As we enter 2020, we say goodbye to a long decade of technological advancement and information augmenting the business landscape globally.
That means a new beginning is on its way. A beginning that is poised to bring in a gamut of changes, ideas, innovation, information, and obviously some new challenges. Businesses that possess information are looking to make intelligent use of this otherwise junkish data.
That naturally gives way to data analysis and data science technologies. Subsequently, it is critical to comprehend what is data science and how it is valuable for businesses of the information age.
Not only that, data science has also enabled numerous startups and enterprises worldwide to bring distinctive offerings making improvements in the business landscape as well as people’s lives. A great example is social media networks, and the way Facebook and similar legacy platforms have leveraged the power of data such as user demographics and interests to bring endless solutions for business owners and individuals.
What is data science?
While there are endless ways of defining it, a standard definition I prefer is: ‘Data science is a multi-disciplinary domain that deploys scientific methods, algorithms, processes, and systems to extract graspable information and insights from structured and unstructured data sets to come to the final stage of decision making and predictions.’
The data science domain employs mathematics, statistics, computer science, and information science to analyse and understand data. The final results and information are then converted into ideas, solutions, and offerings by the business decision makers.
Why do we need data science?
It is really easy to understand the available structured data like that of Google Analytics that helps us ascertain the type of users visiting our platform. But that’s not the only kind of analysis to do. There is a lot of unstructured data available with organisations that can help their offerings to be much more personalised.
In comes data science! It is used to analyse and draw meaningful insights out of semi-structured and unstructured data, various advanced analytics tools, and frameworks like Power BI, Apache Hadoop, Spark, Tableau, and programming languages like Python, which help carry the load.
Data science use cases
Talking about the use cases, there is no stop to the implementation of data and tools to make a difference in business or changing lives. Here are the cornerstone use cases of data science:
Data science has enabled businesses and industries in India and across the globe to analyse market trends, study users’ metrics, predict risks, and make ultimately make careful decisions. There are a number of case studies such as Flipkart vs. Walmart by Data Flair backing the importance of data science in terms of minimising losses Data science has facilitated rapid growth in industries and has minimised their losses.
▸ Understanding demand
With the ever-evolving, competitive business landscape, it has become critical to understand the users and their requirements precisely. With data science, it is possible to create and utilise time-saving automated models to go into users’ purchase history, age, income level, and related demographics. This helps to assess the gaps and come up with ideas and offering.
▸ Artificial intelligence
Artificial intelligence and machine learning are the new cool as demonstrated in movies such as Iron Man. Taking a more real-life case, having a self-driving car today is entirely the outcome of processing data, utilising machine learning algorithms, and implementation of instructions by the machine.
▸ Predictive analysis
Weather forecast is a great example of predictive analysis. It is required by space research organisations, media portals, and the aviation industry. The data pulled from radars and satellites is analysed and used to provide predictions related to weather changes and natural calamities.
▸ Fraud detection
Industries such as telecommunications and social media, which are attracting millions of users and subscribers daily really need to moderate the activities of the users. Frauds such as illegal access, theft, impersonation, cloning, and any other such activities can be monitored and prevented with data science.
▸ Product development
Doing business in the digital era needs accumulating data and analysis to bypass competition and identify user interests. Businesses in India as well as globally are deploying the data tools not only to ease up the product development process but also to bring the unique offerings that society lacks today. Here, we can’t ignore the impact of Airbnb and OYO, which have augmented the entire landscape of the hospitality industry on a global level.
Scope of data science in India
India is undeniably one of the cornerstones of the IT industry today. Enterprises today leverage the power of data to hire, train, and improvise functions within organisations.
At the same time, the rapidly increasing demand for data scientists and experts can’t be ignored. IBM has predicted that the demand for data scientists will soar 28 percent by 2020. It is the very need of the hour for organisations to hire employees armed with data science to create relevancy with the market requirements and sustain in the competitive business environment of the present and the future.
A report published by Express Computer back in July 2018 outlines how OYO utilised data science to come up with a distinctive offering making a mark in the hospitality sector of India. According to The Hindu, at the beginning of 2019, there were close to 97,000 data analytics jobs vacant in India, of which 45 percent lie in data analytics companies. All this makes data science a hot career choice.
Career options for data scientists in India
▸ Data analyst
As a data analyst working for an organisation, you are responsible for analysing large amounts of complex data, identify patterns, and derive conclusions carried forward to make prudent business decisions.
▸ Business intelligence (BI) developer
As a BI developer, you contribute towards organisational growth by developing strategies. You need to deploy various BI tools and build custom models to facilitate understandability for the leaders of an organisation.
▸ Data architect
Data architects ensure maximum performance built by data scientists and developers by testing and demonstrating the results.
▸ Applications architect
Applications architects track the behaviour of applications used within a business and how they interact with each other and with the end users.
▸ Infrastructure architect
Infrastructure architects ensure the optimal performance of systems to contribute towards the development of new technologies and system requirements. A great example of this role is a very trending one - cloud infrastructure architect.
▸ Enterprise architect
An enterprise architect works closely with the stakeholders of a company including the directors, VPs, founders, and other top-level managers to build enterprise level IT assets. One also needs to possess managerial skills to be eligible for this role.
▸ Machine learning scientist
Machine learning scientists understand the binary language behind the applications and models. They perform batch processing of accumulated data to make it readable for data scientists. As a machine learning scientist, you are also responsible for researching new data approaches and algorithms, create data funnels, and deliver solutions.
Education qualifications and skills required
Being technical and analytical to the core, the data science domain requires expertise in various disciplines including, mathematics, statistics, IT, and computer science. Some common educational qualifications required to become a data scientist would be:
- Bachelor’s degree in IT, computer science, mathematics, physics or any related field
- Master’s degree in mathematics, computer science.
- A certification in data science and data analytics is always demanded by most of the organisations across various sectors such as healthcare, aviation, automobile, FMCG, telecommunication and many more.
Data scientists in India earn an average of Rs 650,000 in a year. While the job roles and opportunities associated with the data science domain are appealing, these involve a steep learning curve, experience and a technical background - making data science a tough domain to crack. The steep learning curve associated with data science is evidently the reason behind the ever-increasing demand and shortage of supply of data scientists in India. And therein lies its potential.
(Edited by Evelyn Ratnakumar)
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(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)