Month: September 2020

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.

Source

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!