CS 30800 - Introduction To Data Science And Analytics

This course introduces the fundamental concepts, methods, and practices of data science and analytics. Students will explore the data analytics lifecycle and develop a solid technical foundation in data processing, analysis, and visualization. Emphasis is placed on hands-on experience, as students apply quantitative reasoning and analytical skills to solve real-world problems using modern programming languages and data science libraries. Topics include data acquisition, cleaning and preparation, analysis methods, modeling, visualization, and visual storytelling. Through programming assignments and a comprehensive data analytics project, students will gain practical experience in transforming raw data into actionable insights.

Preparation for Course
P: CS 16100 (two semester-long sequence programming courses) or intermediate programming experience.

Cr. 3.
Student Learning Outcomes
1. Explain the fundamental principles of data science and the data analytics workflow (1).
2. Implement techniques for data acquisition, cleaning, and processing to prepare datasets for analysis (1, 2, 6).
3. Apply quantitative reasoning to solve data analysis problems using modern programming languages and libraries (1, 2, 6).
4. Apply appropriate analysis methods and visualization techniques to extract insights and communicate results effectively (1, 2, 3, 6).
5. Perform exploratory data analysis on real-world datasets and produce analytical results through written reports and visualizations (1, 2, 3, 6).
6. Demonstrate proficiency in Python programming for data manipulation, analysis, modeling, and visualization (2, 6).