A simple definition would be something like this: You have a large amount of data, such as names, ages, locations, tastes, hobbies, and you’re trying to extract something smart and useful from it, like the right communication for advertising. Yeah…it’s a little abstract, maybe a bit of an oversimplification, too. But Data Science is broadly extracting, analysing and visualising data so that it can be used in a better way.
Let’s take a few examples to help you understand it better.
You must have seen smartwatches — or maybe you use one, too. These smartwatches can measure your sleep quality, how much you walk, your heart rate, and even blood oxygen levels.
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Let’s take sleep quality, for instance. In the event that you check each and every day – how could you rest the prior night – that is 1 information point for consistently. You slept 8 hours, you didn’t move too much, you didn’t have short awakenings, etcetera. That’s a data point. But the next day, you slept slightly worse: only 7 hours. That’s another data point.
By collecting these data points for a year or so, you will be able to identify patterns and draw trends from them. Patterns like – On the weekends, you sleep better, on the days when you go to bed before 11 pm, your sleep quality becomes better, etc.
By recognizing these patterns you can draw conclusions as to what is the right time to go to bed and wake up, how much sleep you need, and what are the strain points – which are you keeping awake at night.
Similarly, there are millions of examples of data science such as Optimizing shipping routes in real-time, Finding the next slew of world-class athletes, Stamping out tax fraud, Automating digital ad placement, Algorithms that help you find love….Tinder. Yes, Tinder is using Data Science to help you find love. How sweet of them!
So by now, you have understood what Data Science is and what are some of its applications? But a common question now arises – how do Data Scientists and engineers do it? Is there a magic trick? Let’s see.
Data Science Jobs & Functions
In a team, data engineers first mine complex data, then manage statistical data and look at what their company needs to create different models. Since data is rarely ever clean, they spend ample time collecting, cleaning and munging it with the help of tools like BigML, MATLAB, Tableau, SAS, and TensorFlow.
It is their job to find patterns, build algorithms and models, design experiments, communicate with team members, and perform data-driven decision making. The Data Science domain has been growing leaps and bounds since the internet boom, but in India, it has only started picking up in the last decade. Since then, India has become the second-highest country to recruit employees in the field of Data Science, second only to the United States.
And for the past couple of years, Data Science has also ranked in the top 5 highest paying fields list. An entry-level data scientist can earn around ₹ 6 Lac per annum with less than one year of experience. Whereas the average data scientists salary is ₹ 7 Lac per annum at the fresher level.
On the other hand, a mid-level data scientist earns ₹ 10 Lac per annum in India, and with seniority levels, the salary chart rises beyond 20 lacs per annum. But as you know, India is quite large, and almost every major city has organisations working in this field, so the average salary is also dependent on the geography you work in.
For example, the average salary of a data scientist at a fresher level in Bengaluru is Rs. 9.8 Lac, but for a similar role, you will get Rs. 7.6 Lac in Pune and Rs. 5 Lac in Kolkata.
Data Scientist / Data Science Salary
By the above-mentioned salary figures, it can be easily said that Indian IT companies are paying really good to their employees in the Data Science field. Many prestigious firms like Accenture, EY, TCS, Amazon, HSBC, Infosys, AskTalos – an AI-enabled Chatbot solution provider company- Genpact, Capgemini also hold a reputation for increasing salaries by 15% annually.
So, if you are thinking of making a career in this field, there has been no better time than now.