Cleaning / Tidying / Munging / Wrangling
The principles of tidy data provide a standard way to organize data values within a dataset.
An exhaustive reference to problems seen in real-world data along with suggestions on how to resolve them.
The Log: What every software engineer should know about real-time data’s unifying abstraction
Opinionated python
In this article, I will offer an opinionated perspective on how to best use the Pandas library for data analysis. My objective is to argue that only a small subset of the library is sufficient to complete nearly all of the data analysis tasks that one will encounter. This minimally sufficient subset of the library will benefit both beginners and professionals using Pandas.
The Little Book of Python Anti-Patterns
What’s the future of the pandas library?
Fast Pandas: A Benchmarked Pandas Cheat Sheet
Loop Better: A Deeper Look at Iteration in Python
A Visual Intro to NumPy and Data Representation
Learn a new pandas trick every day
Jupyter
Bringing the best out of Jupyter Notebooks for Data Science
Reproducible Data Analysis in Jupyter
Joel Grus’ I Don’t Like Notebooks Slides
Visualization
Fundamentals of Data Visualization (book)
Git
A guide for astronauts (now, programmers using Git) about what to do when things go wrong.
Learn git concepts, not commands
A guided tour that walks through the fundamentals of Git, inspired by the premise that to know a thing is to do it.
How to Write a Git Commit Message
SQL
Learning SQL 201: Optimizing Queries, Regardless of Platform
Analyzing 89 Responses to a SQL Screener Question for a Senior Data Analyst Position
SQL Window Functions to Pass a Data Analytics Interview
The Most Underutilized Function in SQL (md5)
Data Build Tool (dbt)
Build Your Data Analytics Like An Engineer (podcast)
On the limits of incrementality
Data Warehouses
How Compatible are Redshift and Snowflake’s SQL Syntaxes?
The R.A.G (Redshift Analyst Guide)
Looker
How to Design Your Looker Explores
Is Looker the Right Business Intelligence Tool for My Company?