MS-In-Data-Science-Health-Care

Master of Science 2024 through2025

Data Science

About the Program Untitled 1

Health Care Concentration

Displaying Spring 2024-2025 Course Availability
  • In-Class
  • In-Class and Online
  • Online Only
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Curriculum Requirements

Introductory Courses

No Introductory Course may be substituted for any other course at any level.

Introductory courses may be waived for any of the following conditions based on faculty review:

  • The student has the appropriate course work to satisfy an Introductory Course based on an official transcript review by faculty and successful grades, typically B or better.
  • The student has appropriate and verified professional experience to satisfy an Introductory Course which is demonstrated through successful completion of a GAE exam.
  • If a Graduate Assessment Examination (GAE) is available for the Introductory Courses, upon successfully completion of a GAE, a waiver will be issued.
  • Plan accordingly prior to start of the term, faculty reviews for possible course waivers can take a few weeks. For newly admitted students, possible course waivers will not be initiated until an Intent to Enroll form has been submitted.
  • CSC 401 Introduction to Programming
  • CSC 412 Tools and Techniques for Computational Analysis
  • IT 403 Statistics and Data Analysis

Foundation Courses

  • DSC 423 Data Analysis and Regression
  • DSC 430 Python Programming
  • DSC 441 Fundamentals of Data Science
  • DSC 450 Database Processing for Large-Scale Analytics

Select one of the following applied analytics courses:

  • DSC 424 Advanced Modeling and Analysis Techniques
  • DSC 465 Data Visualization

Advanced Courses

  • DSC 510 Health Data Science
  • DSC 540 Advanced Machine Learning
  • HIT 430 Electronic Health Records
  • MGT 559 Health Sector Management
    OR HIT 421 Introduction to Health Informatics

Some advanced courses may not be offered online. Student may consult with their faculty advisor to determine suitable course substitutions.

Elective Courses

Students must take eight (8) Credit Hours of graduate level elective courses in the areas of statistical modeling, data mining or database technologies. Elective courses must be selected from the following list:

  • CMNS 549 Special Topics in Organizational Communication
  • CSC 452 Database Programming
  • CSC 468 Programming Interactive Data Visualization for the Web
  • CSC 481 Introduction to Image Processing
  • CSC 482 Applied Image Analysis
  • CSC 484 Ethics in Artificial Intelligence
  • CSC 521 Monte Carlo Algorithms
  • CSC 528 Computer Vision
  • CSC 543 Spatial Databases and Geographic Information Systems
  • CSC 555 Mining Big Data
  • CSC 575 Intelligent Information Retrieval
  • CSC 576 Computational Advertising
  • CSC 578 Neural Networks and Deep Learning
  • CSC 580 Artificial Intelligence II
  • CSC 583 Natural Language Processing
  • CSC 594 Topics in Artificial Intelligence
  • CSC 598 Topics in Data Analysis
  • DSC 425 Time Series Analysis and Forecasting
  • DSC 433 Scripting for Data Analysis
  • DSC 540 Advanced Machine Learning
  • DSC 465 Data Visualization
  • DSC 478 Programming Machine Learning Applications
  • DSC 480 Social Network Analysis
  • DSC 484 Web Data Mining
  • GEO 441 Geographic Information Systems (Gis) for Community Development
  • GEO 442 Geographical Information Systems (Gis) for Sustainable Urban Development
  • GPH 565 Designing for Visualization
  • HCI 512 Information Visualization and Infographics
  • IPD 451 Big Data and NoSQL Program
  • IS 478 Information Technology Consulting
  • IS 549 Data Warehousing
  • IS 550 Enterprise Data Management
  • IS 574 Business Intelligence and Analytics Systems

Capstone Options

Four (4) Credit Hours are required for the capstone requirement. Students have the option of completing a real world Data Analytics Project, or completing the Data Science Capstone course, or participating in a Data Analytics Internship or completing a Master's Thesis to fulfill their Capstone requirement.

Data Analytics Project

The real data analytics project is for students who are interested in working in a small team on a research project under the supervision of a CDM faculty. A list of available projects is published on the dampa center website (http:// dampa.cdm.depaul.edu). Students who are interested in proposing their own data analytics project are encouraged to contact a CDM faculty member teaching analytics courses as soon as possible. Students must enroll in CSC 695 for a total of 4 credit hours taken in two consecutive quarters (2 credit hours for 2 quarters) to satisfy the capstone requirement. The faculty who supervises the project will initiate enrollment in the CSC 695course.   

Data Science Capstone course

DSC 672 course offers the opportunity of working on an analytics project in a more structured class format. Students enrolled in the courses will be working in teams on a data analytics project under the supervision of the course instructor.   

Analytics Internship

An internship offers students the opportunity to integrate their academic experience with on-the-job training in an analytics related field. Students must enroll in CSC 697 for 4 credit hours to satisfy the practicum requirement. These are the steps:   

  • Secure an internship with focus in analytics.
  • International Students must obtain the appropriate practical training form and meet with an advisor in the CDM Academic Center for approval (https://offices.depaul.edu/global-engagement/student-resources/student-services/Pages/ Forms.aspx).
  • Login to MyCDM and click the “MyInternships” link on the left to start the course enrollment process.
Master's Thesis

A student who has made an original contribution to the area (typically, through work done by CSC 695) may choose to complete a Master's Thesis. The student and the student's research advisor should form a Master's Thesis Committee of 3 faculty. The student will need to submit to the committee a thesis detailing the results of the research project. After a public defense, the committee will decide whether to accept the thesis. In that case, the student will be allowed to register for the 0 credit hour course CSC 698 and the transcript will show the thesis title as the course topic.
   





Degree Requirements

Students in this degree program must meet the following requirements:

  • Complete a minimum of 48 graduate credit hours in addition to any required introductory courses of the designated degree program.
  • Complete all graduate courses and requirements listed in the designated degree program.
  • Earn a grade of C- or better in all courses of the designated program.
  • Maintain a cumulative GPA of 2.5 or higher.
  • Students pursuing a second (or more) graduate degree may not double count or retake any course that applied toward the completion of a prior graduate degree. If a required course in the second degree was already completed and applied toward a previous degree, the student must meet with a faculty advisor to discuss a new course to be completed and substituted in the new degree. This rule also applies to cross-listed courses, which are considered to be the same course but offered under different subjects.
  • Students pursuing a second master's degree must complete a minimum of 48 graduate credit hours beyond their first designated degree program in addition to any required introductory courses in their second designated degree program.

Students with a GPA of 3.9 or higher will graduate with distinction.

For DePaul's policy on repeat graduate courses and a complete list of academic policies see the DePaul Graduate Handbook in the Course Catalog.