The course objective is to introduce students to the fundamentals of Python programming as it applies to data analytics.
The first part of the course focuses on foundational Python to create scripts in an interactive development environment or IDE. Some foundational Python concepts students will learn in the first part of the course are data types and operators, string manipulation, container manipulation (lists and dictionaries), immutability, control flow, nested and unnested loops, built-in and self-defined functions, and nested and unnested list comprehension.
The second part of the course focuses on combining foundational Python with functionality within data-specific libraries. A Python library is a collection of pre-written code that simplifies common actions like performing statistical computations or creating plots. The libraries students will use are pandas, NumPy, and matplotlib to perform an end-to-end analysis in a Jupyter Notebook. Students will learn to debug, organize, refine, and refactor Python code to produce more efficient and readable code typical of an industry data professional.
In the course, students will use Python within a Terminal, IDE, and Jupyter Notebook to accomplish data analytics goals. Python becomes the single tool to explore, wrangle, visualize, statistically infer, and present data.