and to develop modeling solutions to support decision making.  They will also develop a specialization in either marketing analytics or healthcare analytics. The specialization in healthcare analytics builds upon our division’s undergraduate healthcare informatics major which is now in its fourth year.  The program aims to prepare students with the required qualifications to become "data mining analysts/engineers" or "predictive modelers".

Apply Request Info Plan your Visit

Admission

Application Deadline

  • August 20, 2019

Admissions Criteria

Admission to the program is contingent upon an assessment of the candidate’s ability to successfully pursue graduate study.  This assessment will be made by examining previous academic performance, letters of recommendation, the applicant’s essay, work experience, and any other evidence that the admissions committee believes to be relevant.

Applicants must submit the following evidence of their ability to pursue graduate study:

  1. A baccalaureate degree from a regionally accredited college or university. Transcripts from each institution attended must be submitted even if a degree was not conferred.
     
  2. A record of scholarly achievement at the undergraduate level.  Applicants are expected to have a 3.0 (based on a 4.0 scale) cumulative undergraduate grade point average, and a 3.0 in their major field of study.  An applicant whose grade point average is below 3.0 may submit an official copy of his/her GRE to support his or her application.
     
  3. Two letters of recommendation from individuals who can comment on the applicant’s academic abilities and potential to succeed in an academically rigorous graduate program.  At least one of these letters must be from an instructor who has taught and evaluated the applicant in an academic setting.
     
  4. Completion of the following undergraduate mathematics course work
    1. Calculus
    2. Probability and Statistics

Contacts

Office of Graduate Admission
Office of Graduate Admission
718-990-1601
[email protected]

Faculty Contact Information

Dr. Christina Schweikert
Program Director
Assistant Professor
718-990-7439
[email protected]

Department Faculty

Please see a list of our Computer Science, Mathematics and Science faculty.

Watch our Video

Courses

 Courses
Data Analysis/ Applied Statistics, 
Required 
(6 credits)

Two of the following:
BUA 602 Business Analytics
BUA 609  Advanced Managerial Statistics
BUA 633  Applied Regression and Forecasting Models

 

Database Design/ Data Warehousing, Required 
(3 credits)
CUS 510  Database System Design and Data Warehousing
Data Mining/ Predictive Modeling,
Required 
(6 credits)
CUS 610  Data Mining and Predictive Modeling I  (pre/co-requisite CUS 510)
CUS 615  Data Mining and Predictive Modeling II (pre-requisite CUS 610)
Electives
(6 credits)
Choose 2 elective courses from:
CUS 620   Introduction to Programming for Analytics
(* essential for students without a programming background)
CUS 625   Computer Visualization Applications(pre/co-requisite CUS 610)
CUS 635   Web Data Mining (pre/co-requisite CUS 610, CUS 620)
CUS 640 Natural Language Processing
CUS 675   Database Programming   (prerequisite CUS 1126; pre/co-requisite CUS 610)
Specialization, Required (6 credits)
Choose either:
Big Data Analytics, Marketing Analytics or Healthcare Analytics
CUS 680   Distributed Big Data Analytics I (pre-requisite CUS 610, CUS 510)
CUS 681   Distributed Big Data Analytics II (pre-requisite CUS 680)
or
MKT 600 Decisions in Marketing Management
MKT 611  Data Analysis in Marketing Research
or
HCI 520  Medical and Health Informatics
HCI 525  Applied Healthcare Analytics  (pre-requisite HCI 520; pre/co-requisite CUS 615)
Capstone Course Required 
(3 credits)
Choose 1 capstone course from
CUS 690  Applied Analytics Project  (pre/co-requisite CUS 615)
CUS 695 Software Implementation Project  (pre-requisite CUS 1126; pre/co-requisite CUS 615)
Total (30 credits)