Hustle Hub #25

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Hey friends,

Last week, we finally hit 1,000 subscribers in our newsletter. 🥳

That’s crazy if you ask me how I feel. I started out by writing this as a letter to my younger self, but somehow we built a growing community to learn from each other. If you’re reading this now, thanks for being part of the community — it means a lot to me. 💜

In today's issue, I’d like to share with you the importance of being resourceful, knowing when to ask for help, and a book that forever changed how I approach data storytelling.

Let’s get to it! 🚀 

🛖 What's in the hub today?

• Tip: Be resourceful

• Mistake: I did everything by myself

• Learning: Learn to ask for help

• Book: Storytelling with Data

• Tool: Quadratic (Data Science Spreadsheet)

⭐️ 1 Tip

Be Resourceful

You may be thinking that being a successful data scientist is nothing more than the combination in this Venn Diagram. That’s true — but only to a certain extent. Because something is missing.

That’s soft skills — and being resourceful is one of the most important soft skills to become a successful data scientist.

Here are 2 main scenarios of why being resourceful is helpful.

🌱 Scenario 1 (Aspiring Data Scientist)

You’re overwhelmed with tons of resources to learn data science.

Even worst, you don’t know what resources are useful to your learning journey, so you tried different resources, and jumped from one online course to another — wasting your time and money.

🚀 Scenario 2 (Data Scientist)

In reality, as a data scientist, you have limited resources to solve business problems. Because the field is moving so fast, sometimes you don’t know what you don’t know.

In short, being resourceful is not a talent. It’s a learnable skill. So the next time you face any problem, ask yourself this question:

More often than not, you’d be surprised by how fast you can solve the problem. Try it out and let me know how it goes! 🤝🏻

⚠️ 1 Mistake

When I first started my data science career, I wanted to prove to my manager that I could do the work. So I did everything by myself and didn’t want to ask for help.

However, things started going haywire. I was building an ML model and was stuck at the AUC of 0.65. I knew I got to ask for help from my colleagues or manager because they had the technical and domain expertise — but I didn’t do it. 🤦🏻‍♂️

I didn’t want to look weak in front of others (it was my first job by the way). Eventually, my manager noticed my slow progress and he advised me to seek help and work as a team to get things done.

Eventually, I realised I was feeding my own ego. Bad move. 😭

🧠 1 Learning

After listening to my manager, I decided to ask the other data scientist what went wrong with my ML model.

He looked at my approach in building the model and immediately advised that I should focus more on features engineering, not hyperparameter tuning — which was the reason I was stuck at the low AUC.

I took his advice and built a few relevant features. Guess what? This time I finally hit the AUC of 0.8!

🧠 Here’s what I’ve learned:

• Know when to ask for help.

• If collaboration brings more productivity, you don’t need to let your ego prevent you from asking for help. At the end of the day, it doesn't matter who did it, what matters is whether it's done.

• There is no ego in a team. Learn to work with others to achieve common goals. Less ego, more things done.

📚️ 1 Book

Storytelling with Data: A Data Visualization Guide for Business Professionals

As a data scientist, having the technical skills to solve business problems is important. However, if you can’t convince your stakeholders to take action, your insights will always remain in your PowerPoint slides.

And this is why data storytelling skill is extremely important. I first read this book in 2018. It was such a game changer that I even wrote a Medium article to talk about it.

It was written by Cole — the founder of Storytelling With Data as well as a highly sought-after speaker and author on the topic of communicating effectively with data.

📚️ Here are my takeaways from the book:

• Focus on empathy and simplicity in your storytelling. Put yourself in your stakeholders’ shoes to feel their pains and needs, then make simple visualisation to share your insights that can solve their problems.

• Understand the context by asking these 3 questions:

  • Who am I communicating to?

  • What do I want my audience to know or do?

  • How can I use data to help make my point?

• Choose an effective visual (no 3D chart please…)

• Eliminate clutter (remove unnecessary visual elements)

• Draw attention where you want it (use Pre-attentive Attributes)

• Tell a story

  • Use stories to engage our audience emotionally in a way that goes beyond what facts can do.

  • Don’t communicate for yourself — communicate for your audience instead.

🧰 1 Tool

When I first saw this new data science tool (Quadratic), I couldn’t un-see it because it’s the all-in-one platform for you to use Python, SQL, and Excel formulas in a single spreadsheet.

The best part is that it’s an open-source tool. So you can download it from the repo and start running it locally.

Here are some exciting things you can do with Quadratic:

• Build dashboards

• Create internal tools in minutes

• Explore your data for new insights

• Quickly mix data from different sources

Here’s a snippet of what Quadratic looks like:

Imagine how you can use only one platform without switching to different Jupyter notebooks, SQL editors and databases.

Give it a try and let me know how it goes! 😄

🔥 Hustler Spotlight 🔥

👋🏻 How would you introduce yourself?

‍I’m Kenny and I have been working in the data science industry for over 5 years. I studied physics and mathematics in university and I enjoy running, playing basketball and exploring technology by working on useless projects during my free time!

👀 What’s your day to day like in your current role as a Senior Data Scientist?

‍It really depends. Some days, I spend my entire time planning and coding, while other days are filled with meetings. Some things include talking to stakeholders, project and product managers on business requirements, writing design docs, coding, debugging and more! I would say talking to people and writing design docs are two components where I spent the most time on.

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

‍I don’t think there is one “biggest” highlight for me because each highlight can be quite different depending on the parameters we are defining on! But something that I’m really proud of is a project where we have built an end-to-end system design solution that has truly impacted people, in terms of finding their suitable jobs/internships. Having our end users reach out to me to thank me for making a difference has to be the most fulfilling experience there can be!

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

‍Apart from the obvious ones (LLMs) at this period, I believe this will open up more confidence in industries where AI is generally less accepted. It will be exciting to see things evolve in the healthcare industry!

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

I highly recommend doing a side project related to your hobby as there are several benefits there. One will tend to be more motivated working on it because it’s something that’s exciting. You will also pick up various skill sets along the way and appreciate the application use cases more (compared to studying everything in a course and not knowing the applications). At the same time, it’s important to “diversify” places where you can have “technical fulfillment” and it should not be solely from your work. In many cases (if not all), things are beyond your control and you ended up not doing what you like at one point of time in your work. When your only pillar comes from your work, your support system crumbles if you have a bad work day and you might even get burned out. People get burned out not by doing too much but rather by doing something that is not fulfilling.

There are many things you can do. If you like music and have Spotify, you can experiment with Spotify APIs and build a machine learning solution or a dashboard of your music preferences. If you like running, you can experiment with Strava API. If there are no APIs, you can try web scraping with python selenium. If you like hardware, you can experiment with a Raspberry Pi/Arduino - all these are skills that you may need down the line. There are also tons of open-source packages online and the list goes on and on.

TLDR - we live in a very exciting world today, take advantage of it and combine your hobbies with data science!

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

Started coding too early in the project - writing design docs and talking to stakeholders are two components that I underestimated earlier. Most of the coding work actually comes from writing the design doc as it keeps you aligned with the main objective. More often or not, when I code blindly at the beginning of my career, I started making too many assumptions and end up with too much redundant work or spaghetti code. At the same time, the ability to understand business requirements, and product sense is important because it’s a natural behaviour to have various biases and we tend to make assumptions when working on a problem, especially not within your domain. Asking questions, and having a structure will ultimately help to build a better product.

🔥 Where can we find your amazing work?

I do have a website (https://kennyvectors.com/) that is a little poorly maintained and the last update was in 2021. On my website, I share some of my pet projects or validate ideas in a scientific way. I promise to keep it a little more active!

🚀 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