Minor in Data Science

Program Learning Outcomes for the Minor in Data Science

Upon completing the minor in Data Science, students will be able to:

  1. Formulate questions in a domain that can be answered with data.
  2. Use tools and algorithms from statistics, applied mathematics, and computer science for analyses.
  3. Visualize, interpret, and explain results cogently, accurately, and persuasively.
  4. Understand the underlying social, political, and ethical contexts that are importantly and inevitably tied to data-driven decision-making. 

Requirements for the Minor in Data Science

Students pursuing the minor in Data Science must complete:

  • A minimum of 7 courses (22-26 credit hours, depending on course selection) to satisfy minor requirements.
  • A minimum of 5 courses (15-19 credit hours, depending on course selection) taken at the 300-level or above.
  • 1 course (3-4 credit hours, depending on course selection) to satisfy the Prerequisite.
  • 4 courses (12-14 credit hours, depending on course selection) to satisfy the Core Requirements.
  • 1 course (3-4 credit hours, depending on course selection) to satisfy the Elective Requirement.
  • A capstone project (4 credit hours). 

The courses listed below satisfy the requirements for this minor. In certain instances, courses not on this official list may be substituted upon approval of the minor’s academic advisor, or where applicable, the Program Director. (Course substitutions must be formally applied and entered into Degree Works by the minor's Official Certifier). Students and their academic advisors should identify and clearly document the courses to be taken.

Summary 

Total Credit Hours Required for the Minor in Data Science22-26

Minor Requirements 

Prerequisite 1
DSCI 101INTRODUCTION TO DATA SCIENCE3-4
or COMP 140 COMPUTATIONAL THINKING
Core Requirements 1,2
Statistics
Select 1 course from the following:3-4
APPLIED STATISTICS FOR BIOENGINEERING AND BIOTECHNOLOGY
DATA ANALYTICS
PROBABILITY AND STATISTICS FOR DATA SCIENCE
RANDOM SIGNALS IN ELECTRICAL ENGINEERING SYSTEMS
STATISTICAL METHODS-PSYCHOLOGY
SOCIAL STATISTICS
QUANTITATIVE ANALYSIS FOR THE SOCIAL SCIENCES
ELEMENTARY APPLIED STATISTICS 3
INTRODUCTION TO STATISTICS FOR BIOSCIENCES
PROBABILITY AND STATISTICS
HONORS PROBABILITY AND MATHEMATICAL STATISTICS
Big Data
Select 1 course from the following:3
INTRODUCTION TO DATA SCIENCE TOOLS AND MODELS 2
TOOLS AND MODELS FOR DATA SCIENCE
INTRODUCTION TO DATABASE SYSTEMS
Machine Learning
Select 1 course from the following:3-4
PRACTICAL MACHINE LEARNING FOR REAL WORLD APPLICATIONS
STATISTICAL MACHINE LEARNING
MACHINE LEARNING FOR DATA SCIENCE
MACHINE LEARNING: CONCEPTS AND TECHNIQUES
INTRODUCTION TO MACHINE LEARNING
INTRODUCTION TO STATISTICAL MACHINE LEARNING
Ethics
Select 1 course from the following:3
DATA, ETHICS, AND SOCIETY
COMPUTER ETHICS
Elective Requirement
Select 1 course at the 300-level (or above) from department approved electives (see course list below) 43-4
Capstone Requirement
DSCI 435 / COMP 449APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS4
Total Credit Hours22-26

Footnotes and Additional Information

Course List to Satisfy Requirements 

Department Approved Electives 1
Select 1 course from the following:3-4
STATISTICAL METHODS IN PHYSICS AND ASTRONOMY
ANALYSIS AND VISUALIZATION OF BIOLOGICAL DATA
PHYSICS GUIDED MACHINE LEARNING & DATA DRIVEN MODELING FEM
MATRIX ANALYSIS FOR DATA SCIENCE
LARGE-SCALE OPTIMIZATION
STATISTICAL MODELS AND ALGORITHMS FOR DATA SCIENCE
INTRODUCTION TO COMPUTER VISION
PROBABILISTIC ALGORITHMS AND DATA STRUCTURE
INTRODUCTION TO EFFECTIVE DATA VISUALIZATION
ECONOMETRICS
ECONOMIC FORECASTING
GEOPHYSICAL DATA ANALYSIS: DIGITAL SIGNAL PROCESSING
GEOPHYSICAL DATA ANALYSIS: INVERSE METHODS
DIGITAL SIGNAL PROCESSING
DATA SCIENCE AND DYNAMICAL SYSTEMS
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING AND SIGNAL PROCESSING FOR NEURO ENGINEERING
INTRODUCTION TO ROBOTICS
COMPUTATIONAL LINGUISTICS
RESPONSIBLE AI FOR HEALTH
ADVANCED STATISTICAL METHODS FOR PSYCHOLOGY UNDERGRADUATES
ADVANCED SPORT ANALYTICS
SPORT BUSINESS ANALYTICS
SPATIAL ANALYSIS IN THE SOCIAL SCIENCES
DATA ANALYSIS
R FOR DATA SCIENCE
LINEAR REGRESSION
ADVANCED STATISTICAL METHODS
STATISTICAL INFERENCE
APPLIED TIME SERIES AND FORECASTING
PROBABILITY IN BIOINFORMATICS AND GENETICS
INTRODUCTION TO BAYESIAN INFERENCE
QUANTITATIVE FINANCIAL RISK MANAGEMENT
BIOSTATISTICS
QUANTITATIVE FINANCIAL ANALYTICS
MARKET MODELS
COFES BLOCKCHAIN AND CRYPTOCURRENCIES

 Footnotes and Additional Information

Policies for the Minor in Data Science

Program Restrictions and Exclusions

Students pursuing the minor in Data Science should be aware of the following program restrictions:

  • As noted in Majors, Minors, and Certificates, i.) students may declare their intent to pursue a minor only after they have first declared a major, and ii.) students may not major and minor in the same subject.

Transfer Credit 

For Rice University’s policy regarding transfer credit, see Transfer Credit. Some departments and programs have additional restrictions on transfer credit. The Office of Academic Advising maintains the university’s official list of transfer credit advisors on their website: https://oaa.rice.edu. Students are encouraged to meet with their academic program’s transfer credit advisor when considering transfer credit possibilities. 

Program Transfer Credit Guidelines

Students pursuing the minor in Data Science should be aware of the following program-specific transfer credit guidelines:

  • Requests for transfer credit will be considered by the program director (and/or the program’s official transfer credit advisor) on an individual case-by-case basis.

Additional Information 

For additional information, please see the Data Science website: https://datascience.rice.edu/

Opportunities for the Minor in Data Science

Academic Honors

The university recognizes academic excellence achieved over an undergraduate’s academic history at Rice. For information on university honors, please see Latin Honors (summa cum laude, magna cum laude, and cum laude) and Distinction in Research and Creative Work. Some departments have department-specific Honors awards or designations.

Additional Information 

For additional information, please see the Data Science website: https://datascience.rice.edu/