Program Overview

Introduction to the MS Data Science Program

The MS Data Science program at DePaul University is designed to provide students with the technical knowledge and advanced computational skills to meet emerging challenges in big data analytics. With on-campus and online classroom learning formats, students can launch a career in data science with extraordinary faculty membersanytime, anywhere.

Concentrations

The program offers three concentrations:

• Computational Methods: designed for data science master's students who want to develop strong technical and programming skills to solve complex data problems.
• Health Care: addresses the increasing need of data scientists responsible for gathering, integrating, analyzing, and presenting health-related data.
• Marketing: designed for data science master's students who want to combine strong technical skills in analytics with competency in marketing analytics and customer relationship management.

Curriculum Requirements

Each concentration has its own set of curriculum requirements, including introductory courses, foundation courses, advanced courses, and elective courses.

Computational Methods Concentration

• Introductory Courses: CSC 401 - Introduction to Programming, CSC 412 - Tools and Techniques for Computational Analysis, IT 403 - Statistics and Data Analysis
• Foundation Courses: DSC 441 - Fundamentals of Data Science, DSC 430 - Python Programming, DSC 465 - Data Visualization, DSC 450 - Database Processing for Large-Scale Analytics, DSC 445 - Machine Learning I
• Advanced Courses: CSC 555 - Mining Big Data, DSC 478 - Programming Machine Learning Applications
• Elective Courses: students must select four credit hours of graduate-level elective courses from a list of approved courses.

Health Care Concentration

• Introductory Courses: CSC 401 - Introduction to Programming, CSC 412 - Tools and Techniques for Computational Analysis, IT 403 - Statistics and Data Analysis
• Foundation Courses: DSC 441 - Fundamentals of Data Science, DSC 430 - Python Programming, DSC 465 - Data Visualization, DSC 450 - Database Processing for Large-Scale Analytics, DSC 445 - Machine Learning I
• Advanced Courses: DSC 510 - Health Data Science, HIT 421 - Introduction to Health Informatics
• Elective Courses: students must take four credit hours of graduate-level elective courses from a list of approved courses.

Marketing Concentration

• Introductory Courses: CSC 401 - Introduction to Programming, CSC 412 - Tools and Techniques for Computational Analysis, IT 403 - Statistics and Data Analysis
• Foundation Courses: DSC 441 - Fundamentals of Data Science, DSC 430 - Python Programming, DSC 465 - Data Visualization, DSC 450 - Database Processing for Large-Scale Analytics, DSC 445 - Machine Learning I
• Advanced Courses: DSC 424 - Advanced Modeling and Analysis Techniques, MKT 534 - Analytical Tools for Marketers
• Elective Courses: students must take four credit hours of graduate-level elective courses from a list of approved courses.

Capstone Options

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.

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.

Faculty

The School of Computing at DePaul University has a team of experienced faculty members with research interests in various areas, including artificial intelligence, machine learning, data science, and health informatics. Some of the notable faculty members include:

• Roselyne Tchoua: interests in making seemingly inaccessible technology or unmanageable amounts of data more reachable.
• Jamshid Sourati: research interests in developing and deploying machine learning and AI techniques to model and analyze the flow of knowledge and discoveries in science and technology.
• Bamshad Mobasher: research areas include artificial intelligence and machine learning, with a focus on Web mining, Web personalization, and recommender systems.
• Casey Bennett: work focuses on artificial intelligence in healthcare, including robotics, human-robot interaction, machine learning, internet-of-things, clinical decision support, and personalized medicine.