AI, consciousness & Vedanta
You are never going to be an expert in data science
I’m so glad that data science is not the “hottest” job anymore. Thank you blockchain and cryptocurrencies!
In the past couple of years, we’ve seen hundreds, if not thousands, of training academies prop up in the ed-tech space claiming to teach you data science in 2 weeks / 2 months / 6 months. Because of the high demand from students and beginner data scientists, these businesses have been booming so far but they’ve also setup unreal expectations for their students.
I’ve been working in this field for about 5 years now, and I would consider myself far from being an expert. But don’t take my word for it. Listen to Francois Chollet, the creator of on of the most popular DL libraries, Keras -
I don't consider myself a deep learning expert by any means. There are still a lot more things I don't know than things I know (it's not even close). I've only been working with neural networks since 2009, which is a lot less than many of you.
— François Chollet (@fchollet) April 25, 2021
Besides, I'm not sure that "deep learning experts" exist. People with the highest h-index can't write a GPU kernel or design a DL ASIC. Nor could they win a Kaggle competition. Nor, for the most part, write reusable code (which is really the core of DL).
— François Chollet (@fchollet) April 25, 2021
Not only that, but when I chat with experts, I'm often surprised by how few of them seem to have a clear mental model of what DL is and how it works. In fact, many big-name researchers often say things that are manifestly untrue and easy to disprove!
— François Chollet (@fchollet) April 25, 2021
If someone tells you they're a top expert, a pioneer, the main thing they're an expert at is playing status games. The same people will probably also try to demean those they feel are in competition with them, because that's how status games work.
— François Chollet (@fchollet) April 25, 2021
Peter Norvig, who works as the Director of Research at Google, wrote a beautiful essay titled Teach Yourself Programming in 10 Years. Data science is no exception.
This philosophy is what some people call the “craftsman” mindset. Such kind of roles or careers are best suited to people who enjoy doing “deep work”. I must also admit that such flow states arising from deep work are few and far between.
With time, I hope this hype around AI settles down, and both job seekers AND employers have a more clear expectation about the data scientist roles. As for me, I’ve enjoyed working in this space for the most part. There is still so much I WANT to learn, and then some more, that I NEED to learn. This was also a major motivating factor for me to start writing this blog. Something that will push me to share what I’m learning regularly, something that I will continue to do in the next few years.
2022
Book recommendations from my Covid reading list
5 books to offer a different perspctive on life
2021
Is Data Structures & Algorithms important for a data scientist
Why do many companies still ask CS puzzle type questions in their coding round for DS roles
Structural and Generative Recursion
Looking at the design recipes for 2 common sorting algorithms in Scheme
You are never going to be an expert in data science
Don’t teach yourself data science in 10 days, but in 10 years
Tips and Tricks that helped me find a new job
Some valuable lessons I learnt in my recent job search experience
A lookback at what I was studying as a beginner data scientist
And why you don’t need another ‘How to become a data scientist in 2021’ listicle
How matrices transform space
An intuitive way to look at matrix vector multiplication, with applications in image processing
How much should one know in SQL for data science
Most tech firm interviews include SQL problems for DS roles, so how should you prepare for them?
2020
Matrix algebra operations using recursion
Implementing basic matrix algebra operations in Scheme using a Jupyter notebook
Analyzing gender bias in movie dialogues
Building a gender classifier model based on the dialogues of characters in Hollywood movies
Analyzing my Goodreads data
Simple EDA of my reading activity using tidyverse on R Markdown
Laziness vs The Trap of Productivity & Ambition
My experience using productivity tools for personal projects
Tree vs Tail Recursion & Memoization
Comparing Tree Recursion & Tail Recursion in Scheme & Python
Fun with Haskell
My notes halfway through the book Learn You A Haskell
How I ended up with a data science job
My topsy turvy ride to data science
Favourite learning resources for ML
Books, MOOCs and other resources that I would highly recommend
My journey with SICP so far
The magic of SICP