Data Science Major

1. Prerequisites (2 courses)

2. Core (3 courses)

• CSCI055 CM - Discrete Structures

• or MATH055 CM - Discrete Mathematics  or equivalent

• CSCI046 CM - Data Structures and Algorithms

• or CSCI060 HM - Principles of Computer Science  

   

  One course in linear algebra, selected from:

• MATH060 CM - Linear Algebra
• MATH060C CM - Linear Algebra with Computing
• CSCI048 CM - Numerical Linear Algebra

3. Foundations (1 course)

One course selected from:

• CSCI036 CM - Foundations of Data Science
• ECON122 CM - Data Science and Statistical Learning
• ECON160 CM - Accounting Data Analytics

4. Probability and Statistics (2 courses)

• MATH151 CM - Probability
• MATH152 CM - Statistical Inference

5. Machine Learning (1 course)

• CSCI145 CM - Introduction to Data Mining (also listed as MATH 166 CM )

6. Remote Databases (1 course)

One course selected from:

• BIOL156L KS - Genomics and Bioinformatics
• CSCI143 CM - Big Data

7. Ethics (1 course)

One course selected from:

• PHIL033 CM - Introduction to Philosophy: Political Philosophy
• PHIL034 CM - Introduction to Philosophy: Moral and Political Issues
• PHIL035 CM - Introduction to Philosophy: Science, Technology, and Human Values
• PHIL158 CM - Ethical Theory
• PHIL159 CM - Metaethics
• PHIL177 CM - Global Ethics
• PHIL186 CM - Bioethics
• PHIL187 CM - Environmental Ethics
• PHIL189 CM - Business Ethics

8. Electives (2 courses)

Two courses selected from:

9. Capstone (1 course)

One course selected from:

• DS180 CM - Advanced Projects in Data Science
• DS181 CM - Advanced Projects in Data Science: Silicon Valley Program

Notes:

• Courses taken for this major, including the ethics requirement, may not double-count for general education (GE) requirements.
• GOVT 055 CM  and PSYC 109 CM  may not be taken during or after enrollment in MATH 152 CM .
• Students pursuing the data science major may not also pursue the data science sequence or the computer science sequence .

Dual Major

The dual major in data science requires a minimum of 2 prerequisites and 10 courses. Dual majors in data science may waive both elective course requirements from the full major.

Honors in Data Science

To be eligible for departmental honors in Data Science, students majoring in Data Science, including students with a dual major, must:

• Earn at least a 3.33 GPA in all major courses
• Write a senior thesis in data science that earns at least a grade of A- (3.67).

Study Abroad

All CMC students are encouraged to apply for study abroad. Data science majors planning to study abroad should consult with the department chair to determine which off-campus courses will be accepted for the major.

General Education Requirement in Mathematics/Computer Science

Any course offered by the CMC Department of Mathematical Sciences may satisfy the general education requirement. Any computer science course or calculus course offered at the other undergraduate Claremont Colleges may also satisfy the general education requirement. Students may take a pre-calculus course either in Claremont or off-campus for credit towards graduation but not for the general education requirement in mathematics.

General Education Requirement in the Social Sciences and the Humanities

For the general education requirement in the social sciences and the humanities, CMC students majoring in data science take designated courses in 3 of the 4 fields of the social sciences (economics, government, history, and psychology), and in 2 of the 4 fields of the humanities (literature, philosophy, religious studies, and literature in a foreign language). Courses fulfilling requirements for the data science major, including the ethics requirement, may not double-count for general education (GE) requirements.

Data science majors with a dual or double major in either the humanities or the social sciences will be required to take an additional course in those categories.

Learning Goals

Students with a major in Data Science will learn the following topics:

1. Relevant programming abilities.
2. Understanding computational efficiency and data structures.
3. Proficiency with statistical analysis of data from one or more disciplinary areas.
4. Working with remote databases (often referred to as big data.)
5. Understanding of probability and theory of statistics.
6. Communication skills including the the use of professional level statistical software for analysis, visualization, and reporting.
7. Making decisions based on statistical analysis of data together with an ethical framework.
8. Familiarity with common tools of data science.

Student Learning Outcomes

1. Given a data set, be able to build code to summarize and analyze the information it contains.
2. Given an methodology in software and hardware for handling data, be able to conduct an analysis of the efficiency of the method.
3. For remote databases, be able to access and utilize data using SQL and other technologies.
4. Be able to identify common probabilistic methods and use common statistical tools to analyze data.
5. Be able to communicate effectively by building presentations and writing reports that accurately reflect analyses.
6. Construct detailed models that allow for justified decisions based upon data.
7. Be able to use the common tools of data science to construct models and analyses of data.