Python for Business
Welcome
This workshop is designed to help you (business students) go through some basic Python programming skills.
Many of you are Excel experts. In this workshop, you will learn how to deal with table-like data in Python, without a graphical interface.
The workshop covers a lot of materials. So don’t worry if you can’t remember everything in the end. We will help you go through the material once, and you can always review the website in the future.
Prerequisites
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.
Schedule
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction to Jupyter Notebook |
What is Jupyter notebook?
How to create new notebook? How to open a notebook? How to execute notebook cells |
| 00:30 | 2. Python basics 1 - Intro to Python |
What is Python?
What are variables? |
| 01:00 | 3. Python basics 2 - Conditional statements and Loops |
How can my programs do different things based on data values?
How can I do the same operations on many different values? |
| 01:40 | 4. 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? |
| 02:20 | 5. Intro to Pandas |
How do I read a file
What is pandas How to work with table-like data in Python like Excel |
| 03:20 | 6. Statistical Analysis and Visualization |
How can I perform statistical analysis in Python?
How to visualize my data |
| 04:00 | 7. Extra - Errors and Exceptions |
How does Python report errors?
How can I handle errors in Python programs? |
| 04:30 | 8. Extra - Debugging | How can I debug my program? |
| 05:00 | 9. Introduction to CRISP-DM |
How to approach a seemingly chaotic data analytics problem?
What is the most time consuming step in CRISP-DM? |
| 05:30 | 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? |
| 06:30 | 11. Linear regression |
How to construct linear regression in python?
Continuous or Categorical? How to interpret regression result? |
| 07:30 | 12. Case Study | How to apply my python skills on real world data? |
| 08:00 | 13. Case Study 2 - Yellow Cab Chicago Case | How to extract and deliver valuable information from data |
| 12:00 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.