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Using ten NBA seasons of NBA data from 2010-2019 to practice web scrapping, data cleaning, and wrangling. The scrapped information includes teams, coaches, champions, and players. Some of this data is going to be used for a finals winner prediction project. This repository will therefore highlight how to clean data and make notes about why certain decisions about missing data were made.

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nba-data data-cleaning wrangling-cleaning python

ten-years-of-nba-data's Introduction

๐Ÿ”ญ an aspiring data analytics engineer interested who's worked in data analyst, engineering, and data ops roles, most recently becoming a data ops and pipeline development lead. I also love to learn and code anything (and share by teaching) having built applications in Python, Java, and C# (+ a lot of SQL) every so often for fun and an occassional group project. Additionally, I have been published as a writer and editor of creative works at the collegiate level ๐Ÿ“ฐ ๐Ÿ“š.

Currently learning about cloud platforms as they have left us on-premise folks behind. Would love to ๐Ÿ‘ฏ on developing learning opportunities for folks in the data engineering, integration, and automation space, and anything interesting where beginners are invited! If you'd like to connect and exchange ideas in life and data, you can ๐Ÿ“ซ me here! ๐Ÿ˜Š. If you are hiring and would like to reach out, this one is for you!

๐Ÿ˜ Lastly, No WNBA slander accepted here, and I'll volunteer information about it, unprompted ๐Ÿ˜„ ๐Ÿ˜. (Go Lynx! ๐Ÿ’™ ๐Ÿ’š)

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