**Available Fall 2024**

 A Statistics and Data Science (SDS) degree can offer you more than a secure career path. This major equips you with data literacy — the ability to gather information and ask good questions with data.  Since statistics and data science are at the root of many economic, social, technical, medical, and environmental issues faced by society today, this major will equip our graduates with a deep understanding of statistical principles, data science tools to apply those skills to real-world problems, and the ability to express complex ideas in everyday language. We provide our students with research and experiential learning opportunities and nurture curiosity and creativity. These are essential skills for any informed citizen today.
 

Outline of Major

1. Pre-Major Courses (8 hours)
   1. (4 credit hours) Calculus I (MA 113 or MA 137) with a grade of B or above.
   2. (4 credit hours) Calculus II (MA 114 or MA 138) with a grade of B or above.
      
      The Statistics and Data Science major consists of a total of 41 credit hours not including prerequisites or pre-major courses. Three specializations are offered. Students must attain a grade-point average of at least 2.0 in all major requirements courses (including all pre-major courses).
       
2. Major Core Requirements (16 hours)
   1. (3 credit hours) STA 296: Stat Methods and Motivations (prereq: MA 113 or MA 137)
   2. (4 credit hours) STA 305: Intro to Data Science (prereqs: STA 296 or STA 381; and MA 109)
   3. (3 credit hours) STA 310: Intro to Probability and Inference (prereq: MA 114 or MA 138)
   4. (3 credit hours) STA 315: Applied Statistical Modeling and Experimental Design (prereq: STA 305)
   5. (3 credit hours) MA 322: Matrix Algebra and Its Applications (prereq: MA 114 or MA 138)
       
3. Other Courses Required for Major – From the Major Department (13 hours)
   1. (3 credit hours) STA 300: Data Visualization (prereqs: completion of UK Core’s Quantitative Foundation requirement).
   2. (4 credit hours) STA 415: Predictive Modeling and Introductory Machine Learning (prereqs: MA 322, STA 310, STA 315)
   3. (3 credit hours) STA 425: Computational Bayesian Statistics (prereqs: STA 305 and STA 315)
   4. (3 credit hours) STA 495: Statistics and Data Science in Context: A Practicum (prereqs: completion of Major Core and Other Courses requirements)
       
4. Specialization Through Other Coursework Required for Major (12 hours or more)
   
   In addition to the core courses, students have the option to further specialize their training in statistics and data science. Twelve (or more) credit hours must be proposed by the student and approved by the Director of Undergraduate Studies. This flexible specialization will allow students from a variety of backgrounds to complete the major with an approved specialty of their choosing and will make it easier for students to double major.