Python Programming and its Applications in Statistics
The first part of the course teaches basic Python programming knowledge and skills, such as Python variables, data containers, language controls, functions, objects, class, data structures, regular expressions, graphics, GUI, Jupyter notebook, etc. The second part deals with Python application in statistics (e.g., 2D/3D data visualization and statistical analysis, using some important Python packages for statistical computing and machine learning, for example, Numpy, Scipy, Pandas, and Scikit-learn, etc.) Python-database integration (e.g., access, update and control an Oracle database), and Python web services (e.g., database-driven dynamic webpages using Python CGI scripts). These techniques are utilized in a comprehensive course project.
Topics
- Be able to use a general-purpose computer programming language suitable for various computing needs.
- Master Python programming concepts and object-oriented programming techniques.
- Have the ability to write somewhat complicated Python programs to achieve computational goals of interest. For example, code and test a new model, pull out relevant data from a huge, messy raw data set, directly code and conduct some basic statistical analyses, integrate a database in statistical data analysis, and develop dynamic database-driven webpages.
- Master IPython (Jupyter) Notebook.
- Conduct some statistical data mining and data visualization with various Python packages.
Official roster learning outcomes
Prerequisites & course information
basic programming skills (any language), one introductory Statistics course, SQL (Oracle preferred).
Sources
- Course catalogue ↗2026-2027