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:
- Formulate questions in a domain that can be answered with data.
- Use tools and algorithms from statistics, applied mathematics, and computer science for analyses.
- Visualize, interpret, and explain results cogently, accurately, and persuasively.
- 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
| Code | Title | Credit Hours |
|---|---|---|
| Total Credit Hours Required for the Minor in Data Science | 22-26 | |
Minor Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Prerequisite 1 | ||
| DSCI 101 | INTRODUCTION TO DATA SCIENCE | 3-4 |
| 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) 4 | 3-4 | |
| Capstone Requirement | ||
| DSCI 435 / COMP 449 | APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS | 4 |
| Total Credit Hours | 22-26 | |
Footnotes and Additional Information
| 1 | Note that selecting certain courses for Core Requirements may require additional prerequisites. |
| 2 | In certain situations the DSCI Official Certifier may approve various and specific course substitutions. |
| 3 | The Data Science department has determined that credit awarded for STAT 180 AP/OTH CREDIT IN STATISTICS is not eligible for meeting the requirements of the Data Science minor. |
| 4 | In certain instances, the DSCI Official Certifier may approve various or specific course substitutions. Courses at the 300-level (or above), other than those listed as Department Approved Electives, might also be allowed to fulfill the Elective Requirement, with approval from the Minor Advisor. |
Course List to Satisfy Requirements
| Code | Title | Credit Hours |
|---|---|---|
| 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
| 1 | In certain instances, the DSCI Official Certifier may approve various or specific course substitutions. Courses at the 300-level (or above), other than those listed as Department Approved Electives, might also be allowed to fulfill the Elective Requirement, with approval from the Minor Advisor. |
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. Requests for transfer credit must be approved for Rice equivalency by the designated transfer credit advisor for the appropriate academic department offering the Rice equivalent course (corresponding to the subject code of the course content). 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 the applicable transfer credit advisor as well as their academic program director when considering transfer credit possibilities.
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/.