Hustle Hub #38

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Hey hustler,

I launched Chatted AI two weeks ago, and I’m super grateful that we’ve got over 117 users so far. It might not seem like a lot, but it means the whole world to me 💛

Most importantly, some of you even reached out to me and shared your feedback — really appreciate it. If you’ve not tried it out yet, I’d love to invite you to be one of our early beta users HERE to make data analytics accessible to SMEs!

In today's issue, I’d like to share with you my thought on choosing a data science career path, how to learn data science 10x faster, and Gorilla to call thousands of APIs for your LLM application.

Let’s get to it! 🚀 

🛖 What's in the hub today?

• Tip: Should you become a data scientist?

• Mistake: I took too many online courses

• Learning: Motion ≠ Progress

• Article: Meet the YC Winter 2023 Batch

• Tool: 🦍 Gorilla: LLM Connected with Massive APIs

⭐️ 1 Tip

Should You Become A Data Scientist?

Recently I’ve received a lot of emails asking if the data science career path is really the best choice for you.

While I can’t say if being a data scientist is the right career path for you, I want to share my thought on what you can expect as a data scientist and see if that’s a good fit for you.

When it comes to data roles, there are different roles like data analyst, business intelligence, business analyst, and data scientist. You might think they are similar, but they are quite different in terms of the job nature.

✨ Dealing with uncertainties

As a data scientist, very often you have to design and run experiments to validate your hypothesis through statistical analysis. With that, it’s almost impossible to know if you’re doing the right things until you’ve tested it out.

If you’re not comfortable with dealing with uncertainties on a day-to-day basis, perhaps bing a data scientist might not be a good fit for you.

✨ Having a strong business domain

Being a data scientist is not just about coding and building ML models. In fact, these are probably just 10% of the stuff that you will do.

In reality, you’d probably spend 90% of your time trying to understand and define problem statements, run around the office to get the data that you want, and understand what each data field (or column) means — before you even start analysing data.

Because of these, you’ll need a strong business domain at the company that you work with (or at least be very curious to understand the business domain).

If you don’t like the business or you’re not curious to understand the business, it’s hard to enjoy your work as a data scientist, let alone make an impact.

✨ Be extremely curious to learn and improve

This is especially true in today’s world where many new AI tools and technology are released almost every day. While you can ignore them for the short term, you’ll eventually have to embrace some of them in your work as a data scientist to make your life more productive.

You don’t have to be aware of all these new tools, but you have to be curious to have a good grasp of how these tools could potentially change your way of doing things.

Failure to do so will leave you behind. And by the time you notice that it might be a little bit too late. Be curious, learn, unlearn, and improve.

🧠 So what should you do?

If you’re comfortable with:

• dealing with uncertainties

• having a strong business domain

• being extremely curious to learn and improve

Then data science might be a good fit for you. If you’re not, then you might want to explore other data roles and see if they are what you want.

Again, don’t overthink. Sometimes all you need is just to pick one thing, try it out, reflect and see if that’s what you want. You’ve got this! 🙌🏻

⚠️ 1 Mistake

One of my biggest mistakes in my early career is that I took too many online courses, including Python, Big Data, Hadoop, Spark, machine learning, deep learning, and SQL.

Why is it a mistake?

Because I thought I mastered the concepts after taking the courses. When in reality I couldn’t even know how to apply them in the real work environment.

On the outside, I looked like I was making progress. On the inside, I was actually just making motions. Splashing, not swimming.

🧠 1 Learning

From the mistake, I learned something: Motion ≠ Progress

The best way of learning (that has worked well for me time and time again) — is by DOING. Period.

🧠 Here’s what I’ve learned:

• If you want to learn a new topic well, start by learning from an online course. After that, quickly start working on a project so that you can apply what you’ve learned in the project. This also applies to building your DS portfolio.

• If you just take an online course and think that you know how to use the knowledge. Probably you’re wrong.

📜 1 Article

Meet the YC Winter 2023 Batch

The latest winter batch of YC shows an interesting trend — 35% of the startups are AI startups.

It means that YC is increasingly focusing on investing in AI startups that could potentially shape the future of society.

What does that mean to you?

Whether you’re a business owner or data scientist, the wave is coming, and the time is here. Live in the future and build what seems interesting.

Have you read this article? What's your thought on it?

🧰 1 Tool

So what’s Gorilla?

In short, LLMs need to interact with the world through APIs, and Gorilla teaches LLMs APIs. Gorilla is a fine-tuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls.

It is capable of writing code and accurately invoking 1600+ API calls (more to come) while reducing the model’s hallucination. It is trained on three massive machine learning hub datasets: Torch Hub, TensorFlow Hub and HuggingFace.

With a simple text input, Gorilla comes up with the semantically correct code and API to execute your task. It already reached 5k stars on GitHub and is licensed Apache 2.0.

Check out the GitHub repo HERE.

🚀 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 1,400+ 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