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:
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Learn to use control flow and custom functions to work with data more efficiently.
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Build awareness and basic skills in working with Python from the shell and its environments.
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Exposure to Python's data science ecosystem and modeling via scikit-learn.
Students should have attended the Introduction to Python for Data Science training or have previously used Python and the Pandas package for data analysis in a professional environment.
This workshop offering will be 100% virtual over 4 half-days.
| Day | Topic | Time |
|---|---|---|
| 1 | Introductions | 12:45 - 1:00 |
| Setting the Stage | 1:00 - 1:30 | |
| Conditions | 1:30 - 2:15 | |
| Break | 2:15 - 2:30 | |
| Iterations | 2:30 - 3:45 | |
| Q&A | 3:45 - 4:15 | |
| 2 | Q&A | 12:45 - 1:15 |
| Functions | 1:15 - 2:15 | |
| Applying Functions to pandas Data Frames | 2:15 - 2:45 | |
| Break | 2:45 - 3:00 | |
| Case Study, pt. 1 | 3:00 - 4:00 | |
| Q&A | 4:00 - 4:15 | |
| 3 | Q&A | 12:45 - 1:15 |
| Case Study Review, pt. 1 | 1:15 - 1:45 | |
| Python from the Shell | 1:45 - 2:45 | |
| Break | 2:45 - 3:00 | |
| Kernels and Environments | 3:00 - 3:45 | |
| Python Data Science Ecosystem | 3:45 - 4:00 | |
| Q&A | 4:00 - 4:15 | |
| 4 | Q&A | 12:45 - 1:15 |
| Modeling with scikit-learn | 1:15 - 2:15 | |
| Case Study, pt. 2 | 2:15 - 3:30 | |
| Case Study Review, pt. 2 | 3:30 - 4:00 | |
| Q&A | 4:00 - 4:15 |
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:
These easiest way to install Python, Jupyter, and the necessary packages is through Anaconda. To download and install Anaconda and its graphical interface, Anaconda Navigator, follow these steps:
- Visit the Anaconda download page.
- Select your appropriate operating system.
- Click the "Download" button for Python 3.8 - this will begin to download the Anaconda installer.
- Open the installer when the download completes, and then follow the prompts. If you are prompted about installing PyCharm, elect not to do so.
- Once installed, open the Anaconda Navigator and launch a Jupyter Notebook to ensure it works.
- Download the class materials (see the below section) and use the included
environment.yamlfile to create a new environment from Anaconda Navigator, using these steps:
- In the tabs along the left side, select "Environments".
- At the bottom of the list of environments (you will likely have just one, "base"), look for the "Import" button. Click it.
- In the dialog box that appears, click on the folder icon and then navigate your computer's files in order to select the
environment.yamlfile you downloaded earlier. Click "Open" once you've selected it. - Wait for Anaconda Navigator to finish fetching and installing the needed packages. When it finishes, a new environment called "uc-python" should show up in the list.
There are two ways to download the class materials:
- Clone it - If you're familiar with how to do so, you can clone this repository.
- 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: