Data Science

major

Data Science is a field of study that combines computer science (programming, databases, and algorithms) and statistical methodology, both with a strong mathematical foundation, to apply to diverse areas in ethical ways. Data scientists work in many areas, including business, economics, medicine, epidemiology, agriculture, environmental sciences, sports, and all aspects of government. With the increasing digitization and networking of society, data have become ever more ubiquitous, further expanding the demand for data scientists and their expertise in the collection, management, and analysis of data.

About this Program

To graduate with this major, students must complete all university, college, and major requirements.

Department Information

The mission of the Department of Statistics is to provide its students with a fundamental understanding of statistical reasoning and methodology, to train them to apply this knowledge to the collection and analysis of data, and to prepare them for careers in a highly technological society in which science and decision-making are increasingly driven by a rapid expansion in the quantity and availability of data.
Website

CONTACT

Email | 352.392.1941 (tel) | 352.392.5175 (fax)

P.O. Box 118545
102 GRIFFIN-FLOYD HALL
GAINESVILLE FL 32611-8545
Map

 Curriculum

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 following pre-requisite: STA 2023 (grade of B or higher) OR STA 3032 (grade of B- or higher) OR AP Statistics, (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 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 B.S. in data science requires a minimum of 62 credits in data science and related coursework. It is important that the prerequisites of each class are met before the class is attempted.

Mathematics17
Analytic Geometry and Calculus 2
Analytic Geometry and Calculus 3
Intro to Computational Math
Computational Linear Algebra
Linear Algebra for Data Science
Statistics18
Programming With Data in R
Introduction to Probability
Introduction to Statistics Theory
Regression Analysis
Statistical Learning in R
Statistical Computing in R
Computer Science17
Programming Fundamentals 1 (MAC 2311 coreq)
Programming Fundamentals 2
Discrete Mathematics
Applications of Discrete Structures
Data Structures and Algorithm
Information and Database Systems 1
Ethics3
Ethics, Data, and Technology
Subject Area Electives 9
Select 3 from Humanities or Natural Sciences or Social Sciences
Humanities
Environmental Ethics
Global Ethics
Religion and Science
Classical Archaeology
Gender, Race and Science
Discrimination and Health
History of American Medicine: Race, Class, Gender, and Science
Natural Sciences
Genetical Ethics
Advanced Computational Techniques
Social Sciences
Special Topics in Anthropology
Intro to Corpus Linguistics
Methods in Psycholinguistics
Econometrics 2
Laboratory in Sensory Processes
Special Topics in International Relations
Population
U.S. Population Issues
Foundations of Geographic Information Systems
Special Topics
Laboratory in Cognitive Neuroscience

Relevant Minors and Certificates

Data Science majors may want to consider a minor in actuarial science, which prepares students for careers as actuaries. Required courses cover the material for the beginning examinations and VEE credits leading to an associateship in the major national actuarial societies.

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

Semester 2

Semester 3

Semester 4

Semester 5

Semester 6

Semesters 7-8

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.

Plan of Study Grid
Semester OneCredits
Quest 1 (Gen Ed Humanities) 3
MAC 2311 Analytic Geometry and Calculus 1 (Critical Tracking; State Core Gen Ed Mathematics) 4
State Core Gen Ed Biological or Physical Sciences 3
State Core Gen Ed Composition 3
 Credits13
Semester Two
MAC 2312 Analytic Geometry and Calculus 2 (Critical Tracking; Gen Ed Mathematics) 4
MAD 2502 Intro to Computational Math (Critical Tracking) 3
STA 2023
Introduction to Statistics 1 (Critical Tracking; Gen Ed Mathematics)
or Engineering Statistics
3
Gen Ed Composition 3
Gen Ed Physical Sciences 3
 Credits16
Semester Three
Quest 2 (Gen Ed Biological or Physical Sciences-area not taken in semester one) 3
COP 3502C Programming Fundamentals 1 (Critical Tracking) 4
MAC 2313 Analytic Geometry and Calculus 3 (Critical Tracking; Gen Ed Mathematics) 4
State Core Gen Ed Social Science 3
 Credits14
Semester Four
COP 3503C Programming Fundamentals 2 (Critical Tracking) 4
MAS 3114 Computational Linear Algebra (Critical Tracking) 3
STA 3100 Programming With Data in R (Critical Tracking) 3
Gen Ed Social Science 3
Elective (3000-level or above, not in major) 3
 Credits16
Semester Five
MAD 3107
Discrete Mathematics (Critical Tracking)
or Applications of Discrete Structures
3
STA 4210 Regression Analysis (Critical Tracking; Gen Ed Mathematics) 3
STA 4321 Introduction to Probability (Critical Tracking; Gen Ed Mathematics) 3
Foreign language 5
 Credits14
Semester Six
COP 3530 Data Structures and Algorithm (Critical Tracking) 3
MAS 4115 Linear Algebra for Data Science (Critical Tracking) 3
PHI 3681 Ethics, Data, and Technology (Critical Tracking) 3
STA 4322 Introduction to Statistics Theory (Critical Tracking; Gen Ed Mathematics) 3
Foreign language 5
 Credits17
Semester Seven
CIS 4301 Information and Database Systems 1 (Critical Tracking) 3
STA 4241 Statistical Learning in R (Critical Tracking) 3
Subject Area Elective (Critical Tracking; 3000-level or higher, Gen Ed Humanities) 3
State Core Gen Ed Humanities 3
Gen Ed Biological Sciences 3
Science Laboratory (Gen Ed Biological or Physical Sciences) 1
 Credits16
Semester Eight
STA 4273 Statistical Computing in R (Critical Tracking) 3
Subject Area Electives (Critical Tracking; 3000-level or higher) 6
Elective (3000-level or above, not in major; Gen Ed Social and Behavioral Science) 3
Elective 2
 Credits14
 Total Credits120

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

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

Communication

  1. 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

Courses SLO 1 SLO 2 SLO 3
MAS 3114 I
MAS 4115 R
STA 3100 I I I
STA 4321 I
STA 4322 I
STA 4210 R R R
STA 4241 A A A
STA 4273 R R R
COP 3502C I
COP 3503C R
COP 3530 I
CIS 4301 R
PHI 3681 I R R

Assessment Types

  • Exams
  • Projects
  • Written and oral presentations