Data Science, MS

The Master of Science with a major in Data Science degree is a two-year interdisciplinary program taught by faculty from the Department of Computer Science, the Department of Biostatistics, and the Institute of Biomedical Informatics. The curriculum aims to prepare the future data science professional with a critical skillset that includes data processing, visualization, database management, machine learning, big data analytics, and statistical reasoning. The program includes a required capstone project, in which students analyze real-life datasets in a selected application domain in collaboration with domain experts and other data scientists. 

The Master of Science with a major in Data Science degree is available with two concentrations: 

• Biomedical Informatics 

• Software and Systems 

See below for admission and degree requirements, and the program website (https://www.engr.uky.edu/data-science) for additional information. 

 

Admission Requirements 

Admission to the program requires a minimum undergraduate GPA of 3.0 on a 4.0 scale. Students must have successfully completed a course in calculus (comparable to the UK course MA 113) and two courses in programming (comparable to the UK courses CS 115 and CS 215). Students will preferably have also successfully completed a course on linear algebra (such as the UK course MA 322) and will have experience using several programming languages. Exceptions to these requirements may be made by the Director of Graduate Studies if other persuasive evidence indicating the student’s potential of success is available. Such evidence could include high scores on standardized tests (e.g., GRE); demonstrated ability in computer programming; or strong performance in courses in the sciences, engineering, mathematics, statistics, or other quantitative disciplines. 

 

Degree Requirements 

The program requires 33 credit hours which are divided into Major (27 hours) and Concentration (6 hours) Requirements. 

 

Major Requirement (27 credit hours) 

1. Core (15 credit hours) 
   • DS 501 FUNDAMENTALS OF DATA SCIENCE  (3 credit hours) 
   • BST 600 INTRODUCTION TO BIOSTATISTICAL METHODS  (3 credit hours) 
   • BST 681 LINEAR REGRESSION    (3 credits hours) 
   • DS 710 RESEARCH SEMINAR IN DATA SCIENCE  (1 credit hour x 3 times) 
   • DS 711 MASTERS PROJECT IN DATA SCIENCE  (3 credit hours) 
2. Guided Electives in CS (3 credit hours) (One of the three required): 
   • CS 405G INTRODUCTION TO DATABASE SYSTEMS  (3 credit hours) 
   • CS 460G MACHINE LEARNING  (3 credit hours)  
   • CS 626 LARGE SCALE DATA SCIENCE  (3 credit hours)  
3. Free Electives (9 credit hours) 
   • All electives must be approved by the DGS. At least two free electives must be at the 600 or 700 level. Moreover, free electives must include at least two courses (6 credits) with a strong data science component in the subject area of the student’s project. 

 

Concentration Requirement (6 credit hours) 

There are currently two concentrations from which the student is required to select. 

Concentration in Biomedical Informatics 

1. Concentration Core (3 credit hours)  
   • BMI 633 INTRODUCTION TO BIOINFORMATICS    
2. Concentration Electives (3 credit hours) (One of the three required) 
   • BMI 730 PRINCIPLES OF CLINICAL INFORMATICS  
   • BMI 733 BIOMEDICAL NATURAL LANGUAGE PROCESSING   
   • BMI 734 INTRODUCTION TO BIOMEDICAL IMAGE ANALYSIS   

 

Concentration in Software and Systems

1. Concentration Core (6 credit hours) 
   • CS 460G MACHINE LEARNING  or CS 628 DATA MINING   
   • CS 626 LARGE SCALE DATA SCIENCE    
   • CS 505 INTERMEDIATE TOPICS IN DATABASE SYSTEMS  only if CS 626 LARGE SCALE DATA SCIENCE  is taken as a Guided Elective