Python for Business


This workshop is designed to help business students acquire data processing skills that are:

through using Python as an adjunct for (or replacement to) a spreadsheet. We cover dealing with tabular data, basic statistical tests, plotting, pivot tables, and more.


Learners need to understand the concepts of files and directories (including the working directory) and how to start a Python interpreter before tackling this lesson. This lesson references the Jupyter (IPython) Notebook although it can be taught through any Python interpreter. The commands in this lesson pertain to Python 3.

Getting Started

To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions.


Setup Download files required for the lesson
00:00 1. Introduction to Jupyter Notebooks What is a Jupyter notebook and how does one access a notebook?
How does one create a new notebook?
How does one open an existing notebook?
How does one execute programs using a notebook?
00:25 2. Introduction to Python What is a program and why do we write programs?
What is Python?
How are variables used in programming?
What are the basic data types and containers used in Python?
01:10 3. Controlling Program Behavior How can my programs do different things based on data values?
How can I do the same operations on many different values?
02:40 4. Working with Tabular Data How do I read a file
What is pandas
How to work with table-like data in Python like Excel
03:40 5. Python basics 3 - Functions and Modules How can I define new functions?
What’s the difference between defining and calling a function?
What happens when I call a function?
What are modules? How could I use pre written functions?
04:20 6. Working with Tabular Data How do I read a file
What is pandas
How to work with table-like data in Python like Excel
05:20 7. Statistical Analysis and Visualization How can I perform statistical analysis in Python?
How to visualize my data
06:00 8. Extra - Errors and Exceptions How does Python report errors?
How can I handle errors in Python programs?
06:30 9. Extra - Debugging How can I debug my program?
07:00 10. Data Preparation techniques How do I load data to python?
How to clean up data?
How to handle missing values?
How to create new features?
08:00 11. Linear regression How to construct linear regression in python?
Continuous or Categorical?
How to interpret regression result?
09:00 12. Introduction to CRISP-DM How to approach a seemingly chaotic data analytics problem?
What is the most time consuming step in CRISP-DM?
09:30 13. Case Study How to apply my python skills on real world data?
10:00 14. Case Study 2 - Yellow Cab Chicago Case How to extract and deliver valuable information from data
14:00 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.