Data Science

1. Python (1 course)

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

• CSCI005 HM - Introduction to Computer Science
• CSCI040 CM - Computing for the Web
• CSCI051P PO - Introduction to Computer Science in Python with Lab

2. Data Science Principles (1 course)

• CSCI036 CM - Foundations of Data Science

3. Statistics (1 course)

One course selected from:

• BIOL174L KS - Introduction to Biology Research Statistics
• BIOL175 KS - Applied Biostatistics
• ECON120 CM - Statistics
• GOVT055 CM - Empirical Methods
• MATH052 CM - Introduction to Statistics
• MATH152 CM - Statistical Inference
• PSYC109 CM - Introduction to Statistics for Psychologists

4. Electives (2 courses)

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

5. Capstone (1 course)

• DS180 CM - Advanced Projects in Data Science

Learning Goals

The Data Science sequence presents students with the opportunity to:

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

Student Learning Outcomes

Students who complete the sequence will be able to:

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