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Course Syllabus

MATH 3080 Foundations of Data Science

  • Division: Natural Science and Math
  • Department: Mathematics
  • Credit/Time Requirement: Credit: 3; Lecture: 3; Lab: 0
  • Prerequisites: Math 1210 and (either Math 2040 or Math 3040) with a C or better in each course
  • Semesters Offered: Fall, Spring
  • Semester Approved: Fall 2025
  • Five-Year Review Semester: Summer 2030
  • End Semester: Summer 2031
  • Optimum Class Size: 20
  • Maximum Class Size: 25

Course Description

Students will get an introduction to a programming language for data analysis, data analysis tools, and the necessary statistics to acquire, clean, analyze, explore, and visualize data using real-life data sets. Using statistics, students will learn to make data-driven inferences and decisions, and to communicate those results effectively. Students interested in this course that don't meet the necessary pre-requisites should consider taking Math 2080 instead.

Justification

Data collection and the analysis of data is ubiquitous and fast becoming a prerequisite to economic success for businesses. This course provides a subset of the tools necessary to leverage data for prediction. This course will support the bachelor's in software engineering degree by providing relevant mathematics coursework.

Student Learning Outcomes

  1. Students will be able acquire data through web-scraping and data APIs.
  2. Students will be able to clean and reshape messy datasets.
  3. Students will be able to employ techniques of data visualization using computational software, including interactive visualization.
  4. Students will be able to use statistical software to deploy statistical methods including generalized linear regression and classification.
  5. Students will be able to explain the mathematics behind the models addressed in the class.
  6. Students will be able to identify strengths and weaknesses inherit in models, understand the behavior of the model, and determine when certain models are appropriate to be used.
  7. Students will be able to discuss the ethical issues of modern data science.

Course Content

This course will include an introduction to data analysis tools in a data analysis programming language, descriptive statistics, data structures in the language chosen for the course, introductory hypothesis testing & statistical inference, data acquisition via web scraping and APIs, data visualization, generalized linear regression, classification methods such as logistic regression and Naïve-Bayes, and ethical principles of Data Science. The class will also look at the mathematical processes on which these models are based.