2022-2023 Catalog 
    
    Apr 16, 2024  
2022-2023 Catalog [ARCHIVED CATALOG]

Data Science Major


Major Requirements


The major in data science requires a minimum of 2 prerequisites and 12 courses, distributed as follows:

1. Prerequisites (2 courses)


Python


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

Calculus


4. Probability and Statistics (2 courses)


5. Machine Learning (1 course)


6. Remote Databases (1 course)


One course selected from:

8. Electives (2 courses)


Two courses selected from:

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 .

Senior Thesis in Data Science


The senior thesis is a general education requirement and the capstone experience of a student’s undergraduate education. Students must complete a senior thesis in at least 1 of their majors under supervision of a faculty reader who teaches within that major, unless granted a special exception.

Students interested in a 2-semester thesis project complete a 0.5 credit or 1.0 credit thesis research course in the 1st semester and the senior thesis in the 2nd semester. The senior thesis and the thesis research course may not be counted as courses in the major.

Special Options for Majors


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 Information for Data Science Majors


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 and Student Learning Outcomes of the Program in Data Science


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.