Understanding Data Science and Analytics
By Prerit Kohli
‘In God we trust, all others must bring data’- W. Edwards Deming.
Data is only a buzz today, but it will be everything tomorrow. So, before going deep into it, let’s first understand what data is and why is it so important. Technically, data is just some organized or unorganized form of alphabets, numbers or symbols. When we organize this data to get an insightful meaning, we call it information. Now, when the world is going digital at an exponential rate and each and every piece of information is stored digitally, we need something to get this information turned into insights. This is where Data Science and Analytics comes into the picture. From optimization to recommendation, analytics helps us to grow our channel and firm. Big Data is the biggest transformation that has happened so far.
Now, let’s try to understand why we need to understand Big Data. Everyone talks about Big Data, but nobody actually knows how to do it; and everyone thinks everyone is doing it, so everyone claims that they are doing it. Are you doing the same?
Volume, variety, and velocity are the things that define Big Data. Imagine if any one pillar was missing from this, say volume, then what would be the need of Data Science? Excel would easily solve our problems. If say, there was no velocity, (which is the frequency of data getting appended/updated), then a few men could have solved the problem offline.
We understand that there is a need for a device which can take these attributes and analyze them in no time. Here is when complex software and technologies like Hadoop come to the rescue.
Why should I study Data Science?
There are infinite reasons why one should dive deep into data. There is no doubt that we will continue to generate increased volumes of data with increase in the number of handheld devices with internet connectivity.
Data Science is the most agile field of all, as more and more data is adding up with variety, the frequency is differentiating, and this needs to evolve at a very rapid pace. Newer branches like Predictive Modelling will keep emerging, which will make this field interesting and wonderful to work in. There will soon be a time when only Data Science will be the differentiating factor and will be the primary function variable of success. There will soon be a time when companies will appoint a Chief Data Officer (CDO) who would be the key function for the business going forward.
The importance of a data scientist will increase with the increase in demand for Big Data experts. With increased demand, there’ll be chances of a limited number of employees with these profiles. Companies will soon start with data-as-a-service model for business. The time for Big Data becoming a fast and actionable form of data is near. To deal with such information, the demand for data scientists will reach sky-high and hence, today’s students like you and me will come into play. We all have heard the saying ‘What gets measured gets managed’ by Peter Drucker. It is true. Data is the key to management.
Where can I learn about Big Data?
As a beginner, you might be thinking about what to do, where to start, and so on. The first thing to know is that you need not be a computer engineer to get knowledge of Big Data. Yes, it does help in understanding some basic concepts, but a student can learn about analytics at any point of time in life. There are many specializations and certifications that IIMs and other good institutes are offering. But having said this, internet is the biggest source to learn about Data. One can start with the basic websites like w3schools.com or some certification websites like Coursera. This can be a basic start, but to get the feel, a person needs to develop an interest in a specific domain and understand the use-case of Data Science for that particular field. For example, understand how machine learning and recommendation work for sites like Netflix, YouTube, how Facebook generates revenue, and what is common in all tech Big 4s. Once you have the feel and the need to learn, then based on technical expertise, one can start with SQL (for a beginner). Also, it is quite important to understand the statistics and business aspects. So, you should always question why you are learning that thing.
A student can start with understanding the basic concepts and applications. Learning can go from Data Modelling to Data Mining to Data Analytics to Data Strategies. All a person needs is the discipline to learn with agility. Keep an iterative process of learning and feel free to go back to keep your learning in a proper format. There is no fixed way to learn about Big Data and algorithms, and one needs to define his own learning, as the concepts are both deep and wide to grasp. No one can master everything, but we all can master a niche in data, which is more than sufficient. Keep learning.
Is Big Data always useful?
Most people say data is the new oil. But is it? Let’s talk more about data. This is true for most of the cases, but I want to talk about the cases where this does not hold true. As said earlier, most of the people claim to know Big Data and this is where the definition fails. Now, people have started to plug Data Science into the areas where it is not required and by over-fitting, the results are as expected in magnitude but not in direction. In easy words, many projects fail due to Big Data application because such a fitting was not required. Big Data analytics clubbed with intelligence is the best mix to be successful. You need the right data at the right time and the right place to use Data Science. So, be sure of what you are doing with an agile process and make sure you fit it into the right place. Take a first mover advantage and be the champion of the future.
About the Author:
Prerit Kohli has completed PGPM course from Indian Institute of Management-Indore (2014-2016) after a Bachelor of Engineering degree from Netaji Subhas Institute of Technology (NSIT-Delhi), in Computers. He is presently working with American Express in Enterprise Digital and Analytics Department. Before joining AMEX, he worked with State Bank of India as a management trainee. He also work as a motivational instructor and likes to research about emerging trends in Big Data.
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