MS-In-Predictive-Analytics-Computational

Master of Science Winter & Spring 2012-2013

Predictive Analytics

Computational Methods Concentration

The MS in Predictive Analytics - Computational Methods Concentration provides students with the knowledge and skills necessary to become successful professionals in the field of predictive analytics. The degree prepares students for a career as a data scientist, data miner or predictive modeler, a profession that is in high-demand and that offers many career opportunities. Graduates obtain the technical skills and the statistical and quantitative knowledge needed to manage and extract information from massive amounts of data. Students learn the newest tools and techniques to mine big data, and to help companies make sense of their data. Students gain practical experience in analytics by working on real data analysis projects sponsored by companies or as part of research projects, under the supervision of DePaul faculty. This concentration is recommended for students with programming experience.

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 any courses that are not offered online. 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 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 B- or better in each introductory course of the designated degree program.
  • Earn a grade of C- or better in all courses beyond the introductory courses of the designated degree program.
  • Maintain a cumulative GPA of 2.5 or higher.
  • Students pursuing a second master's degree must complete a minimum of *52 graduate credit hours beyond their first designated degree program in addition to any required introductory courses in their second designated degree program.
    *53 graduate credit hours required for MS Information Systems.

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
Introductory Courses
Introductory courses may be waived for any of the following conditions:
  • The student has the appropriate course work to satisfy an Introductory Course.
  • The student has appropriate and verified professional experience to satisfy an Introductory Course.
  • The student passes a Graduate Assessment Examination (GAE) in the Introductory Course area.
IT 403 Statistics and Data Analysis
CSC 412 Tools and Techniques for Computational Analysis
CSC 401 Introduction to Programming
Foundation Courses
CSC 451 Database Design
CSC 423 Data Analysis and Regression
CSC 424 Advanced Data Analysis
IS 567 Knowledge Discovery Technologies

Students must take 1 course in applied analytics chosen among:
CSC 495 Social Network Analysis
ECT 584 Web Data Mining for Business Intelligence
CSC 575 Intelligent Information Retrieval
Advanced Courses
CSC 478 Programming Data Mining Applications
CSC 555 Mining Big Data
CSC 529 Advanced Data Mining
Students must take 1 course among the following:
CSC 521 Monte Carlo Algorithms
CSC 433 Scripting for Data Analysis
CSC 578 Neural Networks and Machine Learning
Elective Courses
Students must take 3 graduate level elective courses in the areas of statistical modeling, data mining or database technologies according to the following rules:

Students must take 1 500-level CDM elective. ANI, DC, TV, and VFX courses do not qualify.

Students must take 2 courses among the following:
CSC 425 Time Series Analysis and Forecasting
ECT 584 Web Data Mining for Business Intelligence
CSC 495 Social Network Analysis
CSC 433 Scripting for Data Analysis
CSC 478 Programming Data Mining Applications
CSC 529 Advanced Data Mining
CSC 575 Intelligent Information Retrieval
CSC 521 Monte Carlo Algorithms
CSC 481 Introduction to Image Processing
CSC 482 Applied Image Analysis
GPH 465 Survey of Visualization Applications
GPH 565 Designing for Visualization
CSC 453 Database Technologies
CSC 452 Database Programming
CSC 543 Spatial Databases and Geographic Information Systems
GEO 441 Geographic Information Systems (gis) for Community Development
CSC 598 Topics in Data Analysis
IS 549 Data Warehousing and Data Mining
IS 574 Business Intelligence
IS 578 Information Technology Consulting
Capstone
CSC 695 Master's Independent Study (1 - 4 Credits)
or CSC 697 Graduate Internship