India is clearly on a progressive trend when it comes to adopting emerging technologies. Organisations from almost all industries are hiring data science experts in India to help them garner insights from big data. There has been a sharp increase in the demand for highly-skilled professionals and the companies are on a constant lookout for talented persons who can fill the gap.
However, there seems to be a lot of questions regarding this profession. Discussion forums, articles and blogs are full of queries where aspirants, professionals and general folk want to know more about what goes on into being a data scientist.
To understand more about this cadre of “hottest job” holders Analytics India Magazine has compiled a list of top eight most frequently asked questions. A glance at these will update anyone who wishes a career in or just wants to know more about data science.
What are the most valuable skills for a data scientist?
Data Science is now being integrated with industries across all sectors. That’s why not only are the data scientists expected to have a broader set of skills, but the employers also expect more cohesive specialisation and collaboration. According to our study, we found out that the following skills were crucial for a data scientist’s role:
- Thorough knowledge of Python, as 44% of the professionals use it the most
- Knowledge of Tableau, as 51%, of the data scientists use it
- RStudio as an IDE
- And in-depth knowledge of Hadoop
What is the average salary of a data scientist?
A report by AIM had found out that the average salary for a data scientist in India is ₹12.7 lakh per annum in 2018. However, this trend has levelled off with the average analytics salary capped at ₹12.6 lakh per annum across all experience levels in 2019. In fact, Data Analytics professionals are currently benefitting from the big data wave with analytics professionals earning 26% higher than an average software engineer in India.
What are the top algorithms that every data scientist should have in his/her toolbox?
It is very crucial for the machine learning enthusiasts to know and understands the basic and important machine learning algorithms in order to keep themselves up-to-date with the current trends. For example, the top five most popular algorithms that all data scientists should know are:
- Logistic Regression
- K-Nearest Neighbours
- Naive Bayes
- Support Vector Machines
- Random Forest
Are too many people training to become Data Scientists?
Many of the organisations, as well as professionals, are worried that the market is being flooded with too many data scientists, as most professionals from the software background are now rushing upskill. This is rather an unfair question, to be honest.
The term data science is being used rather loosely — from analysts to data visualisers — all are being termed as data scientists. If one goes by this erroneous definition, then yes, it will appear that there are far too many data scientists these days. But if we take into consideration the actual data — that there are as many as 97,000 job openings in the field of analytics and data science in India alone — then we can see that this sector is not saturated, but thriving.
How do you recruit data scientists?
There are numerous traditional as well as non-traditional ways of hiring data scientists. Some of the traditional routes are:
- Job postings on website
- Job posting on social media
But gone are those days. Employers have now realised that to attract the unique breed of data scientists, they need to use unusual avenues like:
- Following profiles on Github
- Organising and participating in conferences
- Tracking WhatsApp communities and groups
- Organising and attending meetups
- Creating hackathons
How to prepare for a data science interview?
Cracking any interview requires preparation and in the case of data science it is not restricted to performing well on the big day alone. An aspiring data scientist is expected to prepare across multiple fronts like:
- Build a portfolio of projects and MOOCs
- Network with peers and stay up-to-date with who’s who, follow trends
- Learn about the position that you are applying for
- Go through questions asked in previous interviews
You can read the entire list here.
Why data scientists need to be story-teller?
As data gets deeper and more complex, it becomes imperative to bring in simplicity in it. And storytelling makes it simple and more interesting, drawing interest from listeners and readers alike. Also, Stories provoke thought and bring out deeper insights.
Also, when data and analytics reveal great insights, an absence of narrative makes it hard to relate to the facts. And this is where data storytelling comes into the scenario — it takes data visualisation to a whole new level. With the help of real-life instances and experience, a data storyteller helps its audience understand better.
Why do data scientists need Math?
If you are a data science aspirant, you need a strong background in mathematics and basic knowledge of statistics. In the midst of the hype around data-driven decision making, the basics are somehow getting sidelined. The boom in data science requires an increase in executive statistics and maths skills. Some of the fundamental concepts expected from a business analyst are correlation, causation and how to statistically test hypothesis. Basic knowledge of linear algebra and calculus is definitely required. It may be hard to master them initially but given the time and practice with working, these areas will be familiar and comfortable to work on.