MS Data Science | Computational

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.
  • IT 403
  • CSC 412
  • CSC 401

Foundation Courses

  • DSC 450
  • DSC 423
  • DSC 430
  • DSC 441

Students must take 1 course in applied analytics chosen among:

  • DSC 424
  • DSC 465

Advanced Courses

  • DSC 478
  • CSC 555
  • DSC 540

Students must take 1 course among the following:

  • CSC 521
  • CSC 575
  • CSC 578

Elective Courses

Students must take 8 credit hours from graduate level elective courses in the areas of statistical modeling, data mining or database technologies. Students must choose electives from the following list of courses:

  • CMNS 549
  • CSC 452
  • CSC 468
  • CSC 480
  • CSC 481
  • CSC 482
  • CSC 521
  • CSC 528
  • CSC 543
  • CSC 555
  • CSC 575
  • CSC 576
  • CSC 577
  • CSC 578
  • CSC 580
  • CSC 583
  • CSC 594
  • CSC 598
  • DSC 425
  • DSC 433
  • DSC 465
  • DSC 478
  • DSC 480
  • DSC 484
  • DSC 510
  • DSC 540
  • GEO 441
  • GEO 442
  • GPH 565
  • HCI 512
  • IPD 451
  • IS 478
  • IS 549
  • IS 550
  • IS 574
  • MGT 559
  • MGT 798*
  • MKT 555
  • MKT 530
  • MKT 534
  • MKT 595
  • MKT 798

*Course is not currently offered online.

NOTE: Student must take only sections of MGT 798 and MKT 798 with the topic specified above.

Capstone Options

Students have the option of completing a real world Data Analytics Project, or completing the Data Science capstone course, or participating in an 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 ( Student 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 695 course.          

Data Science Capstone course

DSC 672 (formerly CSC672) 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 capstone requirement. These are the steps: 1) Secure an internship with focus in analytics. 2) International Students must obtain the appropriate practical training form and meet with an advisor in the CDM Academic Center for approval. ISS Forms 3) Login to MyCDM and click the “Internships” link on the left to start the course enrollment process.          

Master's Thesis

A student who is working on a research project and has made an original contribution to their area of study may choose to complete a Master's Thesis. Additional information and requirements for School of Computing students pursuing the thesis option can be found on the SoC Master's Thesis Guideline page.          

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.