Planning Guide: Data Science Minor

*Note that this guide reflects the requirements put into place for the 2024-2025 and 2025-2025 academic years.

Flow Chart

We offer the following flow chart as guidance for how to approach class sequencing. This is not the only way to sequence classes, so there could be instances where your plan differs from the flow chart, and that's okay. Keep in mind that the flow chart shows the requirement categories, and the specific classes you take may affect how well the flow chart reflects your experience.

Sample Plans

Every student’s academic plan is unique. Yours will probably look different from the samples and/or your friend’s plan, and that does not mean one of them is wrong. It is also normal for your plan to change as you go along, so please remember to be flexible. With that being said, we provide the sample plans as models to show how a student could complete the Data Science Minor.

In Sample Plan 1, the student wouldn’t need to take any other courses/prerequisites outside of those listed (with the possible exception of the Data Science Methodology requirement). This should demonstrate that the Data Science minor is accessible for students with various majors and academic backgrounds!

Courses marked with an asterisk have prerequisites that are not included within the Sample Plan.

Sample Plan 1

Year 1

Math 19, 20, 21 (Prerequisites)
CS 106A (Programming)

Year 2

Math 51 (Linear Algebra)
DataSci 112 (Data Science & Machine Learning)

Year 3

Stats 117 (Probability)
Econ 102A (Statistics)

Year 4

Choice of class* (Data Science Methodology from the cognate field of interest)

Sample Plan 2

Year 1

Math 19, 20, 21 (Prerequisites)

Year 2

Math 51 (Linear Algebra)

Year 3

CS 106A (Programming)
MS&E 120 (Probability)
HUMBIO 88 (Statistics)

Year 4

Choice of class* (Data Science Methodology from the cognate field of interest)
POLISCI 150B* (Data Mining & Machine Learning)

Helpful Hints

In general, we recommend that students who are interested in the Data Science Minor start by prioritizing any math prerequisites that they haven’t completed (Math 19, 20, 21), so they are ready to take the Linear Algebra and Probability requirements.
If you have little/no experience in programming, that’s totally okay! We recommend taking CS 106A early in your minor coursework, as it opens up more options for which courses you can take within the other requirements.
If you would like to talk through course selection or sequencing with someone, feel free to reach out to the datasciencemajor-inquiries [at] lists.stanford.edu (Data Science Student Services Team), or meet with a Data Science peer advisor!

Data Science Minor Courses & Prerequisites

The following chart shows all of the approved courses for the Data Science Minor requirements along with their prerequisites. We hope it is helpful while planning which courses to take.

Data Science Minor Requirement	Course Code	Course name	Prerequisites
Linear Algebra *Students who have taken CME 100 are recommended to take ENGR 108 to satisfy this requirement.	ENGR 108	Introduction to Matrix Methods	Math 51 or CME 100, basic knowledge of computing (CS 106A more than enough, can be taken concurrently).
	Math 51	Linear Algebra, Multivariable Calculus, and Modern Applications	Math 21 or equivalent
	Math 104	Applied Matrix Theory	Math 51, programming experience on par with CS 106A
Programming	CS 106A	Programming Methodology	None
Programming	CS 106B	Programming Abstractions	CS 106A or equivalent
Probability *Students who have taken CS 109 / EE 178 / MS&E 120 for their degree will be required to take STATS 118 or Math 151 to satisfy this requirement.	CS 109	Introduction to Probability for Computer Scientists	CS 103, CS 106B or X, and Math 51 or CME 100 or equivalent
	EE 178	Probabilistic Systems Analysis	Math 51, CME 100 or equivalent, CS 106A or equivalent
	Math 151	Introduction to Probability Theory	Math 61CM or (Math 52 and Math 56 or Math 115)
	MS&E 120	Introduction to Probability	CME 100 or Math 51
	STATS 117	Introduction to Probability Theory	Math 21 or equivalent (AP Calc. BC)
	STATS 118	Probability Theory for Statistical Inference	Math 51, Math 52 (may be taken concurrently), and STATS 117 or equivalents
Statistics	ECON 102A	Introduction to Statistical Methods (Postcalculus) for Social Scientists	Math 20 or equivalent
	HUMBIO 88	Introduction to Statistics for the Health Sciences	None
	HUMBIO 89	Introduction to Health Sciences Statistics	None
	MS&E 125	Introduction to Applied Statistics	(Recommended: MS&E 120, CS 106A, or equivalents)
	STATS 110	Introduction to Statistics for Engineering and the Sciences	Math 20 or AP Calc. AB
	STATS 141	Introduction to Statistics for Biology	None
	STATS 191	Introduction to Applied Statistics	Introductory statistics course, such as STATS 60, STATS 110, STATS 141, or 5 on the AP Stats exam. See statistics prerequisite course equivalencies for equivalent courses in other departments that satisfy these prerequisites.
	STATS 200	Introduction to Theoretical Statistics	STATS 118 or equivalent. See statistics prerequisite course equivalencies for equivalent courses in other departments that satisfy these prerequisites.
Data Mining & Machine Learning	DATASCI 112	Principles of Data Science	CS 106A or equivalent programming experience in Python. (Students with experience in another programming language should take CS 193Q to catch up on Python.)
	POLISCI 150B	Machine Learning for Social Scientists	POLISCI 150A (or 355A)
	STATS 202	Statistical Learning and Data Science	STATS 117, CS 106A, and MATH 51 or equivalents (Recommended: STATS 191 or STATS 203) See statistics prerequisite course equivalencies for equivalent courses in other departments that satisfy these prerequisites.
Data Science Methodology from the cognate field of interest *Because there are many courses across varying disciplines that meet this requirement, students are encouraged to research the prerequisites for the courses that interest them.	The list of courses that are approved to satisfy this requirement are listed on our minor requirements webpage.