I’m back with the third episode of this new series where I 🎙️ interview Data Scientists at various stages of their careers, sharing their journeys to bring YOU fresh perspectives and insights.
In this episode, I share my conversation with my friend Egor Howell, a Data Scientist at Gousto, where we talk about his self-taught journey into Data Science and how he learned to overcome rejection, among other topics.
I think you’ll really enjoy it :)
Disclaimer: The views expressed by Egor in this podcast are his own and do not reflect the opinions or positions of Gousto.
Key Takeaways
Start Projects Early: "Two months of learning the basics, then immediately start working on projects." Hands-on experience through projects helps bridge the gap between theory and real-world application. Starting projects sooner makes interviews easier to navigate.
Don’t Overthink Resources: "80% of the value comes from just doing the course." Don’t get stuck deciding on the “perfect” course. Pick one, stick to it, and avoid switching frequently, as consistency is what matters most.
Embrace Rejections as Learning Opportunities: "The best way to learn is from interviews." Every interview is a chance to identify and address knowledge gaps. Use feedback to refine your skills and improve your strategy.
Apply Broadly: "I'm a big advocate for volume." Casting a wide net, including roles like data analyst or quant analyst, can help you get your foot in the door. The volume approach is particularly useful when breaking into data science for the first time.
Build a Portfolio: "Have some evidence of your work." A documented portfolio or blog posts showcasing your knowledge can make your application stand out. Showing tangible work is valuable to potential employers.
Learn Deployment Skills: "Deployment is where the actual value is." Understanding how to put models into production has a lasting impact in business contexts. Knowing MLOps and deployment skills goes beyond theory and adds real value.
Stay Flexible with Goals: "If you can’t get straight into data science, consider related roles." Being open to adjacent roles like data analytics can lead to eventual opportunities in data science. Flexibility is essential to adapt to market demands.
Thank you for reading! I hope you enjoyed this third episode because there are more coming in the future.
See you next week!
- Andres
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