Skip to content
Branch: master
Find file Copy path
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
83 lines (57 sloc) 5.54 KB

Intermediate Python for Data Science


Course Description

Welcome to Intermediate Python for Data Science! This short course provides a more detailed information on how programming with Python can make working with data easier and a deeper dive into the Python data science ecosystem. You will learn the to program more efficient data science applications using Python using control flow and custom functions, gain comfortability with Python from the shell, and be exposed to a few of the leading data science packages of Python like scikit-learn for predictive modeling.


The following are the primary learning objectives of students:

  1. Learn to use control flow and custom functions to work with data more efficiently.

  2. Build awareness and basic skills in working with Python from the shell and its environments.

  3. Exposure to Python's data science ecosystem and modeling via scikit-learn.


Day 1

Topic Time
Breakfast / Social Time 8:00 - 9:00
Introductions 9:00 - 9:15
Setting the Stage 9:15 - 9:45
Conditions 9:45 - 10:30
Break 10:30 - 10:45
Iterations 10:45 - 12:00
Lunch 12:00 - 1:00
Functions 1:00 - 2:00
Applying Functions to pandas Data Frames 2:00 - 2:30
Break 2:30 - 2:45
Case Study, pt. 1 2:45 - 4:00
Q&A 4:00 - 4:30

Day 2

Topic Time
Breakfast / Social Time 8:00 - 9:00
Case Study Review, pt. 1 9:00 - 9:30
Python from the Shell 9:30 - 10:30
Break 10:30 - 10:45
Kernels and Environments 10:45 - 11:30
Python Data Science Ecosystem 11:30 - 12:00
Lunch 12:00 - 1:00
Modeling with scikit-learn 1:00 - 2:00
Case Study, pt. 2 2:00 - 3:15
Case Study Review, pt. 2 and Q&A 3:15 - 4:00

Course Preparation

In an effort to simplify the setup for this class, we are using Binder for all in-class materials (slides, worksheets, etc.). In result, there is no pre-requisite installation required for the in-class material.

With that being said, we recommend installing the appropriate technologies and downloading the course materials. This will be more stable in the event of network issues, and it will also be required to apply your learnings outside of class.

Follow these steps to download the technologies and materials:

1. Python, Jupyter and package installation.

These easiest way to install Python, Jupyter, and the necessary packages is through Anaconda. To download and install Anaconda:

  1. Visit the Anaconda download page
  2. Select your appropriate operating system
  3. Click the "Download" button for Python 3.7 - this will begin to download the Anaconda installer
  4. Open the installer when the download completes, and then follow the prompts. If you are prompted about installing PyCharm, elect not to do so.
  5. Once installed, open the Anaconda Navigator and launch a Jupyter Notebook to ensure it works.
  6. Follow the package installation instructions to ensure pandas, numpy, scikit-learn, and seaborn packages are installed.

2. Download class materials

There are two ways to download the class materials:

  1. Clone it - If you're familiar with how to do so, you can clone this repository.
  2. Download the files as a zip - use this link

If you have any specific questions prior to the class you can reach out to us directly via GitHub or email:

You can’t perform that action at this time.