Hello! Welcome to my first monthly curation of top Data Science resources that I’ve come across this month. I wanted to do something like a newsletter, but not as intense and something that gives immediate value.
Every month, I’m going to share with you some of the best reads that I’ve come across and group them in themes/topics – This month, I wanted to share with you some articles on SQL tricks and tips, world-class experimentation platforms, and a free resource with over 15 graduate courses on various statistics topics.
With that said, I hope you enjoy and please leave a comment if this is something that you’re interested in following on a monthly basis!
SQL Tricks and Tips
More dangerous subtleties of JOINs in SQL
Whether you’re a beginning or an expert, I highly recommend that you give this a read. This article talks about a small but profound nuance in SQL joins that I actually didn’t realize until last year. If you’re not aware of this subtlety, this small mistake can result in MASSIVE costs and repercussions for your company.
I don’t want to spoil the article completely, but there is a common misconception about how SQL joins work – for example, many people think that a left join will return the same amount of rows in the left table. This is NOT the case. A left join gives at least as many rows as the left table.
BigQuery: Date Sharding vs. Date Partitioning
If you’re a data analyst, data scientist, data engineer, data architect, or basically anyone that is writing data pipelines and creating table views, these next two articles are extremely valuable.
Sharding and Partitioning are two concepts that will help you write queries and create tables that are more scalable and efficient – this means less time for queries to run and less money for each query!
Learning how to create sharded/partitioned tables will separate you from the rest of the pack, so I highly recommend giving this a read.
BigQuery Partitioning & Clustering
Similarly, there’s another concept called clustering, which serves a similar purpose as partitioning. If you found the last article valuable, it’s highly recommended that you give this one a read as well.
World Class Experimentation Platforms
As I get further into my career, I realize more and more how important experimentation is. Experimentation goes far beyond the theory behind hypothesis testing and A/B testing. The two articles below demonstrate the ideal end result that ALL companies should strive for when it comes to experimentation.
Experiments at Airbnb
This article is a great introduction to experimentation. It focuses less on the platform that Airbnb has built and more on the lessons and learnings that Airbnb has accumulated over its life.
Why I love this article is that it shows you a glimpse of all of the nuances and intricacies that you have to be aware of and take into consideration when it comes to experimentation.
Under the Hood of Uber’s Experimentation Platform
This article has so much valuable information that I can’t believe it’s free and accessible for others to see. Uber covers several things in this article:
- They cover all of the experimentation methodologies that they use on a daily basis
- They give a glimpse into their statistics engine and experimentation platform
- And they also cover several conditions and factors that they look out for when conducting experiments.
Over 15 Free Graduate Courses on Statistics
Everyone loves free courses.
This is a website with over 15 statistics graduate courses for free.
It’s a great resource that I’ve leveraged in my learning journey, and I wanted to leave this with you guys in case you ever feel the need to learn more statistics.
Here are some courses that they offer:
- Analysis of Variance and Design of Experiments
- Applied Data Mining and Statistical Learning
- Applied Time Series Analysis
- Sampling Theory and Methods
- and more…
Thanks for Reading!
I hope you enjoyed this curated Reading and resource list! If you enjoyed this, please give this some claps and leave a comment for what topics you’d like to see in the upcoming months!
As always, I wish you the best in your learning endeavors.
Not sure what to read next? I’ve picked another article for you:
A Complete 52 Week Curriculum to Become a Data Scientist in 2021
and another one!
10 Statistical Concepts You Should Know For Data Science Interviews
Terence Shin
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