Data Science majors draw inference from large data generated from a variety of disciplines. Core courses cover mathematical foundations of data science, programming, algorithms, and databases as well as statistical methods for data science. Majors will also learn about data science in practice within subject matter areas.

Students who wish to major in data science must consult a department advisor early in their programs.

Coursework for the Major

To take STA 3100, which is required for the major, students must meet the prerequisite: (STA 2023 with a grade of B or higher) or (STA 3032  with a grade of B or higher) or (AP Statistics with a score of 4 or higher out of 5). Students also must receive minimum grades of C within two attempts (including withdrawals) in every required core course and in every course counted toward the 9 credit elective requirement, with the exception of  MAC 2312 and MAC 2313 where students must receive a minimum grade of B. Students cannot retake core or statistics elective courses after earning a minimum grade of C, with the exception of STA 2023 or STA 3032, MAC 2312 and MAC 2313, in which students must receive a minimum grade of B. The grades from all attempts to satisfy core requirements will be used to compute the minimum GPA. A minimum of 18 credits of major coursework must be taken at UF, including a minimum of 12 credits of core coursework.

Required Coursework

The BS in Data Science requires a minimum of 62 credits in data science and related coursework. It is important that the prerequisites of each course are met before the course is attempted.

Critical Tracking records each student’s progress in courses that are required for progress toward each major. Please note the critical-tracking requirements below on a per-semester basis.

For degree requirements outside of the major, refer to CLAS Degree Requirements: Structure of a CLAS Degree.

Equivalent critical-tracking courses as determined by the State of Florida Common Course Prerequisites may be used for transfer students.

Semester 1

• Complete MAC 2311 

Semester 2

• Complete MAC 2312 and (STA 2023 or STA 3032)

Semester 3

• Complete COP 3502C and MAC 2313 and MAD 2502 

Semester 4

• Complete COP 3503C and MAS 3114  and  STA 3100

Semester 5

• Complete (MAD 3107 or COT 3100) and STA 4210 and STA 4321  

Semester 6

• Complete COP 3530 and MAS 4115 and PHI 3681 and STA 4322

Semesters 7-8

• Complete CIS 4301 and STA 4241 and STA 4273 and one Subject Area elective

Students are expected to complete the Writing Requirement while in the process of taking the courses below. Students are also expected to complete the General Education International (GE-N) and Diversity (GE-D) requirements concurrently with another General Education requirement (typically, GE-C, H, or S).

The Subject Area electives and Data Ethics course count towards 3000-level or above electives outside of this multidisciplinary major.

To remain on track, students must complete the appropriate critical-tracking courses, which appear in bold. These courses must be completed by the terms as listed above in the Critical Tracking criteria.

This semester plan represents an example progression through the major. Actual courses and course order may be different depending on the student's academic record and scheduling availability of courses. Prerequisites still apply.

The Data Science major enables students to achieve proficiency in the fundamentals of programming, databases, and statistical reasoning. Through coursework and projects, students will gain knowledge in problem solving, data science applications and ethics, and statistical inference. Emphasis is on developing the ability to approach real world problems and through the use of computing and statistical methods to draw valid scientific inferences.

Before Graduating Students Must

• Complete an exam on the fundamentals of data science, which will be 5% of their grade in STA 4241 .
• Complete a data analysis project, which will be 10% of their grade in STA 4241 .

• Complete requirements for the baccalaureate degree, as determined by faculty.

Students in the Major Will Learn to

Student Learning Outcomes | SLOs

Content

1. Identify, define, and describe concepts and issues in data science, including those involved in computing and programming, databases, ethics, mathematical foundations, and statistical methods.

Critical Thinking

2. Identify sources of variability and bias in a given set of data and formulate and carefully program an appropriate statistical analysis.

Communication

3. Clearly and effectively present ideas in speech and in writing concerning issues in the proper analysis of data.

Curriculum Map

I = Introduced; R = Reinforced; A = Assessed

Assessment Types

• Exams
• Projects
• Written and oral presentations