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Reflecting and documenting my journey in the past year

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It’s been just over a year since I started writing on Medium.

I’ll be honest, I didn’t even know what Medium was at the time. I thought it was like WordPress, and I was just finding a “medium” to document my learnings in data science.

Fast forward to where I am now, and I’m now officially a data scientist, and I also reaped other benefits like doubling my income. I’ll be honest, there wasn’t really any secret to achieving this aside from a bit of luck and a lot of hard work.

This article serves as more of a reflection…

Using Data Science to Analyze What Exactly Dictates Earnings

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An in-depth analysis of what Medium statistics are indicative of how much money your articles will make.

- Keep scrolling if you want to jump straight to the insights -


I’ve been wanting to do this for a long time now, mainly for my own understanding, but I’m sure that many of you also wonder how Medium calculates earnings. I’m sure many of you also wonder what statistics are actually relevant in determining earnings.

Before this, I’ve read a lot of articles claiming that they’ve discovered the secret formula to determining earnings and then proceeded to claim that read ratio…

A must-read for aspiring data scientists.

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At the beginning of 2018, I really wanted to be a data scientist. I thought that the title sounded prestigious, I thought that building “machine learning models” sounded really neat, and it was a hot career to get into.

If any of the points above resonated with you, then I encourage you to keep reading.

I frequently get asked about how one can make a career switch into data science from something completely unrelated, like accounting or chemical engineering.

I’ve put a lot of thought into this question. One, because this is what I used to ask myself when I…

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As Data Science continues to grow and develop, it’s only natural for new tools to emerge, especially considering the fact that data science had some significant barriers to entry in the past.

In this article, I wanted to go over nine libraries that I’ve come across in the past year that are game changers. These libraries have been incredibly useful in my data science journey and I wanted to share them with you in hopes that it’ll help you with your journey too!

The following libraries are broken down into three categories:

  1. Model Deployment
  2. Data Modelling
  3. Exploratory Data Analysis

1. Model Deployment



Important terms and equations for statistics and probability

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Table of Contents

  1. About This Resource
  2. Confidence Intervals
  3. Z-statistics vs T-statistic
  4. Hypothesis Testing
  5. A/B Testing
  6. Linear Regression
  7. Probability Rules
  8. Bayes Theorem
  9. Interview Practice Questions

About This Resource

When I was applying to Data Science jobs, I noticed that there was a need for a comprehensive statistics and probability cheat sheet that goes beyond the very fundamentals of statistics (like mean/median/mode).

And so, Nathan Rosidi, founder of StrataScratch, and I collaborated to cover the most important topics that commonly show up in data science interviews. …

A new king of the jungle has emerged

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I just want to say that whether you choose data science or data engineering should ultimately depend on your interests and where your passion lies. However, if you’re sitting on the fence, unsure of which to choose because they are of equal interest, then keep reading!

Data science has been a hot topic for a while, but a new king of the jungle has arrived — data engineers. In this article, I’m going to share with you several reasons why you might want to consider pursuing data engineering over data science.

Note that this IS an opinionated article and take…

Results from webscraping over 15,000 Data Scientist job postings

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An in-depth analysis of the most in-demand skills from webscraping over 15,000 Data Scientist job postings.


I just wanted to start off by saying that this is heavily inspired by Jeff Hale’s articles that he wrote back in 2018/2019. I’m writing this simply because I wanted to get a more up-to-date analysis of what skills are in demand today, and I’m sharing this because I’m assuming that there are people out there that also want to see an updated version of the most in-demand skills for data scientists in 2021.

Take what you want from this analysis — it’s obvious…

A comprehensive guide to four popular cross validation techniques

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I wanted to write this article because I think a lot of people tend to overlook the validation and testing stage of machine learning. Similar to experimental design, it’s important that you spend enough time and use the right technique(s) to validate your ML models. Model validation goes far beyond train_test_split(), which you’ll soon find out if you keep reading!

But first, what is model validation?

Model validation is a method of checking how close the predictions of a model is to reality. Likewise, model validation means to calculate the accuracy (or metric of evaluation) of the model that you’re training.

There are several different methods…

Practice Problems for List Comprehensions, Dictionary Comprehensions, and Nested List Comprehensions

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Suppose I wanted to create a list of numbers from 1 to 1000 and wrote the following code…

Can you figure out what’s wrong with it?

numbers = []
for i in range(1,1001):

Trick question. There’s nothing wrong with the code above, BUT there’s a better way to achieve the same result with a list comprehension:

numbers = [i for i in range(1, 1001)]

List comprehensions are great because they require less lines of code, are easier to comprehend, and are generally faster than a for loop.

While list comprehensions are not the most difficult concept to grasp, it…

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