The Analytics Engineering Roundup

The Analytics Engineering Roundup

Share this post

The Analytics Engineering Roundup
The Analytics Engineering Roundup
Agile Analytics. GDPR. Netflix Interview Questions. 10 Reasons Enterprises Suck at Data. [DSR #137]
User's avatar
Discover more from The Analytics Engineering Roundup
The internet's most useful articles on analytics engineering and its adjacent ecosystem. Curated with ❤️ by Tristan Handy.
Over 29,000 subscribers
Already have an account? Sign in

Agile Analytics. GDPR. Netflix Interview Questions. 10 Reasons Enterprises Suck at Data. [DSR #137]

Tristan Handy's avatar
Tristan Handy
May 27, 2018

Share this post

The Analytics Engineering Roundup
The Analytics Engineering Roundup
Agile Analytics. GDPR. Netflix Interview Questions. 10 Reasons Enterprises Suck at Data. [DSR #137]
Share

❤️ Want to support us? Forward this email to three friends!

🚀 Forwarded this from a friend? Sign up to the Data Science Roundup here.

The Week's Most Useful Posts

How will the GDPR Impact Machine Learning?

Answers to the three most commonly asked questions about maintaining GDPR-compliant machine learning programs.

Coming off a week of privacy policy notices, this is probably the single most important post you need to read. It’s an excellent, and very digestible, summary of what you need to know if you’re currently doing machine learning on top of any personal data.

www.oreilly.com • Share

Agile Analytics, Part 1: The Good Stuff

Really great post that reflects the realities on the ground. The author runs the analytics team @ Harry’s.

Agile software engineering practices have become the standard work management tool for modern software development teams. Are these techniques applicable to analytics, or is the nature of research prohibitively distinct from the nature of engineering? In this post I am going to explore some of the pros of using a scrum-like work management process in analytics.

We use scrum to deliver all of our work at Fishtown Analytics and absolutely wouldn’t do it any other way. If you’re not using scrum on your team, this post is a must-read.

www.locallyoptimistic.com • Share

Netflix Data Science Interview Questions

Vimarsh Karbhari has a small publication, Acing AI, that shares information on the data science interviewing processes of major tech companies. His most recent article focuses on Netflix and covers the required background reading you need to do prior to the interview, as well as 18 interview topic areas that you should be ready to address.

Whether or not you are looking for a new role today, this list is a great barometer of the major knowledge areas that one of the best data science organizations in the world cares about. Could you walk into this interview today?

If you found this article useful, check out similar articles for Microsoft, Linkedin, and more at Acing AI.

medium.com • Share

Top 20 R Libraries for Data Science in 2018

If you’re an R user, this summary is worth a look. There were a couple of surprises for me—dplyr has really grown a ton in the past year, as has plot.ly—so it might be useful to update your priors on the ecosystem.

www.kdnuggets.com • Share

Ten Red Flags Signaling Your Analytics Program Will Fail

I often throw shade at bigco efforts in data, but have come to very much respect just how hard their task is. Doing analytics at a F500 company is not (primarily) a technology problem, it’s a coordination problem. When you have 25 (or even 250) people at your company, this coordination / communication problems is tractable. At 25,000, it is exponentially harder.

This article is most useful when read as a piece of anthropology. It could just as easily be titled Top 10 Reasons Why Large Enterprises Suck at Data. As you read it, just absorb just how ineffective many of these efforts are:

…a large organization spent hundreds of millions of dollars and more than two years on a company-wide data-cleansing and data-lake-development initiative. The objective was to have one data meta-model—essentially one source of truth and a common place for data management. The effort was a waste.

That hurts my soul. It’s clearly possible to have a large company that excels at data, but it seems like that competency often needs to be baked in from the beginning, deep into the culture.

www.mckinsey.com • Share

Visualizing Pandas' Pivoting and Reshaping Functions

Visualizing Pandas' Pivoting and Reshaping Functions

Learning pandas after learning SQL, many of the data-reshaping functions may seem a bit foreign. This extremely brief, wholly visual explanation is extremely useful / memorable.

jalammar.github.io • Share

Mikhail Popov

@bearloga

My team is currently considering the differences in the roles & titles “data analyst” vs “data scientist” – which vary greatly by industry & org – and trying to define these internally to establish responsibilities & expectations.

What do y’all personally see as the differences?

6:20 PM - 22 May 2018

Following up on the recent Lyft “data analyst vs data scientist” post, this thread on Twitter generated a lot of interesting replies recently. Mikhail is an analyst at the Wikimedia Foundation. Click through for the whole conversation with a lot of smart people.

Road Map for Choosing Between Statistical Modeling and Machine Learning

Here are some rough guidelines that attempt to help researchers choose between the two approaches for a prediction problem.

This is the clearest, most concise post I’ve seen on this important and frequently-screwed-up decision.

www.fharrell.com • Share

Thanks to our sponsors!

Fishtown Analytics: Analytics Consulting for Startups

At Fishtown Analytics, we work with venture-funded startups to implement Redshift, Snowflake, Mode Analytics, and Looker. Want advanced analytics without needing to hire an entire data team? Let’s chat.

fishtownanalytics.com • Share

Stitch: Simple, Powerful ETL Built for Developers

Developers shouldn’t have to write ETL scripts. Consolidate your data in minutes. No API maintenance, scripting, cron jobs, or JSON wrangling required.

www.stitchdata.com • Share

By Tristan Handy

The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.

Tweet Share

If you don't want these updates anymore, please unsubscribe here.

If you were forwarded this newsletter and you like it, you can subscribe here.

Powered by Revue

915 Spring Garden St., Suite 500, Philadelphia, PA 19123


Subscribe to The Analytics Engineering Roundup

Launched 4 years ago
The internet's most useful articles on analytics engineering and its adjacent ecosystem. Curated with ❤️ by Tristan Handy.

Share this post

The Analytics Engineering Roundup
The Analytics Engineering Roundup
Agile Analytics. GDPR. Netflix Interview Questions. 10 Reasons Enterprises Suck at Data. [DSR #137]
Share

Discussion about this post

User's avatar
Macroeconomics and the data industry
A measured look at what the economy means for data practitioners.
Apr 10, 2022 • 
Tristan Handy
19

Share this post

The Analytics Engineering Roundup
The Analytics Engineering Roundup
Macroeconomics and the data industry
Is the "Modern Data Stack" Still a Useful Idea?
My vote: no.
Feb 11, 2024 • 
Tristan Handy
110

Share this post

The Analytics Engineering Roundup
The Analytics Engineering Roundup
Is the "Modern Data Stack" Still a Useful Idea?
24
The Roundup roundup: 2022 Edition
We linked, you clicked!
Jan 1, 2023 • 
Anna Filippova
13

Share this post

The Analytics Engineering Roundup
The Analytics Engineering Roundup
The Roundup roundup: 2022 Edition

Ready for more?

© 2025 dbt Labs Inc.
Privacy ∙ Terms ∙ Collection notice
Start writingGet the app
Substack is the home for great culture

Share

Create your profile

User's avatar

Only paid subscribers can comment on this post

Already a paid subscriber? Sign in

Check your email

For your security, we need to re-authenticate you.

Click the link we sent to , or click here to sign in.