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Degree Requirements

The student must satisfy the major requirements, the concentration requirements, and the project requirement. All courses for the major and concentration requirements must have regular letter grades, that is, no pass/fail. The student’s overall GPA in these courses must be 3.0 or higher and no incomplete grades are allowed.

Major Requirements (27 credit hours)

Major Core (12 credit hours) (All of the following are required)

  • DS 501: Fundamentals of Data Science (3 credit hours)
  • BST 600: Introduction to Biostatistical Methods (3 credit hours)
  • DS 710: Research Seminar in Data Science (1 credit hour; must be taken 3 times)
  • DS 711: Master’s Project in Data Science (3 credit hours)

Guided Electives in CS (3 credit hours) (At least one of the following must be taken)

  • CS405G: Introduction to Database Systems (3 credit hours)
  • CS460G: Machine Learning (3 credit hours)
  • CS626: Large Scale Data Science (3 credit hours)

Guided Electives in Statistics (3 credit hours) (At least one of the following must be taken)

  • BST 675 Biometrics I (3 credit hours)
  • BST 681 Linear Regression (3 credit hours)
  • CPH 636 Data Mining in Public Health (3 credit hours)

Free Electives (9 credit hours)

The student must earn 9 credit hours of free electives approved by the DGS. This will typically consist of 3 courses, at least two of which must have a strong data science component and be at the 600 or 700 level. Free electives should be selected to be relevant to the student’s MS project. If a course is used to satisfy a major or concentration requirement, it may not be simultaneously used as a free elective.

Any course listed on this page may be used as a free elective, including those for the major and concentration requirements and courses in the following list:

  • BSC 534 Clinical Research Ethics (3 credit hours)
  • BMI 731 Biomedical Information Retrieval (3 credit hours)
  • BMI 732 Biomedical Ontologies and Semantic Web Techniques (3 credit hours)
  • BST 535 Introduction to R Programming (3 credit hours)
  • BST 635 Databases and SAS Programming (3 credit hours)
  • BST 655 Introduction to Statistical Genetics (3 credit hours)
  • BST 661 Survival Data Analysis (3 credit hours)
  • BST 676 Biometrics II (3 credit hours)
  • BST 682 Generalized Linear Models (3 credit hours)
  • BST 693 Statistical Practice in Public Health (3 credit hours)
  • BST 762 Longitudinal Data Analysis (3 credit hours)
  • CS 463g Introduction to Artificial Intelligence (3 credit hours)
  • CS 636 Computer Vision (3 credit hours)
  • CS 688 Neural Networks (3 credit hours)
  • PPS 710 Techniques for Secondary Data Research (3 credit hours)
  • STA 678 Statistical Computational Theory and Data Visualization: R and SAS (3 credit hours)

Note that while these have been approved, the student must still satisify the course pre-requisites, or obtain instructor consent, to enroll.

Concentration Requirements (6 credit hours)

The student must satisfy the requirements for one of the following concentration. Courses used to satisfy this requirement cannot be simultaneously used to satisfy a major requirement. If all courses listed below have been used in that capacity, please consult the program administration for additional options.

Concentration in Biomedical Informatics

  • Concentration Core (3 credit hours) (required)
    1. BMI 633: Introduction to Bioinformatics (3 credit hours)
  • Concentration Elective (3 credit hours) (at least one of the following must be taken)
    1. BMI 730: Principles of Clinical Informatics (3 credit hours)
    2. BMI 733: Biomedical Natural Language Processing (3 credit hours)
    3. BMI 734: Introduction to Biomedical Image Analysis (3 credit hours)

Concentration in Software and Systems for Data Science

  • Concentration Core (6 credit hours) (at least two of the following must be taken)
    1. CS460G: Machine Learning (3 credit hours) or CS628: Data Mining (3 credit hours)
    2. CS626: Large Scale Data Science (3 credit hours)
    3. CS505: Intermediate Topics in Databases (3 credit hours)

Capstone Project Requirement

The student will complete a capstone project under the supervision of a professor. The main steps, which must be completed in order, are as follows:

  1. Select a supervising professor: The student must select a member of the data-science faculty to supervise their project. Potential supervisors are listed on the faculty page. The official, up-to-date list may be found in the faculty database which is maintained by the Graduate School (search by program ‘DAS’).

  2. Conduct the research/development for their project: The student will implement a data science project, following the guidance of the supervising professor, and write a report to summarize the work. There is no specific format required for the report. However, the writeup must be of sufficient quality to enable committee members to determine the general nature, scope, and value of the project.

  3. Form an advisory committee: The student should consult with the supervising professor when forming the committee. The committee must have at least three members; at least one must be a full member of the graduate faculty (see the faculty database to determine who is currently a full member). The student should present all the committee members with a clean draft of the project/thesis report and get their agreement to serve on the committee.

  4. Schedule the Master’s Exam: The student initiates the request for the Master’s exam, by specifying the committee, the date, the time, and the place through an online form. See the Check sheet for Master’s Non-Thesis Students for additional details. The exam should be scheduled at least three weeks before the intended exam date. The student should list the title of the project in the comments section of this form. The Director of Graduate Studies (DGS) will verify the following: (1) that the student has completed the course-related requirements for the MS degree, and (2) that all the committee members are available at the specified time and believe that the student is ready to take the exam. The DGS will then approve the exam and announce it to the faculty and students associated with the Data Science program.

  5. Defend the project at the Master’s Exam: During the exam, the student must explain and defend their project. The Master’s exam is open, meaning that all members of the university community are invited to attend. The student must provide a copy of the project report (in hard copy, if requested) to each committee member at least 10 calendar days before the scheduled examination date.

Program Administration

  • If you have questions regarding degree requirements, please contact Kathy Ice-Wedding at kathy@cs.uky.edu.
  • If you have questions about elective or alternative courses, please contact the DGS at ds-info@cs.uky.edu.