MS in Predictive Analytics

Master of Science 2010-2011

Predictive Analytics

Graduates of the MS in Predictive Analytics program will obtain a variety of skills required for a career in predictive analytics, including the ability to analyze large datasets and to develop modeling solutions to support decision making, a good understanding of the fundamental principles of marketing and customer relationship management, and communication skills to  present results effectively to a non-technical business audience. The program aims to prepare students with the required qualifications to become "data mining analysts/engineers" or "predictive modelers".

Learn more about admission to this program.

Online Learning Options
This degree can be completed entirely online. Some courses in this degree are available for review and playback via the CDM Course Online playback system (COL) . If a course is COL-enabled, any student registered in the course has access to the course playback. Students are strongly encouraged to utilize the COL resource wherever available. Students who wish to complete this degree entirely online need to consult with their faculty advisor to make suitable substitutions for the prerequisite and marketing courses that are not offered online, as noted in the program requirements below. For more information on online learning at CDM visit the Online Learning section. Information on online delivery of Marketing courses can be found on the Kellstadt Online Learning page. ​
Degree Requirements
Students in this degree program must meet the following requirements:
  • Complete a minimum of 52 credit hours (generally 13 courses) beyond the Prerequisite Phase
  • Earn a grade of B- or better in each Prerequisite Phase course
  • Earn a grade of C- or better in all graduate courses beyond the Prerequisite Phase
  • Maintain a graduate level GPA of 2.50 or higher while pursuing their degree
  • Achieve a graduate GPA of 2.50 or higher at the completion of all other requirements

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.

Course Requirements
Prerequisite Phase
The goal of the prerequisite phase is to give students the background necessary for starting the graduate program. These prerequisite phase requirements can be fulfilled in one of three ways:
  • The student takes the course and earns a grade of B- or higher
  • The student takes a Graduate Assessment Exam (GAE) to test out of the course
  • The faculty advisor waives the course because of equivalent academic background or work experience.

All students are blocked from enrolling in Graduate Phase courses prior to completing their prerequisites. Students must submit an online Change of Status request (through myCDM) when the Prerequisite Phase is completed to inform the Student Services offices that the block can be removed.

IT 223 Data Analysis
MAT 150 Calculus I *
MAT 151 Calculus II *
MAT 220 Linear Algebra with Applications *
* Courses are not currently offered online. Consult your advisor for determining suitable alternative coursework.
School of Computing Foundation Courses
CSC 451 Database Design
CSC 423 Data Analysis and Regression
CSC 424 Advanced Data Analysis
IS 567 Knowledge Discovery Technologies
CSC 578 Neural Networks and Machine Learning
Marketing Department Foundation Courses
MKT 555 Decisions in Marketing Management
MKT 530 Customer Relationship Management
MKT 534 Analytical Tools for Marketers *
* Course is not currently offered online. Consult your advisor for determining suitable alternative coursework.
School of Computing Advanced electives
Students must choose
  • 1 School of Computing 500-level elective course
  • 2 courses from the following list:
  • Data Mining and Data Analysis
    CSC 425 Time Series Analysis and Forecasting
    ECT 584 Web Data Mining for Business Intelligence
    CSC 495 Social Network Analysis *
    CSC 575 Intelligent Information Retrieval
    CSC 598 Topics in Data Analysis
    CSC 671 Quantitative Computing Workshop
    CSC 521 Monte Carlo Algorithms *
    * these courses are recommended only to students with strong programming background
  • Image Analysis and Visualization
    CSC 481 Introduction to Image Processing
    CSC 482 Applied Image Analysis
    GPH 465 Survey of Visualization Applications
    GPH 565 Designing for Visualization
  • Database Technologies
    CSC 453 Database Technologies *
    CSC 452 Database Programming *
    CSC 543 Spatial Databases and Geographic Information Systems *
    * these courses are recommended only to students with strong programming background
  • Business Intelligence
    IS 574 Business Intelligence
    IS 549 Data Warehousing and Data Mining
    IS 578 Information Technology Consulting
Marketing Department Advanced electives
Student must choose 1 course from the following list:
MKT 529 Precision Marketing
MKT 595 Internet and Interactive Marketing
MKT 798 Special Topics
Project  Course
CSC 695 Master's Independent Study (1 - 4 Credits)
or CSC 697 Graduate Internship