Program Requirements

Learning outcomes

Upon successful completion of the program, graduates are equipped to:

  • Proficiently use general-purpose programming languages, machine learning software packages, and statistics analytics tools
  • Apply skills in computer science, machine learning, statistics, and mathematics to extract actionable knowledge from data with artificial intelligence techniques and data science tools to solve real-world problems
  • Communicate effectively the solution that meets employer and user needs
  • Articulate the practitioner and users’ responsibility while utilizing data/information and applying the techniques of artificial intelligence and data science in an ethical manner

Program plan

Major Core Requirements (12-18 credit hours)
INFD 601 Mathematics and Programming Foundation for Data Intelligence and Machine Learning*
INFD 602 Programming for Data Intelligence and Machine Learning*
INFD 605 Advanced Data Structures and Algorithms
INFD 611 Foundations of Statistical Algorithms
INFD 612 Data Processing and Manipulation
INFD 622 Big Data and Computationally Intensive Supervised Machine Learning

*INFD 601 and INFD 602 may be waived. See Admissions for more information.

Capstone (3 credit hours)
INFD 799 Capstone for Data Intelligence and Machine Learning

Concentration or Self-Designed Study (choose 15 credit hours)
Students will complete five courses in one of the following concentrations or in a self-designed study.

Artificial Intelligence
INFD 615 Introduction to Artificial Intelligence
INFD 631 Cloud Computing and Applications
INFD 635 Big Data and Computationally Intensive Unsupervised Machine Learning
INFD 645 Natural Language Processing in Artificial Intelligence
INFD 651 Computer Vision
INFD 655.Ethics and Civic Responsibility in Data Intelligence and Machine Learning
INFD 661 Deep Learning
INFD 671 Data Visualization
INFD 672 Advanced Human-Computer Interaction and Usability Studies

Data Science
INFD 621 Computational Statistics for Data Science
INFD 623 Multivariate Analysis Methods for Complex and High Dimensional Data
INFD 624 Computational Bayesian Statistics
INFD 625 Computational Models for Binary Public Health Outcomes
INFD 626 Computational Modeling for Survival Data in Healthcare
INFD 631 Cloud Computing and Applications
INFD 632 Computational Modeling for Longitudinal Health Data
INFD 633 Statistical Inferencing and Dissemination of Healthcare Data
INFD 635 Big Data and Computationally Intensive Unsupervised Machine Learning
INFD 655 Ethics and Civic Responsibility in Data Intelligence and Machine Learning
INFD 671 Data Visualization

Total Degree Requirements: 30-36

Learn more about our Applied Data Intelligence and Machine Learning program

Ready to take the next step?

Schedule your personalized admissions counseling appointment, or contact the Office of Graduate Admissions at 678.547.6417 or copa.admissions@mercer.edu for more information.