Life is a Full Circle? A Programmophobic Petroleum Engineer’s Resilient Journey Towards Data Science
Flashback to my college freshmen year. I received an email from the Science & Technical Committee of my university. The mail read: “We are elated to share that you’ve been allotted to work on the project “Data Analysis of Oil Spill accidents.”
I did not jump in excitement or celebrate this rare opportunity: Back then, I was too naïve to understand the power of data, AI & machine learning. Weeks passed. Our then mentor and one of my favorite seniors (Girish Joshi) walked us through an inspiring introduction to the project. I vividly remember him mentioning that this was crucially important and one of the best projects out of all the others. I was happy and excited to be a part of it.
Soon we had our end-semester exams. I saw myself struggling with computer programming. In fact, it was the course that ruined my first semester grades. I was good at applying logic but bad at resolving syntax errors and got convinced that I’m someone not meant to code.
Over the next few months, I was dragging myself through tableau tutorials and python exercises. I no longer felt excited or enthusiastic to finish the project. Rather did it to fulfill my commitment.
I suffered from programmophobia. I knew that technology and programming were outside of the bubble of my comfort zone. Made up my mind that a petroleum engineer certainly does not have to know how to code.
Fast-forward to March 2020, the world is at a standstill. Schools and colleges are shut down. Challenging times have added to the uncertainty and fear of the students stepping into their final year. I utilized this time to ruminate about my future. Often asking myself farsighted questions:
- Where do I see myself 10 years from now?
- When was the last time I worked on something that consumed me entirely, yet I didn’t feel exhausted?
- Will the career that I’m pursuing be relevant and add value to society 10 years from now?
These questions are hard! Especially if you don’t know the answers to them. I consulted students, working professionals, and researchers from all walks of life, seeking answers to those questions.
A few months passed by, and I still didn’t have a clear picture of my future self. I never will. Neither does anyone else. It was time that I appreciated the uncertainty of life.
After confusing my mind to the maximum limit, I decided to call Girish sir again. After all, he was the one who empathized with me the most during the Data Analysis project. He was the one who inspired me and most of my batchmates to explore different skills. An hour-long call concluded with the advice of not to lose the golden opportunity at hand—campus placement.
The institute I graduated from has a history of attractive job placements in ONGC but only 20% of the students receive the golden cups. Sometimes even less. I made up my mind that I should go with the flow. Do what most of my friends were doing. Prepare for the campus interview.
Between the highs and lows of my preparation, I would often get haunted by questions like “What if I don’t make it through the campus placement? What if I end up with nothing in hand at the end of my graduation?” It was time to think for a Plan B.
I’d just finished studying Nodal Analysis from Production Operations. It was lunchtime. I was already feeling fatigued. Maybe feverish. Post lunch, I immediately took a nap. Three hours later, I woke up to extreme body temperature, mild cough and cold, which only got worse the following day. Temporary relief with home medication was not of much help.
Given my medical history with respiratory illnesses, the local family doctor suggested going to a different city with better hospitals and facilities. I moved to my uncle’s house in a different city. I had already started feeling better but did my COVID test done on a Sunday afternoon. I prayed the college campus interview came only after I completely recovered.
My positive COVID test results surprised all of us as I had a SpO2 of 97 for the past 3 days and was feeling better every passing day. I was immediately home quarantined at my uncle’s house. And a chest X-ray later showed that my lungs were badly affected. I consider myself fortunate that I received the right diagnosis at the right time and started treatment. Grateful that I got saved from the stressful situation of finding a hospital bed and a ventilator. Then came the news that the campus online interview was in 4 days.
There was hardly any time for me to prepare—I was not at my home and still recovering from COVID. My interview hardly lasted 4 mins with questions that were remotely related to the position I was getting interviewed for. I did not make it. Would it have made any difference if I could have dedicated every second of those 10 days heavily preparing for the interview? The answer is a “No.” I never looked back. Nor did I have any energy to overthink, regret, and fret upon what happened as the recovery kept me occupied for the next week.
Rise of the New Amir. Foolishly optimistic—that’s the word I would call myself for the next 9 months or so. I still am a pragmatic person, but there’s a slight difference between the two.
