i quit my stable career at 36 to learn data science... - Culture of Code Podcast - The Podcast for Indie Hackers Recap
Podcast: Culture of Code Podcast - The Podcast for Indie Hackers
Published: 2024-08-01
Duration: 30 min
Summary
Dilian Yen shares his journey from a stable career in finance and digital advertising to embracing data science and machine learning. He emphasizes the importance of recognizing industry trends and carefully planning for a career transition.
What Happened
In this episode, host Henry Suryawirawan speaks with Dilian Yen, the head of machine learning and analytics engineering at Treasuredata, about his significant career shift at the age of 36. Dilian reflects on his time in finance and digital advertising, where he became increasingly aware of the limitations of using Excel for data analysis. Witnessing the advancements in technology made him realize that sticking with outdated tools posed a greater risk than making a leap into data science. He notes, "to me, it was a risk to not actually go and learn data science," highlighting his excitement for the potential of the field.
Dilian discusses his transition strategy, which involved enrolling in Flatiron School’s data science bootcamp while still employed. He emphasizes the importance of planning and financial stability during this time, as it allowed him to focus on learning without the stress of immediate job insecurity. His approach included taking preliminary courses to gauge his interest and aptitude before fully committing to the four-month bootcamp. He mentions the money-back guarantee offered by the program, which alleviated some financial concerns and gave him the confidence to pursue this new direction.
Key Insights
- Recognizing the need to adapt to technological advancements is crucial for career longevity.
- Careful planning and financial stability can significantly ease the transition to a new career.
- Support from friends and family can be vital during significant career changes, despite some skepticism.
- Timing plays an essential role in career transitions, especially in rapidly evolving fields like data science.
Key Questions Answered
Why did Dilian Yen choose to switch from finance to data science?
Dilian recognized that adhering to outdated tools like Excel would hinder his career growth. Having spent years using Excel for data processing, he became excited about the power of technologies like Python and SQL. This evolution in technology motivated him to learn data science, as he believed it was essential for staying relevant in the industry.
What was Dilian's strategy for transitioning into data science?
Dilian's transition involved enrolling in Flatiron School while still working. He completed an initial one-month online data science course to assess his interest and ability before being admitted to the bootcamp. This careful approach, combined with a money-back guarantee, helped him manage financial concerns and allowed him to focus on learning.
Did Dilian face any skepticism from his social circle regarding his career switch?
Yes, Dilian experienced mixed reactions. While he had strong support from some close friends and family members, others expressed skepticism about leaving a stable career for an emerging field like data science. He noted that even those who were initially doubtful became happy for him once he landed a job in the tech industry.
How did Dilian manage financial risks during his transition to data science?
Dilian emphasized the importance of financial planning during his career transition. He had saved enough money to support himself during the bootcamp and the subsequent job search. This financial stability allowed him to focus on his studies without the added stress of needing immediate income.
What timing considerations did Dilian mention regarding his move into data science?
Dilian pointed out that he joined the second cohort of Flatiron School in 2018, just before the tech boom that followed the COVID-19 pandemic. He believes that the timing of his transition was advantageous, as it positioned him well to enter the industry just before it began to grow significantly.