All things data newsletter #9 (#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 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A bit outdated (2017) but still a really fun conversation to listen to.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

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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 #6

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The goal 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 5 articles made the cut for today’s newsletter.

1. How do you measure Word of mouth for growth analytics?

Some really good research and methodologies on how to measure word of the growth analytics? Read here

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2. Lean data science

really good insights like “measure business performance and not model performance” with the end goal of delivering business value instead of focusing too much on the algorithm. Read here

3. Good data storytelling: Emoji use in the new normal

Read this to get inspired about to tell stories through data, really well done! Go here

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4. Why is Data engineering important?

Good post that explains important of data engineering here

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5. Five things you should know about Data engineering career

This is a good post to read along with reading about the importance of Data engineers above. Both of these articles give you a good mental model to explain the role and assess if this the right fit for you if you are considering this career track. 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 #5

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The goal 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 5 articles made the cut for today’s newsletter.

1. Why Most Analytics efforts fail?

Fantastic post from Crystal Widjaja (ex Go-Jek SVP of BI) on why most analytics efforts fail? It walks you through steps by step process that you should follow to ensure that Analytics efforts in your org are successful. Must Read! If there’s one post that you read from this newsletter, then pick this one here

2. Data Engineer vs Data Scientist vs Machine Learning engineer

A good discussion on how do data scientists, data engineers, and machine learning engineers differ and where do they overlap. Youtube Video here

3. Three steps in Data Modeling

Learn about the 3 steps in data modeling (conceptual, logical, and physical) on Youtube here

4. Improving Product Recommendations:

Learn about the advances in product recommendations algorithm through Amazon’s science blog here

5. Top 5 SQL problems to solve

Good list on few problems that you should know how to solve for learning SQL. List here

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

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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 #3

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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.What I love about Scrum for Data Sciene.

I love the Scrum mechanism for all data roles: data engineering, data analytics and data science. The author (Eugene) shares his perspective based on his experiences. I love that the below quote from the blog and you can read the full post here

Better to move in the right direction, albeit slower, than fast on the wrong path.

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2. Building Analytics at 500px:

One of the best article on end to end anayltics journey at a startup by Samson Hu. Must read! Go here (Note that the analytics architectures have changed since this post which was published in 2015 but read through the mental model instead of exact tech tools that were mentioned in the post)

3. GO-FAST: The Data Behind Ramadan:

A great example of data storytelling from Go-Jek BI team lead Crysal Widjaja. Read here

4. Why Robinhood uses Airflow:

Airflow is a popular data engineering tool out there and this post provides really good context on it’s benefits and how it stacks up against other tools. Read here

5. Are dashboards dead?

Every new presentation layer format in the data field can lead to experts questioning the value of dashboards. With the rise of Jupyter notebooks, most vendors have now added the “notebooks” functionality and with that comes the follow-up question on if dashboards are dead? Here’s one such article. Read here

I am not still personally convinced that dashboards are “dead” but it should complement other presentation formats that are out there. The post does have good points against dashboards (e.g data is going portrait mode) and you should be aware about those to ensure that you are picking the right format for your customers. The author is also biased since they work for a data vendor that is betting big on notebooks and so you might want to account for that bias while reading this. Also, I had written about “Are dashboards dead?” in context of chat-bots in 2016 and that hypothesis turned out to be true; you can read that here

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Data is going portrait mode! Source

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

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)

Business Analytics Continuum: Descriptive, Diagnostic, Predictive, Prescriptive

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Think of “continuum” as something you start and you never stop improving upon. In my mind, Business Analytics Continuum is continuous investment of resources to take business analytics capabilities to next level. So what are these levels? 

Here are the visual representation of the concept:

business analytics continuum

Insight Extractor Newsletter #1

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I am kicking off a weekly newsletter to share curated list of things you should read to continue to get better at data. Links for this week below:

  1. AI Hierarchy of needs: Think of Artificial Intelligence as the top of a pyramid of needs. Yes, self-actualization (AI) is great, but you first need food, water, and shelter (data literacy, collection, and infrastructure). Read here
  2. A Beginner’s Guide to Data Engineering: A very good introduction to data engineering. If you work as a data analyst or data science, this is a must read to have a full understanding of an important discipline within data family. Also super useful for Jr. data engineers to explain what they do. Read here
  3. The Rise of the Data Engineer: Must read documents to restructure your thinking on data engineering. Read here
  4. Data engineer in 2020: This is a really good list of tools and skills that you should acquire if you want to become a data engineer. Read here
  5. Why did metric X go down last week?: A really good 2 minute read on Linkedin from Andrew Chen: Read here
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Source: Monica Rogati’s fantastic Medium post “The AI Hierarchy of Needs”

It’s your turn: Which article did you like the most? comment below!