Descriptive Statistics in Python - Step by step guide

• Complete the problems using descriptive statistics and Python.
• Then, understand a new dataset.
• Use the knowledge learned to describe it statistically.

🌱 How to start this project

Follow the instructions below:

1. Create a new repository by forking the Git project or by clicking here.
2. Open the newly created repository in Codespace using the Codespace button extension.
3. Once the Codespace VSCode has finished opening, start your project by following the instructions below.

📝 Instructions

1. Once you start working on the project, you will see a ./notebook/problems.ipynb file containing a list of exercises.

2. Before starting, make sure to select the appropriate Kernel.

   • When you open the notebook, a message will appear at the top indicating "Select Kernel".
   • Click on "Select Kernel" (as shown in the image).

3. A list with available options will be displayed. Select "Python Environments" and choose the Python version you want to use.

   • Make sure to select the version specified in the devcontainer.json file, as this is the recommended one for the project.

Note: We also incorporated a ./notebook/solutions.ipynb file that we strongly suggest you only use if you are stuck for more than 30 min or if you have already finished and want to compare it with your approach.

🚛 How to deliver this project

Once you have finished solving the exercises, be sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link.

🚀 Make Your Work Visible

You worked with a real dataset from the IMDb portal and applied descriptive statistics to analyze something uncommon: the length of movie titles. Through measures of central tendency, dispersion, and distribution shape, you discovered how cinema communicates even through its names.

What to Share?

Post an interesting insight from your results, such as how long the most common titles are, how dispersed they are, or which title is the longest. Accompany it with a chart.

✨ Postable Example

"What can statistics tell us about movie titles?
I analyzed 1,000 real titles from the IMDb dataset and discovered this with Python:
• Average length: 23 characters
• Mode: 15 characters
• The longest title exceeds 60 characters

Most titles are short, but some extremely long ones create a clear positive skew. Cinema can also be analyzed with data! #DataScience #Python #Statistics #IMDb #Visualization #Storytelling"