I would advise you to immediately abort the reading process if you do not love to ‘gossip’. In case you do, even by 1%, this field of study might just convert your hobby into a career.

If I were joking I wouldn’t have compromised on my sleep and devoted time to connect with a person living in a different time zone.

So, let us benefit from what Hardik Jhaveri, a Data Analytics master’s graduate from the University of Maryland, has got to share.

A Quickie to Techie

Best time to plan for Data Science
Sophomore year

Top Universities in USA for data science (master’s degree)
Stanford University
New York University
Carneige Mellon University
Northwestern University
University of Maryland

Best skills to have for such a course
Logical reasoning
Pattern finding
Love for coding
(Where the first two could be achieved from gossiping)

Best innovative use of Data techniques
To use analytical skills at the bar to get a girlfriend

Best word to describe your workday
Eccentric (but in a nerdy way)

Best playlist of yours while working on data
Bollywood top chart (but generally heavy metal or hip-hop)

Getting back to the serious discussion, let’s listen to what Hardik has to say about the data discipline.

The Error of Non-duality

Breaking the first misconception about this field of study: Data Analysis or Data Analytics and Data Science are different from each other.

Regardless of what people want to or are pursuing they boast being an aspiring ‘Data Scientist’.

Although somewhere down the line they would merge, Data Analysis or Data Analytics relates to fetching information out of specific set of data, through the science of coding whereas, Data ‘Science’ relates to application of algorithms to derive insights from the big data.

So, next time whenever you meet someone who’s pursuing either of the two but boasting about being a ‘scientist’ you know where to hit the person.

The Complexity of their Nature

Data Science is a very complex course as compared to its younger brother – Analytics. It requires a person to be well exposed to the field along with being an extraordinary coder; and then one could take up the course.

Another point of distinction between them would involve ‘packages’. Working as a ‘Analytics’ person one would have to work on the prescribed subset of data whereas, being a ‘Scientist’ one would be involved in automated methods of analysing huge chunks of data.

The Intensive Code Unit (ICU)

These courses are pretty intensive by the manner in which they are conducted. Not only one has to diligently attend all the sessions and also be attentive in them but one would also have to work on the coding languages back at home and develop an expertise in any one of them over time.

Having developed an expertise, one could participate in various online competitions or certifications to get an experience to add to the portfolio.

Should you already have your eyeballs sunk into the socket, you’ll need to have phenomenal guts to venture into this.

The Not-so Case Study

Let’s take an example of Dominos. As they would be have the data of past 10 years of operating in a particular city, the data people would arrange and streamline the data and fetch insights into the company’s operation. Successively, the strategy of marketing tactics such as location based promo codes, or category based promo codes could be rolled out.

Another example of such beautiful tactics used is done by Starbucks. Data Analytics consist one of the main components of their marketing strategy.

Companies belonging to different industries, be it Food and beverages, Healthcare, or Travel and logistics have started to enhance their business by leveraging the knowledge fetched by data people.

The Futuristic Vertical

Data courses have majorly two verticals: analytics and machine learning.

We all would know how Facebook has put machine learning to effect and enhanced our experience of browsing through the news feed. Due to the proliferation of application based internet usage, the use of machine learning for each one of them, no matter how small or big they are, implementation of machine learning has become indispensable.

In terms of the market share, it is expected to grow to a mountainous figure of 200 billion dollars by the year 2020.

Then, I guess, only data scientists would be able to afford more than one family vacation to a foreign destination.

The Best Good news

If you are wanting to be at the top universities in the USA for Data Science, then you would reserve the best opportunity to work with top guns like Facebook, Google, Bloomberg, Bank of America, Capital One and many more, who would be among your electives. And, completing a master’s degree, there, can start giving you returns like 70 to 80 thousand dollars a year, being a fresher.

“Data monetization” will become a major source of revenues, as the world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015.*

Yet, I would suggest you to finish your data science course in USA, and return to India and work for your country. An ideal student would be that who wants to revolutionize the data science industry in India.

In Hardik’s words:

“The field of Data Science course in USA, at least, is growing by leaps and bounds, and in no time shall we all be surrounded by everything digital. That is when Data Science would become a fad. But, if you guys plan it from now, you could be one of the best ones. Even our government is turning its eyes towards digital opportunities, and we never know when we could get a call from Modi ji.
So, good luck.”

*Source: IDC

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