MATH 105 - Math for Data Science 1

Units: 5; Breadth Area: GE-B4
Introduces mathematical modeling as a tool for practicing data science. Discusses functions, vectors and vector fields, and change equations. The derivative is defined via Euler’s method and integration as an approximation of area. Students will analyze real world data. Must earn C- (CR) or better for GE credit.

Possible Instructional Methods: On-ground.
Grading: ABC/NC grading only.
Breadth Area(s) Satisfied: GE-B4 - Lower Division
Cross-listed: DATA 105
Course Typically Offered: Fall ONLY

Student Learning Outcomes - Upon successful completion of this course students will be able to:
1. Model problems in data science using functions, vectors, and change equation.

2. Apply appropriate mathematical concepts in the context of data science problems and explain and interpret graphs and plots.

3. Determine patterns of change in a data set and use approximations of area under the curve to solve problems.

B4. Mathematics/Quantitative Reasoning Learning Outcomes

Demonstrate a proficient and fluent ability to reason quantitatively;
demonstrate a general understanding of how practitioners and scholars collect and analyze data, build mathematical models, and/or solve quantitative problems; and
apply quantitative reasoning skills in a variety of real-world contexts, defined by personal, civic, and/or professional responsibilities.