Hustle Hub #21

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Hey friends,

So I attended a PowerBI workshop recently, and I realised there are quite a lot of similarities between PowerBI and Tableau. Personally, I’d still prefer to use Tableau due to its ease of use and versatility.

Have you used PowerBI before? What’s your thought on it?

In today's issue, I’d like to share with you the difference between the data scientist and data engineer roles, diversifying your risk, and one of my favourite data science tools — Saturn Cloud.

Let’s get to it! 🚀 

🛖 What's in the hub today?

• Tip: Data Scientist vs Data Engineer: Which Is Best for You?

• Mistake: The collapse of SVB taught me a lesson

• Learning: Always diversify your risk

• Book: Jack Ma In His Own Words

• Tool: Saturn Cloud

⭐️ 1 Tip

Data Scientist vs Data Engineer: Which Is Best for You?

Throughout my career as a data scientist, I also worked closely with data engineers in a data science team. So that gave me a good understanding of the work that they do, the tools that they use, and the skills required as a data engineer.

Here are the 4 main areas that I’ll go through for each job:

• Responsibilities (day-to-day work)

• Skills (that you need)

• Tools (that you use)

• Salary (junior → senior role)

After covering all these, I’ll talk about which job would be best for you.

Responsibilities

Skills

Tools

Salary

🤔 Which job is best for you?

🔴 Data scientist role might be a good fit for you if:

• You like to solve ambiguous and hard business problems using data.

• You are always curious to understand business domains, ask questions, and test your hypothesis.

• You enjoy running analysis, building machine learning models, and presenting insights to stakeholders.

🔵 Data engineer role might be a good fit for you if:

• You like to deal with infrastructure, architecture, and databases that store and organise data.

• You enjoy helping companies save time and resources by building efficient data pipelines.

• You find purpose in building tools to help others do their jobs.

By the way, I also made a video to explain the differences between a data scientist and a data engineer role in details. If you want a deep dive, this video is for you. 👇🏻

⚠️ 1 Mistake

Silicon Valley Bank (SVB), the 16th largest bank in the US, experienced a collapse on 9th March, resulting in the second-largest bank failure in US history after Washington Mutual's collapse in 2008.

There are 37,000 small businesses using SVB as their sole bank account with more than $250,000 in deposits. This means they couldn’t pay their employees in the next 30 days (if the government doesn’t step in to help).

Although I don’t have a deposit in SVB, the collapse of SVB taught me one important lesson:

🧠 1 Learning

As I reflected on how I allocated my money after learning from the collapse of SVB, I realised I allocated the majority of my capital in my stock investment portfolio without having sufficient cash flow in my savings account for emergency use.

This is quite risky as I probably won’t have enough cash to tide through the tough time if needed (unless I was forced to sell my stocks at a lower price).

Also, I always need to prepare for the worst-case scenario (What if the stock investment platform goes bankrupt?).

🧠 Here’s what I’ve learned:

• Always diversify your risk

  • Be optimistic for the future, but also be prepared for rainy days.

• Although it’s recommended to invest your money, but also don’t forget to have enough savings for you to use whenever you need.

• Financial security and freedom are all about taking calculated risks for expected returns.

  • Risk management is key, and this applies to everything we do in life and business.

📚️ 1 Book

Jack Ma In His Own Words

Jack Ma has been my role model in life and business for many years. His journey from being an English teacher to founding and building Alibaba into the 2nd largest internet company in the world has never failed to amaze me.

Although he has kept a low profile after he criticised Chinese regulators for stifling innovation, I’ve personally learned a lot from his wisdom in life and business, and I’d love to share it with you.

📚️ Here are my takeaways from the book:

• When people think you’re good, you have to be sure if you’re really that good. When people think you’re bad, you have to be clear if you’re really that bad.

• Do what you think is RIGHT

  • Take action because you believe in it.

  • Learn how to think, independently.

• When you’re launching a new product for customers:

  • Convince them logically

  • Motivate them emotionally

  • Tempt them monetarily

  • Be fair and just to them

• Be prepared for the future

  • Believe in the future, and believe in yourself to achieve it.

  • Don’t just see the future and opportunities, but also see the disaster that lies ahead.

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

🧰 1 Tool

As a data scientist, when you analyse a small dataset in a Jupyter notebook on your local machine, things are fine.

However, when you start analysing big datasets in your local machine, things start going haywire due to the limited resources.

This is where Saturn Cloud comes in. You can:

• Run analysis or ML models at scale with up to 4TB of RAM, 8 GPUs, or distributed Dask clusters.

• Deploy and run your code or dashboard in production easily.

• Collaborate with other team members for any data science projects.

… all by using Python / R.

I’ve personally used it to run some analysis without managing the cloud environment and instances deployed. If you’re not a fan of running your code on AWS, then maybe Saturn Cloud is a good alternative for you.

Give it a try and let me know what you think!

🎁 BONUS: 5 Lessons I Wish I Learned Earlier in Data Science

If you’ve been following my journey, you’d know that I’m a self-taught data scientist. I didn’t have a computer science degree nor did I take any data science bootcamps.

So I made a lot of mistakes throughout my career journey. This is the video I wished I had when I was starting out in data science.

Hopefully, after watching this video, you won’t make the same mistakes as I did. 👇🏻

🚀 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