Introduction to Python for Data Science¶

Gus Powers & Jay Cunningham

March 19, 21, 26, 28

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¶

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Data Science and Technology¶

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Applied Data Science¶

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