STAT 650B - Python for Data Science

Units: 2
Overview of Python programming concepts. Basics of NumPy and Pandas packages. Data loading, cleaning, wrangling, and preparation on one and multiple tables. Data visualization and exploratory data analysis. Reproducible research techniques using the Google Colab/Jupyter Notebook.

Prerequisites: Post-baccalaureate standing
Possible Instructional Methods: On-ground, or Hybrid, or Online-Asynchronous, or Online-Synchronous.
Grading: A-f or CR/NC (student choice)
Course Typically Offered: Fall & Spring

Student Learning Outcomes - Upon successful completion of this course students will be able to:

1. Demonstrate proficiency in Python programming, including syntax, data types, control structures, and functions.

2. Utilize Python packages such as NumPy and Pandas to manipulate and analyze data.

3. Implement data cleaning techniques to handle missing values, outliers and prepare data for analysis by transforming/reshaping datasets.

4. Create a variety of visualizations using Python packages and interpret/present data insights.

5. Communicate the results of a data analysis clearly and appropriately to others using reproducible research techniques.