Data analytics

Fool-proof ways to be a successful Data Scientist

The decades of 1990s and the 2000s are synonymous with the rise of large IT/ technology firms which have helped many professionals become US Dollar Millionaires, by just working for a firm. That’s right! Many professionals are Millionaires because of the fantastic salaries that they are able to draw.

There are many engineers and graduates who believed that they could ride the same wave and have the same bright future. Unfortunately that is not the case. With the advent of automation and an uncertain global policy framework, the future is not as rosy as it earlier was for IT professionals.

One phrase which rings true even today is that, opportunities are always on offer for those willing to look for them! Data Science is a field that was limited only to a few high end tech companies 5 years ago, but it’s a division which almost all companies with a staff of 50 or more have. Data Science today is intrinsically linked to the survival of many companies.

A data scientist is a professional responsible for collecting, analyzing and interpreting large amounts of data which help in identifying ways to grow the business of the organization.

A Data Scientist is able to sift through any data shared and inform marketers/ sales heads about the various kinds of consumers, the time they buy, their brand/ product preferences, their buying patterns, etc. This is data which was available earlier, but was not utilized. As researchers and statisticians have developed tools to use this data to our advantage, it has created many job positions which were non-existent earlier.

The demand for Data Scientists is growing significantly as many big corporations want clean information which they can use from a large data dump. According to NASSCOM there are over 5 lakh Data Scientist vacancies in India in 2018. USA alone has a shortage of 1.5 Lakh Data Scientists. A lot of people know about various vacancies, but they struggle to be prepared for it. Here’s how one can be prepared for it:

  1. Define the problem

In the words of the revered patriarch of the TTK Prestige group, Shri TT Jagganathan, it is absolutely necessary to first learn to define and understand the problem, rather than simply learn formulas and applying them wherever possible.

One of the biggest challenges with Data Science is knowing what you want to achieve. When you receive a few GB/ TB of data, it is difficult to absorb that data and make sense of it. Only if you have a defined objective in mind do you actually know what’s supposed to be done. Without an objective it is similar to finding a needle in a haystack. With set road map, your team will always know the steps to be taken and avoid any decisions that waste time.

  1. Understand the business before you start

This is a continuation of the first point. Without a sound understanding of what the business does, it is impossible to understand the challenges it faces. Conducting a SWOT analysis of the business/ industry is a must. Without having a thorough understanding of the operations, one cannot understand the areas that he/ she needs to look into. There are a few things you should know before you start solving a problem. This includes customer information, industry level data, business strategies and product details. All these details are required for proper analysis.

  1. Follow the diverge-converge thinking process

 Data analytics is an important part of innovation and customer service. The ability to think by first considering a wide variety of options/ ideas and then narrowing down to a few good options is known as the diverge-converge thinking process. You must follow a systematic way to diverge and converge. Think of all possible hypotheses which could be applicable to a problem, all the possible solutions. Start eliminating them one by one to finally come down to a handful (2-3) solutions which can be implemented and executed quickly.

Having this approach is necessary in an industry where there is Tera Bytes (TBs) of data available. Without a systematic approach to problems, people can easily get lost in a large dump of data.

  1. Always think of an alternate solution  

When one tends to dwell on a particular industry and all its data, it is very easy and dangerous to develop tunnel vision, .i.e. thinking only about one particular solution/ in one direction. Sometimes the data at hand may not be enough to come to certain conclusions. For that exact reason, we need to plan for contingencies.

In some cases, the hypotheses/ assumptions made are very close to each other. Hence, it becomes difficult to suggest one definite path for resolving a problem. Hence, it’s always necessary to have a second option to fall back on.

  1. Participate in Hackathons

 Wikipedia defines a hackathon as a (hack day/ hackfest/ codefest) an event in which programmers, analysts and all those interested in software design/ programming/ development (including subject-matter-experts), collaborate together on tough/ expert software projects.

In an industry as dynamic as Data Analytics/ Science, participating in hackathons will let professionals understand the various techniques used by competitors and other senior professionals of the industry. This gives individuals a clear insight into the way senior professionals work and the manner in which they can make a positive impact on their businesses.

  1. Learn upcoming tools  

Working with a large quantity of data is very tough hence, it shall always be an industry that needs innovative tools which help professionals manage data with ease. This industry is always ripe for disruption. Therefore, there will always be people who come up with new tools and softwares, which can help us work faster. Keep reading about the same as it is beneficial to stay updated. Learning new tools always helps you to handle a big databases.

BSE Institute Limited’s GFMP Edge Certified Data Scientist program will train you to join the Data Science field in just 4 months. It is currently one of the best data science programs for enthusiasts as one can learn about the latest tools, techniques and can practically get big data analytics training.

As an industry that was not very big a 5 years ago, Data Science is growing at a stupendous rate. Corporates of every major economy of the world are embracing data analysis at a rapid rate and even those who don’t plan to build a career in the industry need to understand its ramifications, and applications to improve their career prospects.

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