Hustle Hub #29

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Hey friends,

I recently completed a course (ChatGPT Prompt Engineering for Developers) taught by Andrew Ng and Isa Fulford (Member of Technical Staff at OpenAI). I must say it was an eye-opening experience as I learned many best practices when it comes to developing the prompts that fit your use cases.

Anyway, I’ll share more about my learnings in my issue next week — so stay tuned! 🧠

In today's issue, I’d like to share with you how to make money in data science, why you should listen to understand (not speak), a practical ML book (by Andrew Ng), and Google Colab powered by AI for your coding.

Let’s get to it! 🚀 

🛖 What's in the hub today?

• Tip: How to make money in data science

• Mistake: I listened to talk

• Learning: Listen to understand

• Book: Machine Learning Yearning (Andrew Ng)

• Tool: Google Colab (AI-powered coding)

⭐️ 1 Tip

How To Make Money In Data Science 🤑

When I first started in data science, I had always wanted to do freelance work to make that extra side income so I could pay off my student loans faster.

Luckily, I managed to land a few freelance projects and worked with clients ranging from startups to SMEs. However, there were a few challenges:

• It was a situation of “farm and famine”. My side income was volatile as I might have a project this month, and then when starving for the next few months, before I landed another client.

• I worked long hours for a small amount of money (I didn’t charge high enough — story for another day 😂). There was a period when I had 2-3 freelance projects while working full-time.

Because of that, I was exploring ways to leverage my time for higher income.

Guess what? I found an in-demand skill to consistently make a good amount of money with lesser time — and that’s teaching.

🔥 Learn How To Teach Data Science

I learned teaching skills when I became a full-time data science instructor at Hackwagon. The experience just blew my mind as I realised how lucrative this education space is in the data space.

I slowly improved my teaching skills. The breakthrough came when I landed a corporate client to teach a workshop for a 5-figure contract! And that’s more than most of my previous freelance projects combined. 🤯 

Since then, I continued to teach data science, be it for public or corporate workshops.

If you want to make more money in data science, learn how to teach. 🧑🏻‍🏫

You can easily join any bootcamps or institutions to teach while earning good money. The best part? You can improve your soft skills by teaching which would be useful for your work. 🤝🏻

⚠️ 1 Mistake

One of the biggest mistakes in my career is that I had always listened to talk.

When people were talking, I couldn’t wait for my turn to talk and share my thought with others. This bad habit eventually backfired on me as people slowly lost interest during our conversations because I did 90% of the talking.

I didn’t realise this mistake until I read somewhere about the importance of listening to understand, not to talk.

🧠 1 Learning

When they feel understood, they’ll be more keen to learn more about you — and that’s how rapport is built.

🧠 Here’s what I’ve learned:

• The hardest part of listening is not just to listen, but to listen with the right intention. 99% of people listen to talk, only 1% listen to understand. Be the 1%.

• Always be very curious about the person whom you’re having a conversation with. When you’re curious, you ask questions. When you ask questions, you’ll learn more than if you do most of the talking.

The next time when you talk to someone, try to listen more to understand the person’s point of view. Because most learnings tend to come from listening, not talking.

Give it a try and let me know how it goes? 🤝🏻

📚️ 1 Book

Machine Learning Yearning

I read this book when Machine Learning Yearning (draft) was launched by Andrew Ng a few years ago.

It was a mind-blowing experience because the book provided practical insights and recommendations for developing ML systems and deploying ML projects effectively, especially when you work as a team.

If you’re keen to learn more about the best practices of building and deploying ML models, then this book is a must-have for you.

📚️ Here are my takeaways from the book:

• Building the right ML system:

  • It’s important to prioritise the development of the right ML system rather than solely focusing on algorithmic advancements. In short, understand the problem, set up appropriate metrics, and collect relevant data.

• Structuring ML projects:

  • I learned the concept of error analysis, which involves understanding and categorising different types of errors made by the ML model.

