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

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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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

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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.

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

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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. 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!

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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
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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?

Data Engineering and Data Science Newsletter #4

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The purpose of this Insight Extractor’s 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 articles made the cut for today’s newsletter.

1. What does a Business Intelligence Engineer (BIE) do in Amazon?

Have you wondered what Analytics professionals at Top tech companies work on? Are you job hunting and wondering what data roles (data engineer, data science, or Bi engineer) at Amazon are a great fit for your profile? If so, read Jamie Zhang’s (Sr Business Intelligence Engineer at Amazon) post here

2. What are the 2 Data & Analytics Maturity models that you should absolutely know about?

If you have read my blog, you know that I am a fan of mental models. So, here are 2 mental models (frameworks) shared by Greg Coquillo that are worth reading/digesting here

3. Using Machine Learning to Predict Value of Homes On Airbnb

Really good case study by Airbnb Data scientist Robert Chang here

4. How Netflix measures product succes?

Really good post on how to define metrics to prove or disprove your hypotheses and measure progress in a quick and simple manner. To do this, the author, Gibson Biddle, shares a mechanism of proxy metrics and it’s a really good approach. You can read the post here

Once you read the post above, also suggest learning about leading vs lagging indicators. It’s a similar approach and something that all data teams should strive to build for their customers.

5. Leading vs lagging indicators

Kieran Flanagan and Brian Balfour talk about why your north star metric should be a leading indicator and if it’s not then how to think about it. Read about it here

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

Data Maturity Mental Model Screenshot:

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Insight Extractor’s Data Engineering and Science Newsletter #2

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The purpose 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. Following articles made the cut for today’s newsletter:

  1. Amazing data storytelling example from Ben Evans. Ben starts from a basic premise around “Amazon is not profitable” that a lot of people argue about. He then goes on a data storytelling journey with publicly available data-sets around his chosen premise. Must read! here
  2. What kind of data scientist am I? Elena Greval from Airbnb wrote this excellent article in 2018 but it’s still relevant to understand 3 different flavors of data scientist. Read here
  3. What does it mean to be a data science leader or manager? Eric Weber’s short post on Linkedin on what does it mean to be a leader. IC’s should exhibit these traits for faster career growth especially if you are the sole data person in a decentralized structure. Read here
  4. Functional data engineering: In the blog post here, Maxime Beauchemin explains how to apply functional programming concepts to data engineering.
  5. Interested in growth analytics? Think about this interview question from Andrew Chen: How would you 10x the growth of Product X? LinkedIn post here

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

3 types of data scientist
3 Types of Data Scientist (Source)

Data Culture Mental Model.

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What is Data Culture?

First, let’s define what is culture: “The set of shared values, goals, and practices that characterizes a group of people” Source

Now building on top of that for defining data culture, What are set of shared values? Decisions will be made based on insights generated through data. And also, group of people represent all decision makers in the organization. So in other words:

An org that has a great data culture will have a group of decision makers that uses data & insights to make decisions.

Why is building data culture important?

There are two ways to make decisions: one that uses data and one that doesn’t. My hypothesis is that decisions made through data are less wrong. To make this happen in your org, you need to have a plan. In the sections below, i’ll share key ingredients and mental model to build a data culture.

What are the ingredients for a successful data culture?

It’s 3 P’s: Platform, Process and People and continuously iterating and improving each of the P’s to improve data culture.

How to build data culture?

Here’s a mental model for a leader within an org:

  1. Understand data needs and prioritize
  2. Hire the right people
  3. Set team goals and define success
  4. Build something that people use
  5. Iterate on the data product and make it better
  6. Launch and communicate broadly
  7. Provide Training & Support
  8. Celebrate wins and communicate progress against goals
  9. Continue to build and identify next set of data needs

Disclaimer: The opinions are my own and don’t represent my employer’s view.

5 stages of Analytical Competition

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I love mental model and frameworks. I have shared some frameworks on this blog already like 3 W’s (What, Why, what’s next) and 3 P’s (Platform, People, Process) focused on helping analytics leader figure what their analytics roadmap should be. I was reading ‘competing on analytics’ book and came across the 5 stages of Analytical competition which seemed like another framework worth sharing.

The two end of the spectrum are org is flying blind to org is competing through analytics. Stages are:

  1. Analytically impaired
  2. Localized Analytics
  3. Analytical aspirations
  4. Analytical companies
  5. Analytical competitor

You can read about each one of these here: Five Stages of Analytic Competition  and you can read a synopsis of the book here.

How Analytics changed Scouting in Soccer

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An interesting video that’s a great reminder on how Analytics is a game-changer when applied correctly. The video shared above how small clubs uses analytics to compete with big clubs and continue to not only stay relevant but grow in the process.

Similar analogy can be drawn for startups (or early-mid stage products inside big companies) where they can use Analytics to compete with incumbents in the market.

Let me know what you think. What’s your favorite analogy to help explain why analytics is useful to your org?

Great example of storytelling through data:

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End of the beginning by Benedict Evans.