Navin Manaswi is an alumni of IIT Kanpur, whose hunger for data crunching and algorithmic task helped him explore Data Science in 2013. The love for Data Science for him has kept increasing since then. He has delivered end-to-end AI solutions for telecom, insurance, digital marketing, media globally. Before joining Mantra.ai as Chief Data Scientist, he worked for Dubai Smart City Project and was also a part of a consulting firm in Malaysia. At present, he offers consultancy for data science and AI projects.
Navin is currently authoring a book on Deep Learning with a globally renowned publication. Besides, he has also reviewed books on Data Science and many of his articles on Data Science have been published in different magazines. He is an active AI blogger. The habit of continuous learning has helped Navin a lot in his journey.
How did you get into Data Analytics? What interested you in learning Data Analytics?
Navin Manaswi: While working on Statistical tasks, I explored R programming. Interest in R and machine learning increased after exposure to genomic data and tasks. My interest kept increasing although I worked on various domains including supply chain, insurance, telecom, smart city, digital marketing and Industrial IoT.
What was the first data set you remember working with? What did you do with it?
Navin Manaswi: I got the genomic data where gene expression level was to be analysed for cancer patients. I tried to find out the genes that are significantly differently expressed in patients.
Was there a specific “aha” moment when you realized the power of data?
Navin Manaswi: My first Aha moment was when we deployed real time ticket generation system for Telecom Company. It solved the problems of missing tickets for problems and duplicate tickets for the same problem. Moreover it sends messages to owners based on problems pro-actively.
What is your typical day-in-a-life in your current job. Where do you spend most of your time?
Navin Manaswi: While working on data offered by many clients, I devote time for the data exploration, data manipulation and model building. Portion of the day is invested in learning new approaches in deep learning through blogs, articles and papers. A small fraction is devoted to making strategy for solving the problems.
How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
Navin Manaswi: I like to get myself updated with LinkedIn posts and articles. I get alerts from data science related blogs/websites. Google now shoots off the relevant news to me.
Share the names of 3 people that you follow in the field of Data Science.
Navin Manaswi: AndrewNg, Peter Norvig and Sebastian Thrun
Team, Skills and Tools
Which are your favourite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
Navin Manaswi: I like Python, Tensorflow, Keras and R for performing deep learning and machine learning activities. Tableau and Spotfire are used for data visualization purposes.
What are the different roles and skills within your data team?
Navin Manaswi: Data Analytics, Machine Learning and Deep Learning are different skills within my team. DevOps and Data Visualization are another skills. Roles of Data Analysts, Machine Learning specialist, Deep Learning researcher, Data Scientists make my team.
Help describe some examples of the kind of problems your team is solving in this year?
Navin Manaswi: Leveraging deep learning to solve two good problems medical imaging problems and precision agriculture. Another problem is Industrial IoT related AI activity.
How do you measure the performance of your team?
Navin Manaswi: Through continual user feedback cycles. Through the analysis of their performance and quality of work.
Advice to Aspiring Data Scientists
According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?
Navin Manaswi: Need to have great hunger for learning new skills and need to have customer centric attitude, constant learning orientation, and great analytical skills.
How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?
Navin Manaswi: They need to first get their hands dirty with raw data. They need to work on various data manipulation, data cleaning methods and business perspectives in initial months of training. Machine learning algorithms can be explored and learnt as well.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
Navin Manaswi: They are advised to spend more time on mastering Python, Machine learning and Deep Learning. It also depends on kind of role they want to take. If they want to join consultancy or telecom or supply chain companies, they need to focus on Business Intelligence (Data Visualization mixed with Basic Statistics)
What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
Navin Manaswi: Eventually, Artificial Intelligence will be a new fibre of each business in each Industry. The way IT started pervading in the 2000s, AI is likely to pervade in this coming decade. We need to keep a track of new trends and keep learning, unlearning and relearning. Innovation is the next language of humans so we need to innovate ourselves and world.
What are the skills that you look for when you recruit fresh and experienced talent ? Do you look at the candidates profile’s on open source platforms like Kaggle, AnalyticsVidya etc, and specifically how would you evaluate. Do learning kernels on Kaggle make a good demonstration of skills, or participation on Competitions is the primary lens.
Navin Manaswi: Mostly I offer some challenge and a day to finish. Based on his solution, I initiate and conduct the tech interview. Github account, Kaggle score can be helpful in selection.
How is the startup ecosystem in Analytics, Deep Learning and AI space in India for the curious and ambitious Data Scientists?
Navin Manaswi: I can see huge value in working for startups where innovation and fast learning is very common. AI needs the spirit of innovation and fast learning. Most of the large companies lag behind startups in terms of adopting the latest technologies, techniques, and frameworks.
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