In this six part workshop series provides an introduction into the open data science practices using Python and its various tools for researchers. This will be a highly interactive/hands on series with plenty of practice provided. 

Topics for this course includes an overview of the Python programming as needed data science from variables, data structures, loops conditional statements (old and new), object-oriented methods. With this foundation we will explore open data, how to obtain it, how to explore, change, manipulate and visual it. How to deal with Big data, working with hundreds to thousands of files including higher performance methods. Finally a quick dive into open AI methods for extracting, summarizing and even create synthetic data using AI.

All along the way I will provide bountiful tips and tricks based on commonly asked questions from your very own peers, coworkers, and advisors!

Here is a breakdown for each session.
Part 1 - Python Fundamentals
Part 2 - Obtaining Data and Working with Data Part 1
Part 3 - Working with Data 2
Part 4 - Working with Large Data, Multiple Files and High(er) Performance Processing
Part 5 - Exploratory Data Visualization
Part 6 - Open AI for Open Data

Who is this workshop for: Both people new to Python as well as seasoned veterans. 

What will I walk away with: A better and firm understanding of how open methods to work obtain, analyze, munge, and visualize data as well as create synthetic data.