Graduate Coursework
Hands-On Technical Laboratory Work
This page collects the laboratory activities completed across three graduate data analytics courses. The exercises document practical experience with data preparation, database systems, SQL, Python, visualization, query optimization, and analytical communication.
DAT 510
Foundations of Data
The DAT 510 laboratory work established the core workflow of data analytics through exercises in data preparation, cleaning, structured analysis, and interpretation. These activities emphasized foundational mechanics such as organizing datasets, identifying data-quality issues, applying descriptive analytical methods, and using technical tools to transform raw information into reliable results that could support later statistical, database, and visualization work.
DAT 515
Enterprise Data Management
The DAT 515 laboratories focused on designing, managing, and optimizing enterprise data systems. Work included relational database development in MySQL Workbench, SQL data manipulation, schema and entity-relationship modeling, CRUD operations, MongoDB document databases, aggregation pipelines, indexing, and explain-plan analysis. Together, the labs demonstrated how relational and NoSQL technologies support scalable data architecture, governance, and efficient organizational access to information.
DAT 530
Present and Visualize Data
The DAT 530 laboratories centered on building effective visualizations in Python using pandas and Matplotlib. Exercises included bar charts, grouped bar plots, histograms, annotations, distribution analysis, plot customization, and comparisons of alternative coding approaches. The labs also emphasized selecting appropriate chart types, providing context, representing variability and uncertainty, and avoiding visual choices that could mislead an audience.