Data Scientist: Making Sense Out of Data
Dr. S. Shyam Prasad
About data scientists
The growth of Information Technology has spawned many new professions; the evolution of big data has ushered in a new career line of data scientist. This article attempts to answer a) what is data science b) Who is a data scientist? c) What does he do? and d) Why is this job attracting much attention?
Who is a data scientist?
Courtesy: Wikipedia – The Free Encyclopedia |
A data scientist job has evolved from the business or data analyst role. Data scientists source rich data sets, analyze them, create visual representation of the data, build mathematical models and prepare reports on their findings. A data scientist uses varied techniques and theories drawn from wide variety of fields such as mathematics, statistics, operations research, information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing. Data scientists’ role encompasses the whole gamut of data analysis in contrast to the traditional roles of data analyst.
According to KDnuggets, 88 percent of data scientists have at least a master’s degree and 46 percent have PhDs. Anjul Bhambhri, vice president of big data products at IBM, says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” A good data scientist also needs to possess strong business intellect and flair to communicate findings to his or her business managers in an appropriate manner. The data scientist needs have an eye for detail as well as the ability to see the big picture.
The difference between a traditional data analyst and a data scientist is that the former may look only at data from a single source – for example from sales system – whereas a data scientist examines data from multiple sources. His job is not just report on data but to look at it from multiple angles and discern what it means and report. He must be somebody like an entrepreneur.
The job of a data scientist
The job of a data scientist is a dynamic one. Data scientists work on all kinds of data such as aircraft flying data, footballer’s performance data, court case data, poll performance data, tax payers spending data, peoples’ health data, credit card holders’ usage data and so on. One day they may analyze TV channels data to find out what drives their business and on another day they may be analyzing text data to decipher social media responses to their promotion. Though the job involves handling varied data, some common patterns do emerge that can be applied elsewhere. Sometimes though, the analyzed data may spring some surprises. For example, polling data analyses showed that of the 88 people at Modakkuruchi, Tamil Nadu, who contested for the elections, did not even vote for themselves, something strange and shocking. Another one that teachers who have high pass percentage of students in their course don’t grade them honestly. Another one that, people taking electric or water meter readings don’t always visit the premises and cook up numbers that are typically multiples of 10. And, children born in August score much more that children born in July(Anand, chief data scientist at Gramener).
Sometimes, data analysis may also throw up counter-intuitive insights. For example, once a data analyst told an American wholesale retailer’s credit card programme to invert their loyalty reward system and reward low spenders instead of the higher ones. This is because, the analyst identifi
ed that the highest spenders as mostly re-sellers who did not need to be incentivized. This saved the company millions of dollars (Raam Nayakar, Myntra).
ed that the highest spenders as mostly re-sellers who did not need to be incentivized. This saved the company millions of dollars (Raam Nayakar, Myntra).
What the future holds for data scientists?
The future looks bright for a data scientist. “Data Scientist” has become a popular occupation with Harvard Business Review dubbing it “The Sexiest Job of the 21st Century”. McKinsey & Company has projected a global demand excess of 1.5 million new data scientists. Accenture Research predicted 32000 analytics jobs in India by the year end. Besides greater opportunity for employment, they are also paid better. Randstad reports that pay hikes in analytics industry is 50% more than IT and the median salary for analytics in India is over 13 LPA (Payscale.com).
The increase in demand for data scientists has also opened the doors of many universities for offering master’s courses in data science. Many startups in India are now offering data science courses and boot camps to train data analysts. There is only one way the future of data scientists and course to train them can go – North.
Conclusion
The foregoing discussion clearly establishes the growing importance of data scientists. Besides being paid handsomely, there are opportunities galore. For the young aspiring post-graduates or graduates who are good at numbers and can visualize pattern latent in them have good prospects. They will be required to learn computer programming for mining data and Python and R, the two premier programmes for data science. This is also opportune moment for education institutions to offer specialized courses to prepare the students for this profession.
This profession will play an important role in predicting the future or the emerging trends; it will be of immense value to policy makers and in strategic decision making in the industry. They may also forecast the spread of infectious diseases, the occurrence of natural phenomenon and stock market behaviour etc. Thus making sense of the data available, one may say that the future of the data scientist is bright.
(My sincere thanks to Dr. Swaroop Reddy for his numerous inputs.)
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