I saw failure as an opportunity to explore limitless possibilities. I’d imagined myself being a researcher, product manager, consultant at MBB (McKinsey, BCG, Bain) and whatnot. I took serious consideration at each of those career paths. What would it look like to be one?
One evening I stumbled upon this 10 min article by Scott Young. Reading through the article gave me a new lens through which to look at my fear of coding. Scott argues that learning to code is easier than we think.
And I watched this video by Google in late September. It inspired me for two particular reasons:
- It highlights machine learning as a gift to humanity.
- It describes machine learning as a powerful tool that can solve problems from any field.
Working on various complexities from different industries has always intrigued me. Suddenly, it all started making sense. It fueled my curiosity on one end and gave me a sense of purpose on the other.
As I started recovering from COVID, I began giving a serious thought on transitioning into data Science. Suddenly my LinkedIn feed would revolve around all things data. I would read posts like your domain expertise is your strength and not weakness. In the weeks that followed, I scheduled one-on-one calls with geologists and chemical engineers who had successfully transitioned into data science. I was learning about people in their fifties, never exposed to programming yet successfully making the transition. The pandemic definitely gave people an opportunity to re-establish themselves. In January 2021, I signed up for a Python course with Datacamp.
When you want something, all the universe conspires in helping you to achieve it.
The Downfall. In January, I returned to the campus after almost a year. Knowing that it was hardly a few months before we end our college life, I got carried away along with my friends. I forgot about the data science journey I recently started. I forgot that I was still unplaced. The only productive part of my day would be concentrating on the online classes. I found myself enjoying the complicated mathematics involved in the Modelling, Simulation and Optimization course.
Between friends and classes, I’d extract chunks of time for my project. The project meant to improve my programming skills occupied most of my time assembling data. I was off track. I wasn’t enrolled in any course. Couldn’t make much progress with the project due to the missing data.
I’m boarding the train to return home. Rising deaths and unavailable beds in the hospital have taken a toll on the country. Our final exams were shortly held online. Soon after finishing my exams, I stumbled upon a compelling career transition story by Avery Smith. My major takeaway from his article: Get paid to learn data science. I somehow believed it.
I was back in the game. I attended machine learning workshops from IIT Delhi in the evenings while listening to Andrew Ng in the mornings. I was putting sincere efforts yet was unsure how long it would take.
I covered basic machine learning, data preprocessing, short regression projects on Python, statistics, and an overview of other ML concepts through the workshop. Even went through the cheat sheets and ML interview guides experts share on LinkedIn. They came in very handy and helpful.
I attended my first off-campus interview for the graduate data scientist position with Atkins. The 75-minute interview went slightly above average based on my experience. The interviewers gave an empathetic ear to learn about my strengths along with weaknesses. There were questions I wasn’t able to answer. But somehow, I was comfortable with those questions. My desire to prove my mettle after a prolonged struggle of fetching interviews reflected as confidence with every answer I gave.
On 28 July, the good news arrived—I got the job. It’s finally the time that I remove the question mark from the title. Yes, life is a full circle! From fearing programming to deploying complex deep learning algorithms, the journey has been an extraordinary ride. I’m beyond grateful and proud.
Final Tips Before You Walk Away
A lot of students contact me asking, “How did I manage to make the transition despite coming from a non-computer science/non-tech background?” What they’re trying to seek is a universal formula that works. Little do we understand it’s different for all of us. Your journey is unique to you based on your sets of experiences and encounters. Yet, there are some essential components common to all. Here are my two cents if you’re coming from a core engineering background looking to transition into data science:
- Despite mastering all the algorithms, codes, and fancy items, develop the aptitude to comprehend engineering problems across multiple domains. I believe that’s what completes an engineer in the true sense.
- Have a broader vision of the industry you’ll be working in. Look at data science as a tool to add value to businesses. Having a thorough understanding of the different use cases and applications reflects your passion to leverage data science.
- Nurture transferable skills. Working on analytical projects, industry-relevant software will help you create a parallel, demonstrating your experience using technology for utility.
- A basket of soft skills (communication, leadership, agility) is always good to have. Besides, with more and more companies encouraging work/life balance, having hobbies and interests certainly improves your chances of being a great fit.
[The original version of the article was published by the author on Medium.]