Courses
Five core areas and two upper division electives
Important:
Prospective data science minors should contact a Data Science professor before junior year, or after taking Foundations of Data Science. This is to verify that your courses will in fact count for the minor.
Prerequisites
MATH 030 is the first course of a standard three course sequence in calculus. The topics covered include differentiation, integration, mean value theorem, transcendental functions, and trigonometric functions.
Prerequisites: MATH 023 or placement examination.
Offered: Every semester
This is the second course of a standard three-course sequence in calculus. Topics covered include techniques and applications of integration, infinite series, power series and an introduction to differential equations.
Prerequisites: MATH 030 or placement examination.
Offered: Every semester
Five Core Areas
The below courses are the only ones accepted for the minor – substitute courses must be approved by your advisor before you register!
- DS001 SC - Introduction to Python and Data Analysis
- MS059 SC - Intro to Python and Viz
- CSCI004 PZ - Intro to Comp Sci for Non-majors
This course is the second part of a two-semester flipped-course introduction to computer programming and data science. Students will explore, using Python and other tools (e.g., SQL), the nuances of gathering, visualizing and analyzing data to drive informed decision-making. Students will be introduced to use various data manipulation/analysis and machine learning libraries (pandas, scikit learn, etc.) and statistical methods. They will also consider the ethical implications and limitations of creating models to deal with large amounts of data efficiently. As in the first course, students will work collaboratively on in-class projects dealing with real-world datasets. This course does not satisfy the Scripps math major/minor, or Scripps Math GE.
Prerequisites: DS 001 SC or equivalent (knowledge of Python).
Offered: Every spring
You must take this class – there are no off-campus equivalents that are accepted. After you take this class, it is strongly recommended that you meet with a member of the DS faculty before proceeding with the minor, if you have not already.
The below courses are the only ones accepted for the minor – substitute courses must be approved by your advisor before you register!
- BIOL174L KS - Introduction to Biology Research Statistics
- BIOL175 KS - Applied Biostatistics
- ECON120 SC - Statistics
- PSYC103 SC - Psychological Statistics
Data science is a set of interdisciplinary approaches that seeks to construct or extract knowledge from large cconomic, environmental, educational, or political policies. This course will give students insight into ethical challenges to and approaches in doing data science.
Prerequisite(s): None
You must take this course – there are no off-campus equivalents that are accepted.
This course emphasizes vector spaces and linear transformations. Topics include linear independence, bases, nullity and rank of a linear transformation, The Dimension Theorem, the representation of linear transformations as matrices, eigenvalues and eigenvectors, and determinants. Additional topics may include inner product spaces and Gram-Schmidt orthogonalization.
Multiple campuses offer this class with the code MATH60. Note - many students take this class without taking MATH32: Calulus III. Taking Calculus III will not majorly complement your understanding of Linear Algebra either.
Two Upper Division Electives
Upper division courses should be numbered 100 and above or approved by a Data Science minor adviser. Study abroad courses can fulfill these requirements. This list is NOT fully comprehensive, speak with an advisor to approve study abroad or additional 5C courses.
Neuroscience
- NEUR 133L KS - Introduction to Computational Neuroscience
- NEUR 182 SC - Machine Learning Using Neural Signals
Biology
- BIOL 138 KS - Applied Ecology and Conservation
- BIOL 138L KS - Applied Ecology and Conservation Lab
- BIOL 174L KS - Introduction to Biological Research Statistics
- BIOL 175 KS - Applied Biostatistics
- BIOL 184L KS - Disease Ecology and Evolution
Math
- MATH 055 SC - Discrete Mathematics
- MATH 101 SC - Introduction to Analysis
- MATH 102 SC - Differential Equations and Modeling
- MATH 131 SC - Principles of Analysis
- MATH 139 SC - Fourier Analysis
- MATH 183 SC - Modeling and Simulation
- MATH 185 SC - Methods in Modern Modeling
Economics
- ECON 120 SC - Statistics
- ECON 125 SC - Econometrics
Psychology
- PSYC 103 SC - Psychological Statistics
- PSYC 183 SC - Data Science Ethics & Justice
- PSYC 182 SC - Machine Learning Using Neural Signals
Physics
- PHYS 178 KS - Biophysics
4 Year Plan
Access and copy the sheet: General 4 Year Plan