CIS 109 - Data Science and Visualization

Credit(s) 3.00
Lecture Hours: 3.00
This course introduces students to the core principles and practices of data science with a focus on data visualization. Using Python and industry-standard libraries such as pandas, NumPy, Matplotlib, Seaborn, and Plotly, students will learn to collect, clean, preprocess, and analyze data. The course emphasizes exploratory data analysis (EDA), creating compelling visualizations, and applying these skills to real-world case studies to inform decision-making. Students will work hands-on in Jupyter/Colab environments with datasets from various domains. No

SLOs:
Upon successful completion of this course, students will be able to:

Collect and load datasets from files, APIs, and web sources.
Clean, preprocess, and normalize datasets using pandas and NumPy.
Perform exploratory data analysis (EDA) to identify patterns, correlations, and insights.
Create effective visualizations using Matplotlib, Seaborn, and Plotly.
Apply visualization best practices to communicate findings clearly.
Complete real-world data analysis projects from collection to presentation.
Work collaboratively in cloud-based environments for data science projects.