Month: November 2020

All things data newsletter #9 (#dataengineer, #datascience)

Standard

(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 The Great Data Debate by a16z

a16z is top venture capital firm and they recently published this amazing podcast. Must listen! here

2 Zen of Pyhon!

some really good tenents that Python community lives by! Read here

Some of my favorites: “Practicality beats purity” and “if it’s hard to explain, it’s a bad idea”

3 Super intelligence: science or fiction?

A bit outdated (2017) but still a really fun conversation to listen to. Speakers include Elon Musk, Stuart Russell, Ray Kurzweil, Demis Hassabis, Sam Harris, Nick Bostrom, David Chalmers, Bart Selman, and Jaan Tallinn.

Watch here:

4 MUST READ! Data Quality at Airbnb; part 2

I included Part 1 in the previous newsletter #8 and in this one, you have the link to part 2 here

5 Some must know SQL concepts

Good list by Eric Weber on LinkedIn here

Elon Musk on Artificial Intelligence - YouTube
Source

—

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

All things Data Engineering & Data Science Newsletter #8

Standard

(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.

What is a data lake?

Good article on basics of data lake architecture on guru99 here

Data quality at Airbnb

Really good framework on how to think about data quality systematically through examples and mental-model from Airbnb here

Monetization vs growth is a false choice

Good article from Reforge for Monetization vs growth mental model here

Performance Tuning SQL queries

Really good basic post on tuning SQL queries here

Improving conversion rates through A/B testing

Good mental model to run effective A/B testing to improve metrics such as conversion rate here

Source: Difference Media Variations for A/B testing

—

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

All things data engineering & science newsletter #7

Standard

(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. Why a data scientist is not a data engineer?

Good post on the difference between data engineer and data scientist and why you need both roles in a data team. I chuckled when one of the sections had explanations around why data engineering != spark since I completely agree that these roles should be boxed around just one or two tools! read the full post here

2. Correlation vs Causation:

1 picture = 1000 words!

No alternative text description for this image
Image Source
3. Best Practices from Facebook’s growth team:

Read Chamath Palihapitiya and Andy John’s response to this Quora question here

4. Simple mental model for handling for handling “big data” workloads
No alternative text description for this image
Image Source
5. Five things to do as a data scientist in firt 90 days that will have big impact.

Eric Weber gives 5 tips on what to do as a new data scientist to have a big impact. Read here

—

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