SQL

All things data newsletter #11 (#dataengineer, #datascience)

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(if this newsletter was forwarded to you then you can subscribe here: https://insightextractor.com/)

The goal of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

1. AWS re:Invent ML, Data and Analytics announcements

Really good recap of all ML, Data and Analytics announcements at AWS reinvent 2020 here

2. How to build production workflow with SQL modeling

A really good example of how a data engineering at Shopify applied software engineering best practices to analytics code. Read here

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3. Back to basics: What are different data pipeline components and types?

Must know basic concepts for every data engineer here

4. Back to basics: SQL window functions

I was interviewing a senior candidate earlier this week and it was unfortunate to basic mistakes while writing SQL window functions. Don’t let that happen to you. Good tutorial here

5. 300+ data science interview questions

Good library of data science interview questions and answers

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Thanks for reading! Now it’s your turn: Which article did you love the most and why?

All things data newsletter #10 (#dataengineer #datascience)

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(if this newsletter was forwarded to you then you can subscribe here: https://insightextractor.com/)

The goal of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

1. Architecture for Telemetry data

A good reminder that the software development architecture can be significantly simplified for capturing telemetry data here

2. 5 popular job titles for data engineers

This post here lists 5 popular job titles: data engineer, data architect, data warehouse engineer — I think Analytics engineer is missing in that list but a good post nonetheless. I hope that we get some consolidation and standardization of these job titles over the next few cycles.

3. [Podcast] startup growth strategy and building Gojek data team – Crystal Widjaja

Really good podcast, highly recommended! here

4. Tenets for data cleaning

A must-read technical whitepaper from legendary Hadley Wickham. These principles form the foundation on top of which R software gained a lot of momentum for adoption. Python community uses similar tenets. Must read! here and here

5. Magic metrics that startup probably as product/market fit from Andrew Chen

A must-follow Growth leader!

  1. Cohort Retention curves flatten (stickiness)
  2. Actives/Reg > 25% (validates TAM)
  3. power user curve showing a smile

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TelemetryTiers
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Thanks for reading! Now it’s your turn: Which article did you love the most and why?

How do I learn #SQL for #data analysis?

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Step 1:

This is a good starting point: SQL School Table of Contents

OR, this: Learn SQL

Both of these resources were put together by analytics vendor and is targeted towards beginners.

Step 2:

Review this Quora Thread: How do I learn SQL?

Participate in competitions like this: Solve SQL Code Challenges

Step 3:

If you like to go more in-depth then check out few books:

  1. Head First SQL
  2. Learn SQL the hard Way
  3. Certification books/material from a database vendor

Hope that helps!

VIEW QUESTION ON QUORA