1. Using Python for Exploratory Data Analysis and Statistical Inference: A Hands-On Short Course
- Dr. Melvin S. Munsaka, AbbVie
Python has become one of the most popular and powerful programming languages in recent years and is now one of the standard tools for data analysis. It has a mature and growing ecosystem of open-source tools for mathematics and data analysis. It has plenty of great libraries that implement various analysis and functions and because of its general-purpose nature, it is often a one-stop-shop for programming with great extensibility. Python also has great dedicated analytical libraries available not just for routine statistical use, but also for complex analyses and machine learning and is used across many scientific sectors. In this short course, students will learn how to use Python for exploratory data analysis, statistical inference, probability, visualization, and modeling. The course will provide some basics of Python and data structures and move onto how to use Python for exploratory data analysis and visualization, statistical inference, probability, and modeling. By the end of the course, the course participants should have a good understand of Python programming basics, load data into Python from different sources, rearrange and aggregate data in Python and its use in statistical analysis, including descriptive, inference, visualization, and statistical modeling. The course will also prepare the course participants for more complex statistical analyses and data science topics beyond those discussed in the course, including Bayesian analysis and machine learning, which will also be briefly discussed in the course.
Audience: Statisticians, data scientists, programmers
Required: This is a hands-on course. Thus, participants should have access to a computer with an internet connection. Although no basic knowledge of Python is assumed, an understanding of basic concepts in statistics will be assumed.