Use simple, everyday examples to understand the fundamental concept so that you can even explain it to your grandmother.

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There is a lot of jargon that comes with data science and machine learning. Often, it takes a while to really understand some concepts that are often masked by formal definitions and mathematical equations. Allegedly, according to Einstein,

“You do not really understand something unless you can explain it to your grandmother."

Despite this, many courses and books explaining Maximum Likelihood fail to make it obvious that the technique is…


Free, Updated Introductory Course Delivered in 2021

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If you want to take your first few serious steps toward becoming an expert in deep learning, here is your opportunity!

They don’t come more easily (and for free) than this…

Perhaps the most well-known resource for learning deep learning is Andrew Ng’s series of 5 courses on Coursera. Those courses are still a great resource for anyone learning the fundamentals of the field but they are now a few years old (their launch was announced in August 2017). In this post, I will give you three main reasons why you should instead start from MIT’s course that I am going to tell you about.

Before I try to convince you to start your deep learning journey from there, here is…


4 reasons why data science is here to stay and what you need to do to ensure that your skillset stays in demand.

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As someone working in data science for over a decade, it is frustrating to see people prophesying on how the field will get extinct in 10 years. The typical reason given is how emerging AutoML tools will eliminate the need for practitioners to develop their own algorithms.

I find such opinions especially frustrating because it dissuades a beginner from taking data science seriously enough to excel in it. Frankly, it is a disservice to the data science community to see such prophecies about a field where the demand is only going to increase even further!

Why would any sane person…

Key lessons and a practical system so that it doesn’t only make you feel good but instead inspires you to transform your productivity routine

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Until recently, I struggled with consistently staying productive. I have achieved phenomenal successes close to deadlines, but I never managed to keep momentum. Thanks to being in academia, I attended numerous courses and training workshops in the last 14 years to improve. However, at best, most of those have been only incrementally helpful. I even bought books on procrastination that I procrastinated on.

Despite these struggles, I always had a way of pulling things together last minute by dialing down on leisure, sleep, and social activities as and when required. This was possible because I was living alone. …

A case study based introduction to using Bayes rule and how it compares with a frequentist, pessimistic and optimistic approaches to drawing conclusions

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This post will help you understand Bayesian inference at an intuitive level with the help of a simple case study. I hope that once you read this article, you will be very clear on how the well-known “Bayes theorem” is used, what do the terms in the theorem mean (prior, posterior, likelihood) and how this compares with other approaches to decision making (pessimist /optimist/frequentist). We will use a simple case study to help explain the concepts. For those who are interested, I have provided simulation results for the given case study and a link to R code for further exploration…


Machine Learning Operations (MLOps) is an emerging field and I strongly encourage you to learn more to catapult your data science career.

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Don’t Let Your Skillset Get Extinct

The data science field is evolving at an unprecedented pace. While the field is certainly not becoming extinct in the foreseeable future, your skillset may well do if you cease learning and upskilling.

Data science continues to enjoy the spotlight as more organizations wish to use data to stay competitive. This is promising for each one of us. However, the rising demand also means that an ever-increasing number of people are getting into data science.

With ubiquitous learning opportunities at everyone’s disposal, you must continue to learn and grow to stay competitive in such an environment.

Why is MLOps Important?

This post will introduce…


A case-study-based introduction that will get you started to use the ggplot2 library to create high-quality graphics and learn about the world

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If you have ever wanted to analyze data and add a new toolkit to your skillset that is used by professional data scientists, then read on.

Rather than presenting a dry list of commands, this tutorial will use a specific case study as a motivating example to teach you everything you need to know about ggplot2, the de facto standard for creating high-quality graphics in R. It is a third-party library supported by the tidyverse ecosystem. While you could plot in R using the base library, you will most likely end up using ggplot2 for any actual project. …


Lesson 2: Being right is not enough, you have to be convincing

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Had the correct scatterplot or data table been constructed, no one would have dared to risk the Challenger in such cold weather. Edward Tufte

It was supposed to be a landmark day in modern history.

The first civilian (a high school teacher named Christina McAuliffe) was selected to go into space. There was even a possibility of a televised conversation between her and President Reagan during the annual State of the Union address, due on the same day in 10 hours. Instead, the space shuttle exploded 73 seconds after launch killing all the seven astronauts on board.

A day before…


What should you do to avoid misuse when assessing the performance of a classifier.

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Would you consider a classifier with 99% accuracy to be good or bad? A good chunk of people would assume that the correct answer is “good". It’s shocking!. The correct answer is neither.

Let that sink in.

I have been in numerous meetings where people assume that a model must be good if the associated performance metric is above a specific value. As a data scientist, you should know better.

Let me explain. No data scientist should ever have, without context, any threshold for any performance metric in mind to suggest whether it is good or not. Data science is…


Let go of any doubts or confusion, make the right choice and then focus and thrive as a data scientist.

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I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.

I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.

Before I dive in, let’s get something out of the way. R and Python are just tools…

Ahmar Shah, PhD (Oxford)

Scientist (several research publications in prestigious journals such as The Lancet, Brain, Thorax, IEEE Transactions), love writing for meaning & impact…

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