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Is Data Science For You? This IBM Data Scientist Tells How To Figure It Out

Is Data Science For You? This IBM Data Scientist Tells How To Figure It Out

Rohit Yadav
Data Science

In the ever-changing data science landscape, people need to assimilate whether they are fascinated by the machine learning processes or just going along with the hype this field has generated. Many a time, fresh graduates get into data science, but they struggle to stay abreast with the latest trends.

To discuss this, and more, Analytics India Magazine got in touch with Sharath Kumar RK, Data Scientist at IBM India Software Labs, for our weekly column My Journey In Data Science. He spoke to us about how natural it was for him to choose data science over other thriving domains.

Data Science Journey

Sharath graduated from Mysore and later on completed his Master’s Degree in Finance from BITS Pilani. Early in his employment career, Sharath worked as a technical support specialist for Microsoft products like Windows Server. However, it was only in his second job when he was exposed to data. Sharath used to carry out business intelligence and analytics for financial services. He understood the potential of data and started exploring it to obtain meaningful insights. 



Fascinated by the abundance and curiosity around data, he learned new techniques for analysing data for solving various real-world business challenges. “I was a good problem solver, which enabled me to move towards curating, understanding and moulding the data using different machine learning techniques for solving numerous problems,” explains Sharath. “I realised that I was never got bored of exploring data using different techniques and started playing around it to churn out insights and predictions,” he added.

Such enthusiasm cleared the clouds for Sharath to choose the data science field. He went on to say, how he never looked back after that and kept honing machine learning-related skills that led him to file a couple of disclosures (file rated patents) under artificial intelligence. At IBM, he used various platforms like Watson Portfolio and Cloud Pak for Data, to create multiple solutions.


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Preparation And Job Strategy

While talking about his strategy, Sharath said he preferred learning while at the job, took help from peers, and downloaded materials from the internet for quickly enhancing his skills. It is a competitive landscape, thus one needs to learn from every possible way and from everyone for gaining a competitive edge. 

For job, Sharath worked on creating a portfolio by working on different projects and hosting them on GitHub. He also solved problems in Kaggle to understand the approach to design the solution. Pinpointing the importance of critical thinking, Sharath said that he firmly believes that data intuition is crucial for solving problems. “Recruiters look at aspirants’ ability to approach and solve problems on paper which include structured thinking, logical reasoning, and call out appropriate techniques that can best fit the scenario. Although those are necessary, the primary focus for recruiters is on candidates capability of understanding the challenges at the micro and macro level,” says Sharath. 

Recalling his first data science interview, Sharath said he was asked to solve guesstimate questions, riddles on the paper to evaluate how he goes about solving a problem. Further, he was assessed based on his applied technical skills where he had to justify which ML, NLP, and DL, techniques were effective in various scenarios.

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Strategy To Flourish In Data Science Domain

Sharath mentioned there always be someone who knows better than you in some of the other techniques. That’s why, instead of getting demotivated, he tries to learn from them and enhance his dexterity. “I believe learning is a continuous process, thereby, I keep pushing myself to learn new things and add value with my deliverables,” exclaims Sharath.

Besides, he said one learning from projects is essential for thriving in the domain. Of many successful projects that he worked on, he prefered a project where he made an end-to-end analytics framework for data extraction, transformation, and building machine learning pipeline, to deploy it in production and build visualisation using dynamic dashboards. Further, he worked on projects such as AI-based humanoid bots, image classification using DL, and create NLP solutions. These projects are open-source and available on the IBM website.

Advice For Aspirants

“Learning new techniques that emerge in the market is key to stay relevant, it also helps in enhancing performance to analyse data quickly. However, one should not lose grip on the fundamentals to thrive in the competitive landscape. At the same time, aspirants should also focus on building and enhancing soft skills. Candidates with a mix of technical and soft skills are preferred by organisations of late. Besides, candidates should stay motivated, participate in different hackathons, and use those skills to ace interviews for getting an amazing job offer,” concludes Sharath.

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