Focus is vital to thrive in any career, and data science is no different. Since being a proficient data scientist requires various skills, developers get perplexed and fail to concentrate on the core of the data science.
To understand effective ways for flourishing in data science landscape, we interviewed Arihant Jain for our weekly column My Journey In Data Science. Jain is a Staff Data Scientist at General Electric. He has 5+ years of experience in the data domain while working at Genpact, RBL Bank, Vodafone, and GE.
Jain is a mechanical engineer-turned-data scientist by choice. He was advised to choose mechanical engineering by friends and family as it was an “evergreen branch”. However, Jain was interested in becoming a quantitative analyst in investment banking during his college days.
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Consequently, he used to binge read about the quantitative analyst domain. And that is when he came across an article that talked about machine learning in trading. Assimilating the depth and breadth of the data science, Jain, while working at his first job, started attending meetups and solving problems on Kaggle.
Preparation And Job Strategy
Being a mechanical engineer, Jain had to study extensively after work. He focused on his programming skills by taking several online courses, engaged on Kaggle, and Analytics Vidhya. He also relied on YouTube videos to learn the ropes of machine learning.
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However, Jain had to struggle to get an initial breakthrough in data science. But he did not lose focus and concentrated on improving his maths, statistics, data intuition, and programming skills. “Obtaining strong basic helped me in getting my first data science project while working on big data platform in my first job,” adds Jain. “That is how I made the switch into the data science field; changing departments in an organisation are not easy, but due to a strong foundation, I was able to get the project.”
After the transition, Jain used to contemplate the importance of his data engineered job and felt it wouldn’t have any significant role in data science. But later on, he understood the importance of data pipelines in machine learning workflows, thereby, stressing on the significance of any experience in the data science field. “It’s about how one connects the dots between past knowledge and the data science use cases,” explained Jain.
Data Science Job Interview
While looking for a new data science job, it was difficult for Jain to handle rejection. But he mentions that such denial from employers’ motivate him to learn from mistakes and work hard for enhancing my skills. Talking about his first data science interview, Jain said he was asked to code the Random Forest algorithm from scratch and explain the mathematics behind. “Interviews experience are good to understand and improve on machine learning techniques,” says Jain.
Thriving In The Competitive Landscape
Data science field is vast and dynamic, thus, covering a lot of ground is of paramount importance. Therefore, Jain said it is crucial to stick to basics and revise fundamentals. “A strong foundation helps me learn new machine learning techniques quickly,” says Jain. “I set aside four to six hours per week to learn new machine learning methodologies.” Further, Jain prefers to take feedback on his work from colleagues to understand the impact of his efforts. This assists him in enhancing his ML-based techniques and workflows to accomplish projects.
Advice For Aspiring Professionals
Data science is a vast domain, and it keeps evolving over the years. This often distracts aspirants. Thus, they should focus on learning from 2-3 different sources and develop skills on top of it; Simultaneously, learning many things and from various sources can be intimidating. Consequently, budding data scientists should stick to basics as it will help them in learning new techniques as and when it takes the lead in the market,” concludes Jain.