The best way to learn data science is to be good with the fundamentals like Maths, Statistics, SQL & Python /R. – Pranav Chandaliya

Interview with Pranav Chandaliya

Pranav Chandaliya

Assistant Manager – Data Science at US-Based Analytic firm,
Data Science Consultant, Data Science Trainer at Gamaka AI,
Data Science Mentor at The Sparks Foundation, Singapore
Pune, Maharashtra, India

Q1. Please share your educational and professional journey with us?

Currently, I am working at EXL analytics as Assistant Manager – Data Science. I am part of a Data Science team that Provides Analytical solutions to Fortune 100 clients in the Finance Domain to solve complex business problems using data. Prior to that, I worked at Gamaka AI as a Data Scientist / Data Science Trainer. I am passionate about teaching and mentoring students in Data Science. I have Trained Interns at Spark Foundation, Singapore in association with AINE AI, Microsoft Partner.

I have done my Bachelor of Engineering in Information Technology from MIT, Pune. I completed my schooling in Aurangabad. I was awarded a certificate of merit by the central government for excellent performance in academics.

In my Final Year of engineering, my journey towards data science started. I started my self-learning process by experimenting, reading blogs, and doing projects. My final year project was selected for the Grand Finale of the national level hackathon.

Q2. How Fresher can Start Learning Data Science?

The best way to learn data science is to be good with the fundamentals, once you are good with fundamentals you can keep upgrading in the field. To be precise, fundamentals of data science are Maths, Statistics, SQL & Python /R. If you find yourself comfortable with the fundamentals of Maths & Statistics it makes a strong foundation and it will be easy to grasp the knowledge on Machine learning & Deep Learning.

If you’ve reached the later stage i.e., have learned some of the topics, start doing projects to get a hands-on idea of the concept. Before commencing with any machine learning project, understanding the problem statement and figuring out the business impacts, helps in decision making at different stages of the project.

Q3. Why Data Science is growing rapidly in 2021?

Data Science is still growing at a staggering speed as Data points are growing rapidly. Even the institutes which were not using data science have started setting up their data science team in 2021. Data Science enables companies to make a smart decision and solves the complex problem using data. In 2021 every domain is creating a huge amount of data and to make sense out of these data points, organizations require Data analysts & Data Scientists.

Q4. How was your experience at Gamaka AI & The Sparks Foundation, Singapore as a Data Science trainer so far?

It was amazing. I got a wonderful opportunity to train fresher’s, experienced, IT, Non – IT background data science Gamaka AI We have designed practical Data Science courses with guided projects. We have made learning easy by breaking difficult concepts into easy by using real-life cases and analogies that helped aspirants to understand concepts effortlessly. At Gamaka AI we follow experimental learning that helps students to get hands-on projects which aid them to understand how data science concepts are applied to the real world and also helps to crack the data science interviews.

At The Spark foundation association with AINE AI Microsoft partner. I have trained Interns from India, Australia and Indonesia. Guided them with ideas of approaching problems in Data Science projects. Trained them on topics like ML, DL, and Statistics & Deployment of the model.

Pranav Chandaliya

Q5. What is ML Ops and why a data scientist must learn it?

ML Operation deals with deploying, maintaining and monitoring models in production. The journey of the machine learning project does not stop after the creation of the model. Following which there are many important that is vital to make that model useful in real-world scenarios.

The model which performs well on the training and testing set doesn’t mean that it’ll perform well in production, as data scientists, we have to decide and plan the deployment of the model and also monitor it in real-world data. Machine learning project is an iterative process. We have to analyze the error created by the model in production and based on that we have to decide the retraining approach and keep improving the model in production.

Q6. How to get a Data Science job as a fresher?

As a fresher, you have to put on more effort into showcasing your skillset. As a fresher, your fundamentals should be clear on topics like Maths, Statistics, and Programming & SQL then followed by ML & DL. Prepare well for SQL & Python questions. Also start accomplishing projects on all the topics you have learned.

Write a blog illustrating your project and maintain your GitHub profile. Keep doing ML projects and uploading them on Github. Prepare well on your project, most of the questions can be asked from the project which you’ve built. Before applying to any job, refer to their job description and according to that modify your resume and make sure that you have those keywords mentioned on your resume.

Q7. How do you update yourself with the latest technology?

I am always curious about the latest technology. I keep upskilling by reading blogs, research papers and Documentations.

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