DATA 747 - Python for Data Analytics

3 Credits
This course introduces students to Python programming, focusing on data analysis, the data pipeline, and database data integration techniques. Students use popular libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn to clean, extract, transform, load, analyze data, and create visualizations to communicate their findings. Engaging in hands-on experiences through a variety of exercises and projects, students develop a strong foundation in Python programming and gain the skills needed to build applications to tackle real-world data analytics problems.

Course Learning Outcomes:
1. Develop procedural computer programs using mathematical operators, library functions, logical operators, expressions, data structures, conditional statements, iteration statements, and exception handling.
2. Develop procedural computer programs to process data that incorporates input from the keyboard, a file, the network, or a database, directing the output to the screen, a file, the network, or a database.
3. Build reusable functions or methods.
4. Explain how a program satisfies the functional and non-functional requirements and is free from defects.
5. Identify best practices for source code organization, documentation, testing, version control, and team-based code review.