Daily Dose of Data Science: A Year in Review and What's Next

Today is a special day as this newsletter has completed one whole year of serving its readers.

Exactly one year ago — 3rd October 2022, I started this newsletter with only 1 subscriber (me :P).

It’s crazy to see that in just a year’s time:

• The newsletter has grown to almost 40,000 subscribers.

• It stands among the top 25 technology newsletters on Substack.

The feeling is inexpressible.

I am immensely grateful to every one of you who make time every single day and look forward to reading this newsletter 😇.

Today, I want to share the story of how I started this newsletter, and it’s something I have never shared before in this newsletter.

The Origin Story of Daily Dose of Data Science

Since my time at university and later transitioning to the industry, I had always been passionate about AI research and going for graduate studies.

Thus, getting admitted to the University of Maryland, College Park, was a dream come true and something I never expected — given the competition (only 10-15 admits every year) and its highly renowned research culture.

Back in August 2022 (2 months before starting this newsletter), I was supposed to travel to the US from India.

• I left my full-time job.

• Rented a home there.

• Booked my flight.

• Got my visa approved.

I still remember how excited and joyful I was after my visa approval. Anyone who’s been in that situation can relate, I guess :)

But just two weeks prior to my travel, some tough and demanding events in my family led me to cancel my travel and admission.

Letting go of the desire to pursue graduate studies was disheartening.

While my employer was comfortable if I wanted to rejoin, I decided to explore new career directions and reflect on my learnings in data science and machine learning.

At that time, I was also inspired by Ali Abdaal, his newsletter, and his creator journey.

And that’s how I started this daily newsletter.

The objective was simple. Share short yet valuable daily insights with readers and help them upskill.

Honestly speaking, I never expected that writing this newsletter would turn out to be so rewarding.

But the love and support you have shown as a reader is inexpressible and has immensely helped me navigate those tough times with patience and resilience.

So once again, thanks a lot for appreciating the work and considering this newsletter worth your time.

It means the world to me.

What next?

Going ahead, I am excited to grow Daily Dose of Data Science and help you with whatever I possibly can.

The goal is quite clear — I want to help upskill as many data-oriented folks as I can!

While this newsletter and paid memberships are actively contributing to that goal, I want to give you a sneak peek into the next thing I intend to release pretty soon.

It’s called The Daily Dose of Data Science Lab!

It will be a cohort-based platform for readers to:

• Attend weekly live sessions (office hours):

  • Hosted by me

  • Hosted by invited guests

• Enroll in self-paced and live courses

• Join query discussions

• Refer to the internal data science resources

• Have fun with other learners in the chat room, and more.

• Any suggestions? Feel free to share :)

I have been working on this for some time now, and I can not wait to launch it.

To ensure an optimal and engaging experience, The Lab will always operate at a capacity of 120 active participants at a time, with a priority to current paid members to upgrade and get access to The Lab.

I will be sharing more details soon.

But I don't want to spam this daily newsletter.

So, if this interests you, please fill out this form to receive updates about The Lab: The Lab interest form.

I am excited to help you out in your data journey in any manner I possibly can!

Please don’t hesitate to reach out with any queries or suggestions.

Thanks for making this newsletter a fulfilling endeavor.

Have a good day!

Avi

Latest full articles

If you’re not a full subscriber, here’s what you missed last month:

• Deploy, Version Control, and Manage ML Models Right From Your Jupyter Notebook with Modelbit

• Model Compression: A Critical Step Towards Efficient Machine Learning.

• Generalized Linear Models (GLMs): The Supercharged Linear Regression.

• Gaussian Mixture Models (GMMs): The Flexible Twin of KMeans.

• Bayesian Optimization for Hyperparameter Tuning.

• Formulating the PCA Algorithm From Scratch.

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