Hustle Hub #19

Hey friends,

How are you doing? This week has been particularly exciting with the latest release of ChatGPT API — which I’ll share more about it later!

In today's issue, I’d like to share with you why you should know how to write production code as a data scientist, the importance of storytelling skills, and most importantly, the mighty ChatGPT API. 🤖 

Let’s get started! 🚀 

🛖 What's in the hub today?

• Tip: Why you should know how to write production code

• Mistake: I focused only on technical skills

• Learning: Storytelling skills > Technical skills

• Book: Steve Jobs

• Tool: ChatGPT API

🧐 What is Data Science?

When I first started out in data science, I was confused by the job scope (because different companies defined it differently) and what exactly a data scientist does at work.

After receiving similar questions from beginners in data science, I decided to make this video to explain:

• What data science really is

• How data science roles are different at different sizes of companies

• End-to-end data science workflow

I hope you’ll find this video helpful to help you learn more about data science!

⭐️ 1 Tip

Why You Should Know How to Write Production Code

Production code is a well-tested and stable code which accounts for real-life scenarios and it must be robust to function.

Because this is where our analysis and models truly add value to our end users.

Without the deployment of models after agonizing months (or even years) of models development, models will always remain as models if they don’t bring any benefits to customers or end users.

All those long hours of data collection and cleaning, models building and optimisation, and presentation are meant to show that your models can generate results and insights to achieve business objectives.

Once you’ve successfully convinced stakeholders (provided your models are robust, the analysis makes sense from the business perspective, and the results can achieve the business goals), the deployment phase will not be too far away.

And that is when you need to put models into production by delivering production-level code. ✨ 

In short, having the ability to write production-level code is one of the highly sought-after skills as a data scientist for a company.

Hopefully, this gave you the edge as a data scientist to understand the importance of writing production code and master this important skill yet not explicitly stated in job descriptions.

I’m curious. Do you write production code in your company? Reply to this email and let me know! 🧑🏻‍💻

⚠️ 1 Mistake

When I first started out in data science, I thought technical skills are important.

So I focused on learning the ins and outs of Python, how to write the most efficient code, how to run SQL queries faster, how to build robust ML models etc.

I was 50% right.

Yes — while technical skills are important, I still faced a huge challenge in my career. Guess what? I still couldn’t convince my stakeholders to take action from my presentation.

That’s when I realised I missed the second part of the picture — storytelling skills.

🧠 1 Learning

The realisation of the importance of storytelling skills was a game changer for me.

Because before that, I spent most of my time running analysis and building ML models, thinking that if I have the best results then people would pay attention and take action.

But in reality, that’s not the case.

When I presented insights to my stakeholders the next time, I framed it into a story and shared it with them as if I was telling a story instead of presenting facts.

The result? It worked! 😊 

They listened to my sharing. They found the story interesting and asked some good questions along the way. Most importantly, they took action based on my insights.

🧠 Here’s what I’ve learned:

• 🚀 Facts tell, stories sell.

• 🤯 Storytelling skills > Technical skills

• 🥱 Facts are boring. People mostly won’t pay attention to facts. And if they don’t pay attention, they don’t listen, and they’ll never take action.

• 😍 Stories are interesting. People love to listen to stories. Frame your sharing into a story with takeaways, people would take action without you asking.

📚️ 1 Book

Steve Jobs

Unlike other biographies, Steve Jobs asked the author (Walter Isaacson) to write his life story and he asked for no control over what was written. Because of that, the story was raw, honest, and full of lessons about innovation, character, leadership and values.

It took me 2 months to finish reading the book, but I’m gonna share all my takeaways with you so you can learn everything in less than a minute.

📚️ Here are my takeaways from the book:

• 🌅 On Life:

  • People who are crazy enough to think they can change the world are the ones who do.

  • Stay hungry, stay foolish.

  • Be very curious and obsessed with what you love and do.

  • Always question the acceptable norm and thinking.

  • Embrace and practice the power of intuition and experiential wisdom.

  • Seek self-awareness and enlightenment to achieve greatness in life.

• 🚀 On Company:

  • Create a company to last, not just to make money.

  • Recruitment is one of the most important jobs for a founder. Find the right people.

  • Lasting companies know how to reinvent themselves. Never be afraid of cannibalising yourself.

  • Be paranoid about the potential disasters ahead.

• 📱 On Product:

  • Simplicity is the ultimate sophistication.

  • Always get people to see the value of your product.

  • Customers don't know what they want until you've shown them.

  • The first sale is the most important thing in a startup.

🧰 1 Tool

ChatGPT API is finally here baby! 🎉

Developers can finally integrate ChatGPT into their applications and products through the API without typing manually on the preview dashboard.

Here’s why ChatGPT is so exciting and a game changer in tech:

• It’s 10x cheaper than the existing GPT-3.5 models (priced at $0.002 per 1k tokens).

• With cheaper prices, more AI startups and companies will begin to integrate ChatGPT into their products via ChatGPT API.

• With better products powered by AI, users will begin to harness the benefits and use them in their daily life.

• AI can be democratised to more people in the near future!

You can already use ChatGPT API using Python with OpenAI’s Python library.

Will AI replace data scientist jobs? Nope.

Will AI make our life more productive? 100%.

🚀 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 ~1000 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