• Bias and variance trade-off:

  • It’s important to strike a balance by managing underfitting (high bias) and overfitting (high variance) through techniques like regularisation and model complexity control.

• Training and evaluating ML models:

  • The book also covers strategies for training ML models, such as the importance of starting with simple models and iteratively increasing complexity. It also provides insights on evaluation metrics, cross-validation, and error analysis.

👉🏻 Grab the e-book for free HERE.

🧰 1 Tool

If you’ve ever used Jupyter notebook before for data analysis, chances are you’d have used Google Colab in some ways. Google Colab is just like Jupyter notebook but without the hassle of installing libraries. The best part? You can collaborate with others to code together.

Here’s a good news for you — Google Colab will soon introduce AI coding features using Google’s most advanced family of code models, Codey. 🎉

One of the craziest features is that you can ask an integrated chatbot to generate code for you in case you forget how to use certain library (i.e. Pandas 😉).

Say goodbye to constant googling and reading documentation. To be honest, I can’t wait to use it when this feature is finally launched! 🤯

🔥 Hustler Spotlight 🔥

👋🏻 How would you introduce yourself?

‍I’m Vijay Pravin - Founder & CEO of bitsCrunch, a Blockchain Analytics and Forensics company focusing on securing the NFT ecosystem. Living in Munich, Germany 🍻 - enjoying Octoberfest for the last 10 years, and originally from India. Till date, we have raised 6.35M USD from prominent Web3 VCs like Coinbase Ventures, Animoca Brands, Chainlink, Polygon, Crypto.com Capital, Gate.io labs, and a few others.

👀 What’s your day to day like in your current role as a Founder & CEO at bitsCrunch?

‍As a CEO of a startup, I get to see everything that’s happening inside. Right from Sales, to Marketing, to Technology, decision making and so on. The learning has been surreal so far.

⭐️ What has been the biggest highlight of your career so far?

‍Deciding to quit Siemens at the peak of the pandemic, and starting up is definitely the biggest highlight.

🚀 What's a data or AI trend you're watching this year?

‍Obviously, ChatGPT! And the legal and compliance talks around it.

💼 What advice would you give someone starting out in Data Science?

Data is definitely the future, with more and more data expected to be generated and expected to be collected across industries and across the globe. With IoT (Internet of Things) data is going to get even heavier than it is now. Get the basics right. Get the fundamentals right.

🤯 What’s the most important career lesson you wish you’d learned earlier?

Switching from a 9-to-5 job routine to entrepreneurship.

Raising funds for startup

🧠 How would you learn Data Science if you had to start over?

Back in 2013, when I started learning Data Science, there were very few materials or resources that were available online. But now, there are a plethora of resources available at fingertips. Youtube Lectures, Podcasts, Online Classes, there are plenty of ways now.

🔥 Where can we find your amazing work?

https://bitscrunch.com/ is what we are building, and we recently launched our B2C platform: https://unleashnfts.com/ which can be a great example of what data could do! (It requires NFT knowledge)

You can follow me on Twitter: https://twitter.com/VijayPravinM

Or on LinkedIn: https://www.linkedin.com/in/vijaypravin/ (50K+ followers)

📚 What's your favorite book?

Think Like a Monk - Jay Shetty

🚀 Whenever you’re ready, there are 4 ways I can help you:

1. Book a coaching call with me if you need help in the following:

2. Promote your brand to 1,000+ subscribers in the data/tech space by sponsoring this newsletter.

3. Watch my YouTube videos where I talk about data science tips, programming, and my tech life (P.S. Don’t forget to like and subscribe 💜).

4. Follow me on LinkedIn and Twitter for more data science career insights, my mistakes and lessons learned from building a startup.

That's all for today

Thanks for reading. I hope you enjoyed today's issue. More than that, I hope it has helped you in some ways and brought you some peace of mind.

You can always write to me by simply replying to this newsletter and we can chat.

See you again next week.

- Admond