Hey friends,

Having been asked by a number of people why I decided to transition from physics into data science, and eventually quit my job to build a startup, I'd love to share my story with you today to hopefully encourage you to keep exploring, and most importantly, inspire you to pursue your passion.

The truth is that I didn't know I wanted to become a data scientist when I was studying Physics. Heck, I didn't even know I'd end up building a startup when I was a data scientist. I can't connect the dots looking forward.

Today, when I look back at my journey in the past 4 years, everything just clicks from studying physics, and working in data science, to building a startup.

Here's my story from physics into data science.

P.S. I'll share my story from data science into building a startup in next week’s issue. 😉

Let's get started!

🚀 It all began during the summer studentship at CERN​

In June 2017, I was very fortunate to be accepted to join the CERN Summer Student Programme to work with top scientists on a research project in particle physics. It was a dream come true for me!

During the 2-months internship period, I was assigned to do data analysis and simulation via cloud computing to identify potential new particles for Compact Muon Solenoid (CMS) Experiment.

The "aha moment" happened when I was first introduced to Machine Learning in one of the workshops conducted at CERN. Mind blown. I was fascinated with the idea of how machine learning techniques could be used to classify and detect various microscopic particles to extraordinary precision with terabytes of data.

🧐 Curious about data science

Once I was back to Singapore from my internship, I did some research to understand more about machine learning. It turned out that machine learning is part of an interesting field called data science.

Back then, data science was quite new in Singapore, and so that caught my attention.

I was amazed by how data could be used to generate insights and drive business values for companies. From understanding a business problem to collecting and visualizing data, until the stage of prototyping, fine-tuning and deploying models to real-world applications, I found the fulfilment of tackling challenges to solve complex problems using data.

Gradually, my passion began to take form…

📈 My Starting Point — Data Visualization

In August 2017, I joined the NIC Face-Off Data competition to dip my toes into data science.

This experience gave me the opportunity to use Tableau Public to visualise open data sources which investigated the origins of haze in Southeast Asia to deliver actionable insights. Here's my first published Tableau dashboard. 😂

The result? I got more interested in data science.

🛴 My first part-time Data Analytics Internship with SMRT​

During the same month, I stumbled upon an opportunity to work as a part-time data analytics intern at mobilityX — a SMRT seed-funded start-up.

From the internship, I learned data extraction using PostgreSQL, data cleaning and analytics as well as web scraping using Python, and the soft skills to work with other stakeholders.

🧑🏻‍💻 I graduated one semester earlier to do a data science internship

All my previous experiences had reinforced my passion for data science.

Determined, I took a leap of faith. I planned my studies timetable and managed to graduate earlier to pursue a 6-months data science internship at Quantum Inventions in December 2017.

You may ask — Why did I go for an internship instead of a full-time data science position?

Short answer: I needed work experience in data science.

Long answer:

• I knew I had zero chance of landing a data science job if I were to find a full-time position. Instead, I took a long-term approach.
• Working as an intern allowed me to build my portfolio, obtain more technical exposure, and learn the full cycle of data science workflow from scratch by dealing with real-world projects before applying for a full-time job.
• Once I've accumulated more work experience, I can then leverage it to secure a full-time job in data science more easily.

📚 Self-learning from different resources

During my internship at Quantum Inventions, I continued to learn from various resources, including online courses, LinkedIn, Medium, and books.

Simply because I wanted to build my technical skills in data science within a short period of time while building my portfolio working for the internship.

🏆 Joined a machine learning competition on Kaggle

In March 2018, I joined a machine learning competition with my friends organised by Shopee on Kaggle.

This was my first time joining a Kaggle competition and learning how to use Convolutional Neural Networks (CNN) and transfer learning for image recognition. The learning curve was steep but the journey was definitely rewarding!

We didn't win the competition, but we learned a valuable lesson from the champion team. We learned the importance of using ensemble models to improve the overall prediction accuracy.

🎉 I finally landed my first full-time job in data science!

After I completed the internship at Quantum Inventions in May 2018, I landed my first full-time job at Titansoft as a research engineer in June 2018! 🎉

Was the job title called data scientist? Nope.

Then why did I still accept the role? Simply because the job title didn't matter to me in the long run. All I wanted is to learn the skills required in data science.

And the rest is history.

Conclusion

That’s all for now. It took me almost 1 year to land a full-time job in data science. If you're struggling to go into data science, don't give up!

I hope that by sharing my data science journey, this issue could in some ways inspire you to go for your passion despite challenges and difficult circumstances.

If you're curious about how I ended up building a startup 3 years later, I'll share my story from data science into building a startup in next week’s issue. 😉

Stay tuned, and have a great week!

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

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