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 鈥� 72
- In-Class Lab Hours: 0
Requisites and Advisories
- Prerequisites: Intermediate Algebra or equivalent, or eligibility for transfer level math
- Co-Requisites: None
- Advisory: MATH 010, CIST 005A
Transfer Details
- CSU/UC:
Transferable to both UC and CSU - WVC GE: Area A-2: Mathematical Concepts & Quantitative Reasoning
- CSU GE: Area B4 - Mathematics/Quantitative Reasoning
- UC/IGETC GE: Area 2 - Mathematical Concepts and Quantitative Reasoning