Data Science Sequence

Sequence Requirements

The sequence in data science requires 6 courses, distributed as follows:

An introductory course in programming in Python. Courses at The Claremont Colleges that satisfy this requirement include:

One course selected from:

One course selected from:

Two approved electives, at least one of which must be from Group B.

These are intermediate courses that require a quantitative or statistical skills prerequisite.

PSYC143 SC - Advanced Statistics I
Any two of PSYC308B CG - ANOVA (1/2 credit), PSYC308C CG - Applied Multiple Regression (1/2 credit), PSYC308D CG - Categorical Data Analysis (1/2 credit)

These courses are more advanced and generally require an intermediate quantitative course as a prerequisite.

One course selected from:

Students may not pursue the data science major and the data science sequence.
Students may not earn credit for more than one data science principles class (CSCI 036 CM , ECON 122 CM , and ECON 160 CM ).

The Data Science sequence presents students with the opportunity to:

Analyze data from a variety of disciplines and present its inherent features.
Predict outcomes of data processes or generate forecasts.
Work with computer programs, statistical packages, and data management programs.
Devise methodologies to turn data information into valuable insights.

Students who complete the sequence will be able to:

Analyze data and present its inherent features.
Have a working knowledge of advanced data analysis using a variety of platforms.
Work with commonly used statistical packages and programming languages (for example R, STATA, SPSS, Python, SQL) used in research, government, nonprofit organizations, and industry.
Make informed predictions, test theories, and evaluate policies.
Communicate the results of such analyses to all stakeholders.