How to start out in the field of data science?

How to choose the right boot camps/online courses for data science?

How important it is for data scientists to be good at Maths?

Vijay Pravin Maharajan, a Data Story Teller listed among Inspiring Data Scientists to be followed (by AI Time Journal, USA) and a ‘40 under 40 Data Scientists’ Awarde in India, shares everything you need to know before starting out in the field of Data Science.

Q. In layman terms, what exactly is data science?

Data Science is something that you explore no matter what the domain is. You would see a lot of data science real-world applications. From the recommendation system of Youtube and Netflix to the suggestions to follow people on social media, everything is an application of data science.

In layman terms, data science is the field exploring various kinds of data and taking out meaningful insights from them.

Q. Why should anyone strongly consider becoming a data scientist in 2020?

Data science, machine learning, and artificial intelligence are picking up and would be the go-to fields in the coming times.

Add to it the fact that it’s one of the highest-paying jobs in the developing nations and is quickly getting more popular in the developing countries!

Q. What are the most valuable skills for a data scientist and how to cover them up?

Learn Python as it’s one of the main skillsets a data scientist should have. Learn some basics of SQL and some data visualization tools like Tableau, Spotfire

Q. How to choose the right boot camps and online courses when there are plenty of them out there?

It’s true. There are plenty of options to choose from. The first step is to get yourself in a couple of boot camps and online courses. There are a lot of platforms like Udemy, Coursera, Lynda (LinkedIn Learning), etc and my suggestion would be to go through two courses on each of these platforms. Rather than completing all these courses, spend 20-25 minutes on each of these courses and you would know which one suits you the most.

There are a lot of free resources available on the Internet. Explore them before opting for paid boot camps/courses.

Q. Is learning from open source sufficient to become a data scientist?

I am from an IT background and graduated in the field of Electrical Engineering. I made use of the open-source to become a data scientist in Germany and would suggest every data-science aspirant to do the same.

Apart from learning from content readily available on platforms like Youtube, try to take part in hackathons and datathons to make the most of the opportunities.

Q. Should a beginner (from a totally different background) start with reading materials to understand the basics? What book would you suggest?

I am from non-IT background and I started learning from online sources through articles, blogs, and tech news.

For beginners, I would suggest them to keep looking on the internet for informative articles and stay updated with the latest trends in the field of data science. Try staying connected with data scientists on platforms like LinkedIn, Quora, and Medium.

Q. How to balance between understanding business problems (formulating solutions) and developing technical skills (coding, core math knowledge, etc.)?

Understanding the business problems needs you to settle in a particular domain. For example- I work in mobility and we work with trains. So, I needed time to understand how this industry works and to draw meaningful insights from the data available to us.

Developing technical skills is up to you. Ramp up your technical skills, spend more time honing your command over Python, and keep enhancing your mathematical knowledge.

So, while understanding business problems require you to give yourself some time to settle in and know the industry, developing technical skills is a continuous ongoing process and needs to be worked upon as regularly as possible.

Q. What are the challenges and how can we overcome the challenges of starting a career as a data scientist?

As data science could be literally applied in almost any industry, it’s important to understand the industry first and this remains one of the most challenging tasks.

Not all data are clear and picture-perfect. Data preprocessing is another tough challenge. Combine that with the level of competition in the field of data science, and one could understand how important and crucial is an effective process of data preprocessing.

Q. What kind of portfolio can help us to get the first job in data science or machine learning?

A good CV, a decent LinkedIn profile, and a GitHub repository where you can store your code snippets (be it a Kaggle or an industrial problem). You can show them to your HR/hiring manager so that they could get a quick look at your skills and capabilities.

Q. What are the top algorithms that every data scientist should have in his/her toolbox?

It depends on the problem, application, need, and the industry itself. Still, having a good grasp over regression models, decision trees, random forest classification could help you get a long way in the field of data science.

Q. How to prepare for a data science interview?

If you have a good CV, a decent LinkedIn profile, and a solid Github repository in your armory, it’s good to have clarity of thoughts on your CV.

Clearly mention your previous experience, education, and your motivation to get into data science. If your CV could reflect what kind of people you follow and your learning methodologies, it could be an add-on.

Mathematics, Statistics, problem-solving ability, and algorithms are some of the major fields you would be tested upon. Get in touch with data scientists all around the globe and try learning as much as you can from them before you appear for your interview.

Q. Data scientists need to be strong at Maths. To what extent is this statement true and why?

If you are good at Maths, you can definitely excel in the field of data science. I have seen many Ph.D. holders in statistics who take up data science has a completely different approach and perspective as compared to someone who doesn’t come from a strong mathematical background.

How strong your analytical skills are and how your approach a given problem is the most crucial factor in the success of any data science.

Q. What would you like to advise every aspiring data scientist out there?

Keep learning. It’s going to be a long journey. You would be needed to learn a lot of things including statistics, mathematics, SQL, Python, Tableau, and much more stuff like these.

So, it’s important to never stop learning and exploring new things. Data science is a vast field and succeeding in this field requires years of hard work, perseverance, and determination.