Advanced Python in Excel: Data Analysis and Visualization

This is the repository for the LinkedIn Learning course Advanced Python in Excel: Data Analysis and Visualization. The full course is available from LinkedIn Learning.

In this course, learn how to use Python to go beyond Excel’s limited functionalities to perform complex data analysis and visualizations. Instructor Sarah Om details how Python’s integrations with Excel bring new dimensions of functionality, allowing for more advanced data manipulation, automation, and visualization than ever before. Sarah shows you how to leverage Python’s powerful libraries for data analysis to transform raw data into meaningful insights and stunning visualizations—right from the familiar Excel environment.

Capstone Project Instructions

Objective

For the Capstone, I invite you to take the following approach to deepen your understanding and application of Python in Excel.

Steps to Follow

1. Refine the Dataset:

   • Continue to clean and transform additional variables in the dataset.
   • Ensure the data is well-prepared for analysis.

2. Apply Predictive Capabilities:

   • Utilize your predictive knowledge to refine views for a specific city or neighborhood in Los Angeles.
   • Modify the code provided in this chapter to return a filtered view for your chosen area.

   Hint: You may not have enough data points for individual cities. Consider grouping cities creatively to produce meaningful results.

3. Consider the Community Member Persona:

   • Think deeply about what information would help a community member understand the safety of their neighborhood better.
   • Identify any additional data points that might be valuable for this purpose.

4. Develop Visualizations:

   • Explore other visualizations that could provide better insights.
   • Develop new visualizations that might be worth presenting.

5. Make the Data More Actionable:

   • Substitute the current dataset with the original dataset (or some larger representative sample) that includes a wider time range of reported incidents. Remember, the dataset has been intentionally reduced to help the code run quicker, so ensure your code can handle the larger dataset.

Installing

You may choose to clone this repository to your local machine or fork it to your GitHub account.

Cloning the Repository

Cloning a repository means creating a copy of the repo on your local machine. You can do this using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.

Using the Terminal (Mac) or CMD (Windows)

1. Open the Terminal (Mac) or CMD (Windows):

   • On Mac, press Cmd + Space, type "Terminal," and press Enter.
   • On Windows, press Win + R, type "cmd," and press Enter.

2. Navigate to the directory where you want to clone the repository:

   cd /path/to/your/directory
   

3. Clone the repository:

   git clone https://github.com/username/repository-name.git

   Replace https://github.com/username/repository-name.git with the actual URL of the repository.

Forking the Repository

Forking a repository creates a personal copy of the repository on your GitHub account. This allows you to freely experiment with changes without affecting the original project.

1. Go to the GitHub repository page.
2. Click on the "Fork" button at the top right of the page.
3. Choose your GitHub account as the destination for the forked repository.

After forking, you can clone the forked repository to your local machine using the steps described above for cloning.

Instructor

Sarah Om

Data Analyst and Educator

Check out my other courses on LinkedIn Learning.