Revised 03/2025

CSC 195 - Computer Science Topics in: Data Science Using Python (3 CR.)

Course Description

This course provides an in-depth exploration of key topics in data science and deep learning, focusing on theoretical foundations, practical applications, and recent advancements in the field. Students will learn essential data science techniques, including data preprocessing, feature engineering, and model evaluation, while gaining hands-on experience with machine learning frameworks. Basic knowledge of programming (Python preferred), linear algebra, probability, and introductory machine learning concepts are recommended. Lecture 3 hours. Total 3 hours per week

General Course Purpose

CSC 195 is intended as an elective course for the AS in Computer Science, and as requirements for Certificate in Computer Science program.

Course Prerequisites/Corequisites

CSC 221 is recommended.

Course Objectives

Upon completing the course, the student will be able to:

  • Explain key data science concepts, and Python packages used for data science.
  • Explain fundamental concepts of probability and statistics used in data science.
  • Define database concepts and the use of Structured Query Language (SQL) for Data Science, exploring techniques of data wrangling and data exploration.
  • Explain concepts related to data wrangling and data exploration.
  • Explain fundamental concepts of regression used in data science.
  • Explain supervised and unsupervised learning used in data science.
  • Define basic concepts of decision trees and their use in data science.
  • Analyze case studies and projects that apply data science and machine learning techniques to a real-world problem.
  • Present insights from data science projects using visualizations and summaries, effectively explaining model choices and outcomes in a written or verbal format as part of a final project.

Major Topics to Be Included

  • Introduction to Data Science and Python
  • Probability and Statistics
  • SQL for Data Science
  • Data Wrangling and Exploration
  • Regression in Data Science
  • Supervised and Unsupervised Learning
  • Decision Trees
  • Artificial Neural Networks