ren0nie0 / analytics-readings

Readings for Analytics Engineers

Resources for Analytics Engineers (Work in Progress)

This repository is a curation of good blog posts and books for Analytics Engineers. It can also be very useful for Data Analysts and Data Scientists.

Contribute

I really appreciate any contribution. Just make sure to describe the theme and why you found the resource useful.

Table of Contents

• SQL
• Python
• Infrastructure
• Analytics Skills
• Data Warehousing
• Data Pipelines
• Starting analytics in a company
• Testing data
• Success Stories
• Organisation
• Data Visualisation
• Marketing and data
• More rigor for the analyst
• Other readings lists
• Top bloggers/blog

Readings

Definition of the Analytics Engineer: The Analytics Engineer.

SQL

• Learning SQL 201: Optimizing Queries, Regardless of Platform By Randy Au. I finally found a complete post on advanced SQL.

Python

• Python for Data Analysis. Very comprehensive book about using python for data stuff.
• Pandas Cheatsheet I use it everyday!
• TIL Python by Vicki Boykis. Some tricks to deal with some frequent tasks when manipulating data.
• Modern pandas. A series of blog posts on intermediate/advanced pandas written by one of the maintainers.

Infrastructure

• The Startup Founder's Guide to Analytics. An excellent introduction to the stack necessary for analytics and its evolution following the growth of the start-up.
• The missing layer of Analytics Stack.
• Choosing a Data Warehouse. A lot of excellent answers on what to choose for your data warehouse.
• Data science for start-ups. You can find some useful information in this free book.

Comparison of tools by Stephen Levin

• Looker vs Tableau vs Mode. Data Visualisation tools compared. .
• Segment vs Fivetran vs Stitch: Which Data Ingest Should You Use?

Analytics Skills

• One analyst's guide for going from good to great
• Suceeding as the first data person in a small company/startup. A must read for anyone working in data even in a big company.
• Prioritizing data science work. Too many engineers like building ivory towers. Make sure you don't fall in the trap.

Data Warehousing

• The beginner guide to data engineering series. Start here if you don't know what is a star schema, Airflow and some basic practices when writing data pipelines.
• The Data Warehouse Toolkit by Ralph Kimball. A classic in Business Intelligence. Some chapters can be gold on modeling your data warehouse.
• Functional Data Engineering — a modern paradigm for batch data processing. You will learn the spirit behind good data pipelines and a well-designed data warehouse.
• The rise of the Data Engineer. Explains recent evolutions of the job and data practices.
• Five principles that will keep your data warehouse organized
• For Data Warehouse Performance, One Big Table or Star Schema?. Discussion on an alternative to star schema.

Data Pipelines

• Functional Data Engineering — a modern paradigm for batch data processing. You will learn the spirit behind good data pipelines and a well-designed data warehouse.
• Maintenable ETL: Tips for Making Your Pipelines Easier to Support and Extend. Best practices to write good ETL.

Starting analytics in a company

• Building a data practice from scratch. Very useful for your first weeks as a data person.
• The Startup Founder's Guide to Analytics. An excellent introduction to the stack necessary for analytics and its evolution following the growth of the start-up.

Testing data

• Automated Testing In The Modern Data Warehouse. Practical advice to test data. Useful for everyone building data pipelines. Rare to found such a post dealing with non-sexy thing in data.

Success Stories

• Scaling analytics at Wish
• Building Analytics at 500px

Organisation

• Engineer shouldn't write ETL. It's more data science focused but it's a classic.
• Does my startup data team need a data engineer?

Marketing and data

• Data Driven Marketing. Reading some chapters can help you think like a marketer with data driven approach. It's a gem. Didn't find this kind of insights elsewhere.

More rigor for the analyst

These books helped me to think better when analysing data.

• Thinking fast and slow. Learning about bias can be super useful. For instance, I didn't have the reflex to think of a base rate anytime I see a figure.
• Fooled by randomness. Nassim Taleb taught so much both professionally and personnaly. In Fooled By Randomness, you will learn about major pitfalls when dealing with data in real life.
• Why you should care about the Nate Silver vs. Nassim Taleb Twitter war. Great chess players learn from high elo games. Great data people learn from debate between data experts.
• Five books every data scientist should read that are not about data science. I have not read them all yet. But these suggestions seems judicious.

Data Visualisation

• Fundamentals of Data Visualisation. Complete guide to visualisation. Free version online.

Other readings lists

I really love Reading in Applied Data Science. But it is more for data scientists.
The Gitlab data team also made an excellent list. (close to mine)

Top bloggers/blog

• Randy Au. You can read almost all his posts there are all very relevant for analytics engineers.
• Locally Optimistic. A blog dedicated to data in organizations.
• Tristan Handy. I also love his newsletter: Data Science Roundup.

Where is the community?

• Dbt blog
• Twitter
• Locally Optimistic