The Velocity, volume & variety of data around is increasing at a tremendous pace.
According to IBM data reports, 90% of data available today has been created in the last 2 years. We create 2.5 quintillion bytes of data every day. Approximately 400 hours of video content is uploaded on YouTube every minute.
Social web and mobile phones have revolutionised the way the internet is being used. Around 4 billion people around the world are using the internet and generating tonnes of user data.
Data is useless, until turned into actionable knowledge.
With so much data around; governments, corporates, brands etc are getting bamboozled on how to make sense of it. How to utilise it for more efficient functioning (& profits!).
Data Science is the answer to this challenge. It is the science of extracting insights from the data. Along with domain knowledge, it assists strategic decision making, leading to better customer experience, reduced costs and higher profits.
The most familiar example of this in our daily lives is that of food delivery platforms like Zomato, Swiggy, Uber eats etc. Food delivery is one of the fastest growing segments of the Indian e-commerce market. These platforms leverage the user data and AI/ML in three ways – To deliver an amazing personalised customer experience to their user; To help their partner restaurants plan for future demands by building time series based forecast models; To optimise their delivery partners’ routes to enable efficient and quick delivery to the end customer.
Is Data Science the sexiest job of the 21st century?
Harvard Business Review has dubbed Data Scientist as the sexiest job of the 21st century. A survey by IBM predicts that the demand for Data Scientists will increase by 28% by 2020. While the learning curve in Data Science and Artificial Intelligence (AI) is steep, it opens a plethora of exciting opportunities across industries. The huge scale of adoption also means that there are a lot of jobs available (in India) at this juncture – it is approximated that there are approx. 97,000 open positions in the field of analytics & data science. Clearly, the demand and supply balance for these jobs is skewed towards the former.
Ever heard of Embibe or Kint.io or TechEagle?
While large organisations are still trying to understand data science, agile start-ups are leading the pack in terms of adoption. These AI based start-ups are revolutionising ‘business as usual’. They are picking specific problems and trying to build a business model around it.
Embibe, an ed-tech start-up, helps students improve their test scores by identifying and focusing on their weaker areas.
Niki.ai is providing artificial intelligence as a service through chatbots which offers services like hotel bookings, paying bills, tickets reservations etc.
Kint.io specialises in applying deep learning and computer vision for object recognition in videos and has been acquired by Swiggy for their technology.
TechEagle is building customised drones and UAVs solutions for businesses and has been recently acquired by Zomato.
Opportunities not restricted by Industry
Organisations have started to realign themselves for the new world. A world where data flows through the organisation and everyone understands it in a similar fashion. They are redesigning jobs to take advantage of the huge amounts of data, faster computing capabilities and advanced machine learning algorithms to better the decision making process and automating tasks.
Hence, demand for skilled Data Scientists and AI professionals is constantly increasing across industries like banking, healthcare, Finance and Insurance, media & entertainment, telecom, e-commerce etc.
A study by Analytics India Magazine and Edvancer shows that the banking sector contributes most to the analytics jobs markets followed by Energy & Utilities, e-commerce and so on.
The number of new analytics jobs advertised per month increased by almost 76% from April 2017 to April 2018.
The number of new analytics jobs increased by 52% from April 2015 to April 2016, and almost doubled from April 2016 to April 2017.
Better salaries than other functions
According to a study by a hiring firm Belong, salaries of data scientists increased by 25% on an average in 2017. Owing to high demand, data scientists switch jobs frequently and get very good hikes as compared to other job roles. According to IBM, machine learning jobs are paying an average of $114,000, advertised data scientist jobs pay an average of $105,000 and advertised data engineering jobs pay an average of $117,000.
Will Data Science & AI as A Career Path Ever Die?
Artificial Intelligence and Data Science are touching almost every industry. This is an exciting time to be a part of this transition. Organisations are shifting their resources from expensive tools like SAS, SPSS to open source platforms like python and R. To manage and analyse the voluminous data, existing tools are being replaced or scaled by open source Hadoop platforms like Cloudera, Amazon Web Services, Hortonworks etc. This has led to an increase in demand for professional with distributed computing capabilities like MapReduce, Hive, Spark and Pyspark.
As we have discussed through the article that there are massive opportunities in this field. Having said that, the path to becoming a data scientist is not easy and does not always involve working on fancy models and forecasts. A career in Data Science and AI research has a steep learning curve. But if you think you have the passion for data, nothing can stop you from flying high on the cloud of success.
Welcome aboard and continue to the next article if you already feel the excitement and zeal to jump-start your career in data science and AI.
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