Hustle Hub #24

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Hey friends,

Hope you loved the Hustler Spotlight that I shared last week featuring Michael Dillon. Just let me know if you have any feedback for improvement (or any particular questions you want me to ask the guests). 🤝🏻

By the way, I met an impressive founder on Lunchclub last week on building a product and startup, can’t wait to share with you my learning next time. Are you using Lunchclub for networking? If not — you can join HERE (highly recommended!)

In today's issue, I’d like to share with you how you can fast-tracked your data science learning journey with my favourite course, the importance of getting feedback early, and the latest release of Pandas 2.0.

Let’s get to it! 🚀 

🛖 What's in the hub today?

• Tip: The course that fast-tracked my data science learning journey

• Mistake: I only showed my manager results when it’s ‘perfect’

• Learning: Get feedback early

• Book: Practical Statistics for Data Scientists

• Tool: Pandas 2.0

⭐️ 1 Tip

The Course That Fast-Tracked My Data Science Learning Journey

I remember when I first started out, I was just like any other aspiring data scientist searching for an online course just to get my hands dirty and improve my understanding of data analysis, visualisation and machine learning using Python.

To be honest, back then I just had little programming experience with Python and knew nothing about machine learning, let alone the knowledge of deep learning.

But one thing was for sure, I was hungry for knowledge and wanted to learn from someone who had deep experience and was already at the stage where I aspired to achieve.

So this course — Python for Data Science and Machine Learning Bootcamp came highly recommended and appears as one of the top-rated courses.

I was sceptical at first, thinking that I’d fall into the trap of the fancy and catchy title, again.

So I did a lot of research by checking its reviews and comparing it with other online learning platforms. And guess what, it was worth the bucks. 🔥

Because the course fits the learning approach that works best for me:

The course fits my needs to gain a high-level understanding and implement what I’d learned in jupyter notebook right after each tutorial. Maths were sufficiently covered to get me started in the right direction.

I hope that sharing my experience on how I got started in data science and the single course that impacted me the most as a beginner would help you in your data science journey in some ways.

⚠️ 1 Mistake

When I was working at Micron as a data scientist, one of the projects was to build a Tableau dashboard for my stakeholders to identify potential opportunities for cost reduction.

Because I was new to Tableau, so I learned the basics on the job and started building from scratch — which took me a longer time than expected.

One day, my manager reached out to me asking if I was okay since my progress had been idle for quite some time.

Instead of showing him a draft of the visualisation, I wanted to show my manager results only when it’s ‘perfect’.

The result? The dashboard got delayed because of my slow development progress. The worst part? The dashboard wasn’t what my manager expected in the first place. 🤦🏻‍♂️

🧠 1 Learning

This was actually one of the biggest mistakes in my career, simply because many stakeholders were waiting for the Tableau dashboard to be done. The stakes and visibility were high. That was when I realised I was in deep sh**. 💩

🧠 Here’s what I’ve learned:

• Get feedback early.

  • It’s okay to show a draft of my work to get feedback for improvement.

• Apply agile development framework to iterate and build things.

  • Instead of taking longer time and showing them my final work, use agile approach to build MVP quickly, show it to others, ask for feedback, iterate and repeat.

  • Listen to users and build what they want (not what you want).

📚️ 1 Book

Practical Statistics for Data Scientists: 50 Essential Concepts

As a data scientist, having a strong foundation in statistics is a must. Because we will need statistics to do EDA, build experimental design (i.e. A/B testing), understand distribution and statistical ML techniques.

However, if you’re not from a math or statistics background, it’s difficult to learn statistics given the breadth of the topic. And this is why this book (Practical Statistics for Data Scientists) is useful as it covers various statistical methods and how you can apply them in your data science projects.

Less about theoretical stuff, and more about practical guides.

📚️ Here are my takeaways from the book:

• Comprehensive Coverage

  • The book covers a wide range of statistical methods, from EDA to unsupervised learning. It also explains how to avoid misuse of statistical methods, so you know what's important and what's not.

• Accessible Format

  • The book is written in an accessible, readable format that is easy to understand, even if you have limited statistical knowledge. It is an excellent quick reference guide if you want to refresh your statistical knowledge.

• Real-World Examples

  • The book provides numerous real-world examples, demonstrating how statistical methods are applied in data science. If you want to understand the practical applications of statistical methods in data science — this book is for you.

🧰 1 Tool

Ladies and gentlemen — Pandas 2.0 is finally out! 🐼

Pandas has been used by data scientists for data cleaning and analysis, yet the main downside was its speed when analysing huge data — and this is why Pandas 2.0 is released to fix this issue.

You can see the release log HERE. In short, I’m excited about the integration of Apache Arrow as backend for Pandas 2.0 because:

• Pandas becomes much faster as Arrow is faster (both read and write).

• Arrow as better interoperability.

• Arrow can handle various data types and missing values more efficiently.

Check out how you can use pyarrow as backend for Pandas here:

🔥 Hustler Spotlight 🔥

👋🏻 How would you introduce yourself?

Hey! I’m Avery & I’m obsessed with data. I’ve worked in data analytics for a bio-tech startup, an oil & gas major, the Utah Jazz and more. Now I spend my time helping people land their first data job through my company Data Career Jumpstart.

👀 What’s your day to day like in your current role as a Founder at Data Career Jumpstart?

‍6AM: Wake up. 2.5 minutes in the cold plunge.

7AM: F-45 Workout

8:30AM: Emails. Plan day. Student DM’s

10AM: Prepare next Data Career Podcast Episode

12PM: Work on improving The Data Analytics Accelerator Bootcamp

3PM: Update website or social media

5PM: Take my dog Peach on a walk

6PM: Watch Survivor

9PM: Read

10PM: Asleep

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

‍Probably helping 100+ people land their first data job. It’s amazing to hear how some people’s lives change due to landing their first data job. It’s honestly really humbling and rewarding.

If not that, interning with the Utah Jazz was fun. I grew up loving basketball & wanting to play in the NBA. Obviously, that didn’t work out for me, so this was the next best thing.

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

‍On generative text AI for sure. All of the GPT products are amazing & I can’t wait to toy around a bit more with them.

Maybe I already have a little bit? More to come…

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

Don’t try to learn it all. Start with building a project as soon as you can.

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

I think the importance of communication. I’ve never been that great of a communicator & the better you can communicate, the higher you will go.

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

I don’t think I would have changed much. I learned on the job by doing projects. I think that’s the best way.

🔥 Where can we find your amazing work?

You can find everything I do at DataCareerJumpstart.com or the Data Career Podcast.

📚 What's your favorite book?

My favorite data book is Envisioning Information by Edward Tufte.

My favorite personal development book is Crushed It by Gary Vee.

🚀 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