It’s no secret Datageddon (sandhi Data + Armageddon) is upon us. With all those selfies, contacts, blogs, apps (and I can go on) people refuse to delete. I know what you are thinking, it may not be a direct Data Analytics problem, more of a storage problem, but it is.
How do you think Google, who accommodates an unimaginable amount of data from users, analyses which data is important enough? Or Facebook for that matter with billions of status updates, check-ins etc, deems it relevant to show you the ones which you are most likely to like or engage with? Or Twitter? Or Instagram? Enough about the tech giants. From startups to small enterprises to medium enterprises to Fortune-500 all rely on data analytics to make sense of raw data and to make informed decisions.
Data analytics helps to make sense out of raw data and to make informed decisions.
Experts in this field see data as Decision Anticipation Through Analytics. I just made that up but its also based on data analytics. Because once you get the feel of it, it’s exciting and you start to see raw data as diamonds. Even carbon under pressure turns to diamond. Carbon is data, pressure is data analytics and what do you get as a result? A substance so beautiful and most importantly, something which can disperse light into a spectrum of colors. So does data analytics. Data analytics is like a catalyst without which a reaction or a result may not possible
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Data = Decision Anticipation Through Analytics
All theories aside, let’s jump into what people usually ask themselves before making any career decisions. Note the word decision here. Any decision-making process just gets a whole lot easier and accurate once it’s been analysed through data. Something which we (6figr.com) have been doing for a long time to help people make smart career decisions.
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As a result over a span of one year and through Data analytics, we observed that 6figr.com users, in general, earn 38% more. That was a diamond moment for us.
Now back to careers in data analytics.
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The hottest job of 2016? Data analytics.
The hottest job of 2017? Machine Learning.
The hottest job of 2018 (so far)? Machine Learning.
We have incorporated a neural net for careers to enable our users to make the best decisions which are powered by insights/features from other careers. We sliced one particular layer from it see which are the best skills people have who are doing very well based on parameters like pay, rank, experience, demand (count) and jobs and the results are not surprising.
Don’t get confused here. I know Data Analytics can be seen on the 3rd position and there are many different skills mentioned which have gained traction over time. And all of them, with the exception of Java & c++, are closely related to data analytics and in fact, are used by most data analysts.
Some may even argue for the usage of Java and c++ in data analytics as well, but I’m avoiding a debate. If you are wondering what those graduation hats are, besides the listed skills, they indicate courses available for people who want to pursue all those skills – made possible by our awesome partners such as UpGrad.
For eg., if anyone wants to pursue the Data Analytics course via UpGrad they get to learn R, Tableau (which is also trending…comes in #11 in the above list), Python and much more.
Still confused about the percentage values attributed and that they don’t add up to a 100? Well, it’s a representation of how skills are distributed over millions of careers – which means a guy who knows Data Analytics might also know R and that’s why there is an overlap.
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Now let’s look at how much you can earn as a Data Analyst:
This salary distribution certainly acknowledges a massive difference in adoption of data analytics, in a financial view of career, as compared to software engineers:
One may argue it’s incomplete or unjust to compare software engineers to data analysts. Let me remind you that this is just an example and, in fact, this is taken from a neural net we built over millions of profiles. Head over to 6figr.com to check out all details.
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Let’s quickly look into the future of Data Analytics:
1. Big Data has gone mainstream: Heard of that term echoing through space around you for an infinite amount of time? Big data…big data…big data! Just take a moment to acknowledge it and explore Big Data before you fall behind.
2. Predictive Analytics has gone mainstream: Of course, it has! At least in India where there is a clear boundary between a role in Data analytics and one in Predictive Analytics, and even machine learning, (which makes sense to be honest because they consist of an entirely different breed of study to master) however, data analytics has always been the catalyst to these studies.
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3. Data Analytics has gone mainstream (since 2013): It has always been the stepping stone to the aforementioned 2 areas. Let me remind you again before you think Data Analytics is in the past, that all of these areas and skills are correlated. There is a moon-size overlap!
4. AI is going mainstream (since 2016): You may wonder why I’m talking about all – Big data, Predictive analysis, AI, Data science and Data Analytics – together. Remember the neural net we have built to analyse millions of careers? Well, it says all of these belong to the same cluster and when we tried to extract a certain feature to see the date of occurrences it turns out Data Analytics is the parent. This is not a bias, it’s a data-driven conclusion.
Looking at the numbers we already have a huge population opting for Data Analytics. Data Analytics enables you to make smart decisions. Talking about numbers, don’t take my word for it.
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It’s time for action on your part. Maybe just Google Data Analytics or related keywords and begin. Maybe you are experienced enough to just begin your career in it.
What is the use of Data Analytics?
Data analytics has many uses. Businesses can prosper in an increasingly competitive environment by improving performance, and data analysis helps in improving this performance. Data analytics plays a crucial role in predicting market trends and assessing risk in the banking and financial industries. With the help of Data analytics, valuable information can be gained that can benefit healthcare, crime prevention, and environmental protection. These data analytics applications help in making the world a better place. Data analysis is also employed in scientific study and sophisticated analytic tools.
Is Data Analytics a part of Data Science?
While Data science is an interdisciplinary subject that seeks meaningful insights from vast amounts of raw and organized data, Data analytics is concerned with the processing and statistical analysis of existing datasets. Data science is an amalgamation of various areas that use massive databases to harvest information. Data analytics software is a more concentrated version of this and may even be regarded as a component of the whole process. Data science isn't concerned with answering particular questions but rather with sifting through vast numbers in often unstructured ways to uncover insights. Data analysis works best when targeted, with questions in mind that need to be answered using current data.
Is learning Data Analytics tough?
Data Analytics is not a challenging field. It may require prerequisite knowledge of a few subjects like programming languages such as R, Python, etc., as well as mathematical and statistical topics. But learners can learn these topics along with learning Data Analytics. Overall, to accomplish a data analyst job, you will need to master a wide range of skills. All you need to do is devote a few hours per week to learning these skills, and finally, you will be able to land a data analyst job that’s worth the effort.