Data Science

This course is an introduction to the field of Data Science, exploring its definition, scope, ethical considerations and workflow from initial investigation to data acquisition to the communication of results. Students will be introduced to effectively wrangling and managing data, creating compelling data visualizations, and using statistical inference to draw meaningful conclusions. Students will also gain an overview of machine learning, including supervised and unsupervised learning techniques. Software tools and techniques will be integrated into all topics. Meets General Education requirement for Mathematics. 

This course introduces students to the principles and practical techniques of data wrangling using Python. Students learn how to collect, import, clean, reshape, merge, and transform data using Python’s core analytical libraries within the Jupyter Notebook environment. Emphasis is placed on mastering data manipulation, handling missing or inconsistent values, integrating multiple datasets, and preparing clean, reliable data for analysis or modeling, including both categorical and time-series data. Through hands-on labs and applied projects, students develop strong programming and analytical skills for efficiently and reproducibly managing real-world datasets.

This course explores the principles and practice of data visualization, from fundamental data mapping to the construction of interactive dashboards. Students will apply the psychology of visual perception to design high-integrity, accessible graphics that effectively communicate complex distributions and trends. By mastering professional storytelling and information architecture, learners will synthesize datasets into cohesive narratives suitable for publication and decision-making. Students will gain hands-on technical proficiency by utilizing Tableau for rapid dashboarding and Python 3.x within Jupyter Notebook environments for reproducible data analysis and visualization.

This course provides a foundation in Exploratory Data Analysis (EDA) using the Python ecosystem to bridge the gap between raw data and actionable insights. Students navigate the data science workflow by executing complex wrangling, interpreting descriptive statistics, and analyzing multivariate relationships. The curriculum covers hypothesis testing and time-series analysis while distinguishing between supervised and unsupervised learning contexts. Emphasizing critical thinking and ethical interpretation, the course uses hands-on labs and projects to prepare students to synthesize technical findings into narrative-driven reports for advanced analytics and modeling.