Pandas is an open source data analysis and manipulation tool. It is important to learn if you are interested in data science.

We just published a course on the freeCodeCamp.org YouTube channel that will teach you how to use Pandas through interactive projects. You will develop 7 projects ranging from the basics of Pandas for Data Analysis, to Data Cleaning and Data Wrangling.

This course targets everyone, from data science enthusiasts to professionals, aiming to refine their skills in data analysis, data cleaning, and data wrangling using Pandas and Python.

Santiago Basulto developed this course. His is knowledgeable and has an engaging teaching style, guarantees an enriching learning experience. Santiago is also the creator of datawars.io, a platform that offers a bunch of interactive data science projects.

## A Sneak Peek into the Course

The course is designed to provide you with hands-on experience through real-life projects. The projects vary in complexity, catering to learners with different skill levels. It’s advisable to try resolving the projects independently and then compare your solutions with Santiago’s.

Below are the projects you will build.

### For Beginners:

DataFrames Practice: Working with English Words: This project is an excellent entry point for beginners. You will get acquainted with the basics of Pandas DataFrames, focusing on understanding and manipulating their structures, all while working with an extensive dictionary of English words.

Filtering and Sorting with Pokemon Data: Dive into the captivating world of Pokemon as you perform fundamental data analysis tasks like filtering and sorting. This project is not only educational but also fun, making it perfect for those just starting.

### Intermediate Level:

The Birthday Paradox in the NBA: Have you heard of the Birthday Paradox? Discover the answer to an intriguing question: How many people need to be in a room to have a 50% probability that at least two people share a birthday? Apply these insights to explore shared player birthdays within NBA teams.

Matching Strings by Similarity using Levenshtein Distance: String handling is an integral aspect of data cleaning. This project introduces advanced techniques such as Combinatorics and the Levenshtein distance to detect irregularities in company names.

Data Cleaning with Google Playstore Dataset: This project is a comprehensive guide to data cleaning. Learn how to identify and rectify null values, duplicate values, outliers, and more, using a dataset scraped from the Google Playstore, which is replete with irregularities.