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Data Scientist | Photographer | Storyteller

A case to be an artist before and after anything else

Chef’s Table on Netflix. Image by author

If you haven’t seen the Netflix Original Chef’s Table yet, I couldn’t recommend it enough. Every episode highlights a different iconic chef and walks you through their incredible story & mindset. Being in Data Science, I saw an incredible amount of parallels in these different worlds and I believe us data people can learn a lot from artists.

Quick disclaimer: I am not a chef or aspiring chef myself. So my ignorance for the culinary arts is bound to show here, but the arguments I’ll make should still stand strong.

When you watch Chef’s Table, you see some of the…

Modeling Adidas’s Yeezy & Nike’s Off-White Resales on StockX

New York Times. Image by author


If you’re coming here from part 1 then I’ll already assume you know at least the basics of Python & statistics. If not, starting at part 1 may be more comfortable for your own learning experience.

In part 1, I exhibited how you can use PyMC3 via Python to conduct Bayesian modeling via a coin flipping example. This example is a great place to start because it’s a concept familiar to most and the data isn’t as “messy”. The problem with this example is its contrived nature makes it difficult for you, the learner, to jump into actually using this…

The best hobby to have

Revolutions. Image by author

This will be an opinion piece so if you have thoughts/disagreements please comment!

If you’re even tangentially in the data careers space or are in a predominantly Data role, you know how hot the Data Scientist role has been. Ever since that one Harvard Business Review article (you know the one), this space has been dominating ‘Top 10 Careers” and “Highest Paid Careers” rankings everywhere. Even now, you really can’t avoid being inundated with some article about this title; “Data Scientist job is dead”, “Data Engineer vs Data Scientist”, “Data Scientist is still the sexiest job”, and so much more

Hands-on Tutorials

An Introduction to the Conditional World

Milky Way above Maui. Image by author

This story will be for those already a little familiar with statistics and Python and looking to take their skills to the next level. I’ll start with philosophies and then attempt to directly implement concepts in Python to build a more actionable guide for you. I’ve read about this world countless times, but until I actually applied it myself I didn’t really feel confident in my abilities so I implore you to find a dataset you’re fascinated in and dive right in!

Prerequisites that will help: familiarity with statistics up to hypothesis testing, beginner to intermediate Python skills.

In this…

A low-cost & high-quality guide to begin your own journey

Sunset in Iceland. Image by author

First, what is Data Science? Wikipedia claims it to be:

“Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.”

In this domain, there are several job roles and subfields forming as it evolves. I imagine that over time, saying you ‘do data science’ will be equivalent to saying you ‘do science’ or ‘do programming’. It’ll require more nuance and specification to accurately mean anything. For our current world, I think…

Ani Madurkar

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