Introduction to Python for Data Science

Gus Powers & Jay Cunningham

Introductions

Gus Powers

Lead Data Scientist at 84.51° Creating and maintaining data science tools for internal use Python, Bash (shell), & R Academic BS, Chemistry, Thomas More College MS, Chemistry, University of Cincinnati MS, Business Analytics, University of Cincinnati Contact GitHub: augustopher LinkedIn: August Powers Email: guspowers0@gmail.com

Jay Cunningham

Lead Data Scientist at 84.51° Researching and developing forecasting models Machine learning, Python Academic BA, Mathematics, University of Kentucky MA, Economics, University of North Carolina (Greensboro) Contact LinkedIn: Jay Cunningham Email: james@notbadafterall.com

Around The Room

We'll go around the room. Please share:

1. Your name
2. Your job or field
3. How you use Python now or would like to in the future

Course

Defining Data Science

Data Science and Technology

Applied Data Science

Course Objectives

The following are the primary learning objectives of this course:

1. Develop comprehensive skills in the importing/exporting, wrangling, aggregating and joining of data using Python.
2. Establish a mental model of the Python programming language to enable future self-learning.
3. Build awareness and basic skills in the core data science area of data visualization.

Course Agenda

Day	Topic	Time
Day 1	Introductions	12:30 - 12:45
	Python and Jupyter Overview	12:45 - 1:15
	Fundamentals	1:15 - 2:00
	Break	2:00 - 2:15
	Packages, Modules, Methods, Functions	2:15 - 3:00
	Importing Data	3:00 - 3:45
	Q&A	3:45 - 4:15
Day 2	Q&A	12:45 - 1:00
	Selecting and Filtering Data	1:00 - 1:45
	Working with Columns	1:45 - 2:30
	Break	2:30 - 2:45
	Case Study, pt. 1	2:45 - 3:45
	Q&A	3:45 - 4:15

Day	Topic	Time
Day 3	Q&A	12:45 - 1:00
	Review	1:00 - 1:15
	Summarizing Data	1:15 - 2:00
	Break	2:00 - 2:15
	Summarizing Grouped Data	2:15 - 3:00
	Joining Data	3:00 - 3:45
	Q&A	3:45 - 4:15
Day 4	Q&A	12:45 - 1:00
	Exporting Data	1:00 - 1:30
	Visualizing Data	1:30 - 2:30
	Break	2:30 - 2:45
	Case Study, pt. 2	2:45 - 3:45
	Q&A	3:45 - 4:15

Technologies

Binder

• We've developed this class using a product named Binder.
• As a result, this course requires zero setup on your part.
• There are two core techologies within the Binder repository: Python and Jupyter.

We will cover more on this shortly.

Python

• Python is the programming language we'll be learning in this class.
• We are using Python 3.12, the newest version of Python, for the entirety of this class.
• The core libaries we will be using are pandas and seaborn.

Jupyter

• Jupyter is the integrated development environment (IDE) we will be using.
• This is where we will write and run our Python code.

Course Material

• All of the material for this course can be reached from our GitHub repository.

• You can either access this material through Binder or by downloading the material

and opening it via Anaconda Navigator and JupyterLab.

Slides are notebooks

• We will be teaching using slides.

• These slides are created from the notebooks in the course repository -- so you can follow along and run the code in your notebook.

Source Code

• Source code for the training can be found on GitHub.

• This repository is public so you can clone (download) and/or refer to the materials at any point in the future.

Questions

Are there any questions before moving on?