Pandas & Python for Data Analysis by Example – Full Course for Beginners
Learn how to use Pandas and Python for Data Analysis, to Data Cleaning and Data Wrangling. You will learn by creating real life projects interactively to help you take the next step in your Data Science Career.
Learn more about the projects at https://www.datawars.io/freecodecamp
⚠️ Important! We encourage you to try to resolve the projects by yourself first! And watch the solution afterwards ⚠️
💻 Course created by Santiago Basulto from DataWars.
🔗 Find more interactive Data Science projects solve at: https://www.datawars.io
⭐️ Projects Covered ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:02:22) DataFrames practice: working with English Words [🟢 Easy]
This project focuses on the basics of pandas DataFrames, including understanding its structures and modifying them. The data we're using is a big dictionary of english words.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-english-words
⌨️ (0:33:26) Filtering and sorting with Pokemon data [🟢 Easy]
This project focuses on the main tasks of Data Analysis: filtering and sorting and question/answering. The dataset includes information of pokemons from all generations (to make it more fun!)
🔗 Solve the project by yourself: https://www.datawars.io/fcc-filtering-pokemon
⌨️ (1:24:30) The Birthday Paradox in the NBA [🟡 Intermediate]
The Birthday Paradox answers the question: how many people do you need in the same room in order to have a probability of at least 50% that two people share a birthday. The answer is astonishing! You'll use your findings to find which teams in the NBA share player's birthdays.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-birthday-nba
⌨️ (1:55:28) Matching Strings by Similarity using Levenshtein distance [🟡 Intermediate]
One of the most challenging tasks of Data Cleaning is dealing with Strings. This project is all about string handling and advanced techniques, as using the Combinatorics and the Levenshtein distance to find irregularities in company names.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-string-similarity
⌨️ (2:24:15) Data Cleaning with Google Playstore dataset [🟡 Intermediate]
An all-encompassing project that covers all the aspects of Data Cleaning, including: finding and fixing null values, duplicate values, outliers and more. The data was scraped from the Google Playstore which means that is full of irregularities. The project finishes with some Data Analysis tasks!
🔗 Solve the project by yourself: https://www.datawars.io/fcc-data-cleaning-playstore
⌨️ (3:18:34) Premier League Match Analysis [🔴 Advanced]
This project increases the complexity of your Data Analysis skills, as it combines Data Cleaning, with some analysis based on grouping operations. The dataset comes from the Premier League, the top-division Football/Soccer league in England.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-premier-league
⌨️ (3:53:46) NBA 2017 season analysis: joining and groupby practice [🔴 Advanced]
This project puts your Data Wrangling skills to a test, by asking you to merge different dataframes, clean them, and finish doing some analysis and question/answering. The dataset contains the full information of 2017 NBA statistics.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-nba-analysis
🎉 Thanks to our Champion and Sponsor supporters:
👾 Agustín Kussrow
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👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
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