The Job of a Data Scientist


The term “data science” wasn’t coined until 2009. Many start-ups are now offering data science incubators and boot camps to train an army of data miners. Business analytics training institutes are offering courses, as are some of the IIMs. Big data is going to get even bigger.

About the Job

  • It’s a dynamic field. Finding new ways to analyse data keeps data scientists on their toes.
  • There are usually several insights hidden in data which data scientists discover and often stumble on strange patterns that tell a story, for example, children born in August score much more than children born in July.
  • Data analytics sometimes results in counter-intuitive insights which is a matter of great excitement for the data scientists.

Data Scientist

A Regular Day at Work

  • S Anand, chief data scientist at Gramener, says, “We’re short circuiting the gap between gathering data and understanding data. We work with all kinds of data. We work with aircraft flight paths, call data, court case results, fielding performances, poll forecasts, TV serial transcripts, etc. But across these domains and types of data, there are common patterns of problems. Which are the biggest problem areas? What factors drive performance? What is the expected outcome? Fortunately, we have also discovered common patterns of analysis and visuals that answer these. Our work involves applying these patterns to any and every dataset.”
  • Raam Nayakar, senior manager of marketing analytics and strategy at fashion e-retailer Myntra, says, “One day I am trying to find which TV channels drive better traffic for Myntra; another day I am engaged in text mining to rank social media responses for a contest we ran. It’s the diversity of business problems that keeps my work interesting.”

On Becoming a Data Scientist

  • A data scientist has to have a business context.
  • One should be able to look at numbers, understand what they are telling and how that message can be converted into an actionable form.
  • A good data scientist needs to have an eye for detail as well as the big picture.
  • Certain tools like Python (a programming language), R, Julia, Excel, etc. are used for data exploration quite effectively. However the tool is often less important than the scientist.


Source: The Times of India

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