Interview with Shrayansh Yadav
Mumbai, Maharashtra, India
Q1. Please share your educational and professional journey with us?
I am Engineer by profession and an artist by heart. I have done my BE from Ramdeobaba College of Engineering and Management in Electronics Engineering. I was a decent student in college and always an outdoor person. I spent most of my time staying outside the class and learning different things. I have done a Research Internship with CSIR-NEERI in the domain of Silicon commercial Solar panels. I am a singer-songwriter as well. I have joined Accenture Technology in 2018, straight out of my college. I have started working as an Associate Application Developer Engineer there working in their Technology stack.
Q2. You are an Electronics Engineer then what did attract you to Data Science Domain?
I remember while working on my Research Project with CSIR NEERI, we were dealing with a ton of Experimental Data. We were doing experiments with Solar panels silicon chips and with multiple experiments, we got a huge amount of data with multiple features. While preparing our report, we were looking for a tool/process where we can visualize our data for better understanding it. While excel was the obvious choice for this, it was old-fashioned and frustrating. I got to know about this analytics tool called Tableau and we have inculcated it in our reports. And guess what? Our visualizations look out of this world. It was new for us to see our data with these many details and attractive. Being always a creative kid, I found this interesting and figured out the bigger branch of Analytics and Data Science, and Machine learning. And boom, I got into this domain.
Q3. You have worked on following all three positions in your career. According to you, what is the basic difference between NLP Scientist, Data Analyst, and Data Scientist?
The Basic difference that I could see in all these profiles is the type of use cases you want to solve for Business problems. Data Science is a wide field with AI, ML, NLP, and Data Analysis as their sub-part. Usually, in business, you would not be using hard-core deep learning, NLP, and AI. These are used to develop intelligent solutions while Data Analysis and Science are widely used for Business-oriented solutions.
Q4. Which type of Machine Learning is highly used to solve a real-world problem?
For the real world with business-oriented use cases, mostly Data Analysis, Data Visualization, Supervised Algorithms, and a bit of ML algorithms such as Regression, classification, and Time Series predictive analytics are mostly and widely used. While developing some super-intelligent solutions, you can always go to unsupervised algorithms which mainly consist of black box solutions.
Q5. Data Science is everywhere in the world right now. Why has Data Science grown tremendously in the last decade?
In my opinion, Data Science has always been here. We have been using these techniques for quite a time. Nut the main reason for it to grow tremendously is that every business is now moving to a digital ecosystem. As the digital ecosystem is in boom now and with increasing customers and business data, we need highly skilled people in the industry to analyze this data and help businesses to make well-informed decisions. Plus, a solid reason I can think of is the increase in social media and Digital Marketing. This has been said that data science is the highest paying job, institutes are selling it as a very cheap practice. Institutes and organizations are on the verge of selling their course and with the allure of money, they tend to get enrollment and increase their business. This has created an illusion and hence it is trending.
Q6. How does another domain student migrate to Data Science Domain easily? Please share the right path?
It’s easy. I have many of my friends and colleges who are from economics, liberal arts, public policy, Management consulting working as Data scientists. Data Science is more to do with business acumen knowledge and less of the coding part. If you have the right attitude for understanding a business and want to help it take well-informed decisions, Data science is for you. You must be creative, learn a go attitude, and have the patience to get into this domain.
Q7. How Fresher can start learning Data Science? Which are the best Online Courses for Data scientists?
What I have done is to see data science as the need of the hour. With tremendous data in hand, and many institutes offering Damn good Data sciences courses, I would always suggest taking help from YouTube. It’s free of cost and has everything written from scratch. Courses such as Stanford, Harvard, MIT Lectures are of great help. YouTube channels such as Krish Naik etc. are of great help if you are starting from scratch.
Q8. How to get a Data Science job as a fresher?
Try to get a data analyst position in your organization first. If not, try to get hold of some internships. If not both, start taking DS cases in your small projects and small business problems you can see around you. Like how you can predict the number of airplanes which are taking off from airport and day? You will find a path to get into this role.
Q9. How do you update yourself with the latest technology?
Medium, Towards Data science Magazine, GitHub repos, and Google. These are my go-to places to learn new in the field of Data Science. YouTube channels such as one-minute-paper are also of great help.
Read more about Shrayansh Yadav @