Newsletter #013
My 3 Data Savviness Articles
Hello and welcome back!
It’s come to my attention that a lot of you are new readers looking to up level your data understanding and know how. Welp, you came to the right place!
This article is focused on 3 different online resources that I like to share and refer folks to. While going trough these
1 - Working with Data Teams
I love how technically.dev breaks down what data teams work on. I learned some new things myself going through this article, and gained a better understand of what my data engineering counter parts do and the tools that they use.
The article does branch off into further articles, and feel free to go down that rabbit hole, but I would recommend going through the main article first.
It’s an easy read and an article that’s great to bookmark for later reference to go deeper and deeper.
The main goal for this article is to gain an understanding of what different data teams do, so that the next time you talk with a data partner, you’re speaking the same language.
2 - Are you Bayesian or Frequentist?
What even is the difference between Bayesian or Frequentist perspective? Honestly, even as an analyst I have a hard time describing it.
But, this article coupled with a short video does a great job explaining the difference between the two and how the difference in mindset is approached when looking at questions surrounding statistics.
What I love the most about this article is that it truly shows how an individual looks at the world. It’s almost a statistical personality test.
Cute.
3 - Hack Gandalf AI
This last one is a fun one.
By now I would assume many of you have chatted with an AI chatbot Ala ChatGPT, Bard, etc.
This is something similar, except it’s a game where you have to engineer a prompt that will make the AI reveal a password it knows.
It’s a great way to gain a better understanding of how AIs “think”. As we enter the age of AI I think it’s important to understand not only the strengths, but also the weaknesses of AI. Using this we can determine what we should use AI for, not only if AI can do something.
Give it a try and see if you can get further than level 4, that’s where I got stuck.
Would love to hear more about YOU my audience! Is there anything in the data world that could use more clarification? What information can I provide to help you utilize data to it’s fullest?
Reach out to me here and I’ll curate my newsletters to fit your needs.
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Thank you and be good,
Irfan - Founder
