Python Statistics Essential Training

This is the repository for the LinkedIn Learning course Python Statistics Essential Training. The full course is available from LinkedIn Learning.

The field of statistics has become increasingly dependent on data analysis and interpretation using Python. With the rise of big data and data science, the demand for professionals who can effectively analyze and interpret data using Python has skyrocketed. In this course, Matt Harrison teaches you how to collect, clean, analyze, and visualize data using the powerful tools of the Python programming language. Join Matt as he gives into the various techniques that form the backbone of statistics and helps you understand the data with summary statistics and visualizations. He explains how to create predictive models using both linear regression andXGBoost, and wraps up the course with a look at hypothesis testing. If you’re interested in exploring statistics using a code-first approach, join Matt in this course as he shows you how to use Python to unlock the power of data.

Instructions

You can run the notebook using:

• Codespaces
• Locally

Running in GitHub Codespaces

You can use GitHub Codespaces to run this codebase in a fully configured, cloud-hosted development environment:

1. Click the green "Code" button on the main repository page.

2. Click on "Open with Codespaces" and then "New codespace."

3. Open up PyStats.ipynb

Running Locally

To run this codebase locally, you'll need to set up a Python virtual environment and install the necessary packages. Follow these steps:

1. Ensure you have Python installed on your system. If not, you can download it from here.

2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.

3. Navigate to the repository folder via your terminal.

4. Create a virtual environment. You can do this by running:

   python -m venv env
   

   This will create a new Python virtual environment in a folder named env.

5. Activate the virtual environment. On macOS and Linux, run:

   source env/bin/activate
   

   On Windows, run:

   .\env\Scripts\activate
   

6. Install the necessary packages using pip by running:

   pip install -r requirements.txt
   

   This will install all the packages listed in requirements.txt to your virtual environment.

7. Launch jupyter

   jupyter lab
   

8. Open up PyStats.ipynb

Solutions

The PyStats-Solutions.ipynb file has the solutions for the exercises.

Instructor

Matt Harrison

Check out my other courses on LinkedIn Learning.