With Xiaodan Leng (Pasadena City College) and Yu-Chung Chang (Pasadena City College)
Statistics' fast-expanding contacts with other fields of studies are pushing for more sophisticated and comprehensive technology tools in the introductory statistics course. For example, computation and statistical analysis of large, real-world data are becoming both a valued transformable skill to the follow-up college courses and marketable skill to expand employment of higher economic quartiles.
Python's simplicity and readability, robust open-source standard libraries, and community support make it super easy to learn and an ideal choice as a technology tool for an introductory statistics course. This workshop aims to give hands-on interactive training of Python programming in statistics for instructors who teach statistics at all levels. Participants will take home the working code they write during the workshop and additional resources to use in the classrooms.
The workshop starts with a review of widely used Python libraries for statistics. Participants then will learn how to write Python programs by following the presenter through some examples. Choices of programming areas are random sampling from a population and probability distributions, accessing large real-world online data in multiple formats, conducting hypothesis tests and visualizing p-values, plotting data, and performing linear regression. The workshop will end with an overview of the Elementary Statistics course curriculum with Python at Pasadena City College.
No prior programming experience is required. Participants will open the starter Jupyter Notebooks in Google's Colab and save a copy in their google drives. A laptop or desktop computer is required. It would be convenient to use an additional monitor to follow the presenter's demo.