This is a multipart series to walk you through the crucial data engineering tools and skills

Many of these labs are multi-part series, and many of the series build on the previous one. So, in case there's any confusion, the proper order is:

Name		Name	Last commit message	Last commit date
Latest commit History 95 Commits
.github		.github
AWS - Part 1		AWS - Part 1
AWS - Part 2		AWS - Part 2
Getting Data - Part 1		Getting Data - Part 1
Getting Data - Part 2		Getting Data - Part 2
Matplotlib - Part 1 (Intro)		Matplotlib - Part 1 (Intro)
Pandas - Part 1 (importing data, summary stats)		Pandas - Part 1 (importing data, summary stats)
Pandas - Part 2 (column munging, selections)		Pandas - Part 2 (column munging, selections)
Pandas - Part 3 (feature engineering)		Pandas - Part 3 (feature engineering)
Pandas - Part 4 (df problems, missing data)		Pandas - Part 4 (df problems, missing data)
Pandas - Part 5 (handling missing data - continued)		Pandas - Part 5 (handling missing data - continued)
Spark - Part 1		Spark - Part 1
Unstructured Data - Part 1		Unstructured Data - Part 1
Unstructured Data - Part 2		Unstructured Data - Part 2
Web Scraping - Part 1		Web Scraping - Part 1
Web Scraping - Part 2		Web Scraping - Part 2
Web Scraping - Part 3		Web Scraping - Part 3
.gitignore		.gitignore
Combined Wine Data.csv		Combined Wine Data.csv
README.md		README.md

Provide feedback