The term "data science" (originally used interchangeably with "datalogy") was used initially as a substitute for “computer science” by Peter Naur in 1960. Since then it has existed in forms loosely referring to computer sciences till the late 90s. Data Science (as we know it), is a relatively new and perhaps ever-evolving discipline. Its definitions vary and there is no standard approach towards this role, unlike traditional professions like accountancy and sales. There are websites like Big Data Made Simple which compiles more than 10 definitions of Data Science.
The most common definition is available at Wikipedia which writes about Data Science: “Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics, similar to Knowledge (KDD).”
Here is a humble attempt to define a data scientist as “Data scientists (who are partly artists as well) are a new breed of analytical data experts who have the logical, technical and creative skills to solve complex business/industrial problems across domains or specific domains – and the curiosity/tendency to explore and identify problems that need to be solved in cost-effective and time-efficient manner.”
Soaring Demand of Data Scientists: The Supply Gap
Many surveys like McKinsey Global Survey, 2015 or Data & Analytics Report, 2017 by MIT Sloan Management Review finds that the percentage of companies deriving competitive advantage from analytics in increasing and it is a fact that analytics activities have a positive impact on company revenues, margins, and organizational efficiency. However, the supply of right kind of talent is really short.
There is enormous demand across the globe & especially in India and this demand is going to be increasing in the wake of the evolving data science and technology.
As per International Data Corporation (IDC) predicts a need by 2018 for 181,000 people with deep analytical skills and five times that number of jobs with the need for data management and interpretation skills. Mckinsey believes, by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.
Here in India as well, NASSCOM indicated that the analytics market in India is slated to more than double to reach $2.3 billion by 2018 from about $1 billion last fiscal. Times of India says India is seeing a 32.2% demand with people having analytics and data science qualifications over and above degrees in IT or business administration or even doctorates. This is six to eight times more than the demand for IT jobs that is 26.4% nationally. One of the Analytics magazines quoted India, a rapidly growing hub of data and analytics is seeing an increased adoption of analytics and holds promise for the 600+ companies operating domestically to crank out $16 billion in market opportunities by 2025.
Skills that Matter: Making a Right Choice for Becoming a Data Scientist
Becoming a data scientist is both art and science. Those with Bachelor/PG/ Ph.D. in math, science, computer science, physics, engineering, econometrics, stats or management with an aptitude and flair for data-driven business problem-solving can have an advantage over other educational backgrounds.
SAS defines a data scientist (who is partly an artist as well) as a new breed of analytical data expert who has the logical, technical and creative skills to solve complex business/industrial problems across domains or specific domains.
The key skills that are looked after in a Data Scientist are:
- Big Data Management Skills which includes managing technologies and techniques responsible for data capture, transfer, storage, and data processing for analytics. The skills encompass knowledge of Data Management products, Open Source like Hadoop and their integration with each other.
- Analytical Skills which includes the ability to analyse the data with speed and accuracy. Typical skills include knowledge of machine learning techniques (supervised and unsupervised), skills in data mining including predictive modeling, pattern recognition, experimentation, and optimization.
- Business Visualization Skills includes the ability to showcase results to the business audience enabling them to take timely and correct decisions. Typical skills include knowledge of visualization products and domain expertise and skill set to lead analytical modeling results to actionable business insights.
- Communication Skills which includes the ability to contextualize and decode a problem and its solution to interested parties of varying backgrounds using a common ground, allegory, skillful listening, and storytelling.
There are many courses in and outside the country to obtain the above skills. Now a days, a variety of prestigious institutes like IIMs, ISBs and IITs offer business analytics or data science courses. Also, a variety of courses are offered by corporates giants like SAS, Microsoft. Moreover, lots of options exist in form of MOOCs. One need to decide based on one’s learning patterns and needs, budget, time and final aim of becoming a data scientist.