Course Description and Goals

Pre-requisites

CSC-121 Introduction to Computer Programming

Fulfills requirements

Core course requirement for Data Analytics minor
Human Behavior (HB) General Education Requirement (GER)
Pre-requisite for CSC-372: Machine Learning with Big Data

Course Description

This course introduces students to the core tools, methods, and mindset of modern data science, with a focus on practical skills in Python’s data ecosystem. Students will learn how to navigate the complete data science pipeline: collecting, cleaning, transforming, analyzing, and visualizing data, using industry-standard libraries such as pandas for data manipulation, matplotlib and seaborn for visualization, and introductory applications of scikit-learn for basic machine learning tasks.

While the course emphasizes hands-on coding and problem solving with real-world datasets, it also highlights the mathematical underpinnings that make data science work. Connections to Probability, Statistics, Linear Algebra, and Calculus are introduced throughout, ensuring students understand not just how to run analyses, but why the methods work and how they relate to future study in machine learning and artificial intelligence.

Course Goals

By the end of this course, students will be able to:

Navigate the data science pipeline by collecting, cleaning, transforming, analyzing, and visualizing data.
Develop proficiency with core Python libraries:

pandas for data wrangling and manipulation,
matplotlib and seaborn for effective visualizations,
scikit-learn for introductory machine learning workflows.

Apply statistical and mathematical reasoning to data analysis, recognizing how probability, statistics, linear algebra, and calculus underpin modern data science methods.
Work with real-world datasets to practice hands-on coding, problem-solving, and critical interpretation of results.
Build a foundation for advanced study in machine learning, statistical modeling, and artificial intelligence.