Course Description
The goal of this course is to teach students data science as a way of thinking. The course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from real-world phenomenon, the course asks how one analyzes the data so as to understand that phenomenon. The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design. As a byproduct, it is also meant to teach students some basic Python programming language syntax, and how to apply it to solve problems.
Units: 4
Credit - Degree Applicable Transferable to both UC and CSU
Course Details
- Grade Options: Letter Grade
- In-Class Lecture Hours: 64 – 72
- In-Class Lab Hours: 0
Requisites and Advisories
- Prerequisites: Placement as determined by the college’s multiple measures assessment processor completion of a course taught at or above the level of intermediate algebra .
- Co-Requisites: None
- Advisory: STAT C1000, CIST 005A
Transfer Details
- CSU/UC:
Transferable to both UC and CSU - WVC GE: Area A-2: Mathematical Concepts & Quantitative Reasoning
- Cal-GETC GE: Area 2: Mathematical Concepts and Quantitative Reasoning