The Truth Behind the scope of Data analytics?

Much has been said about the word ‘Data analytics. Some are true, and most are just unsubstantiated marketing. Data analytics is not a new kid in the town, but quite the opposite. What is new is data analytics courses.

I’ll also show you why these courses have come up and will present you with the data for you to decide. So let’s start this exploration.

The Real Data Analytics

As opposed to the sci-fi perception of data analytics, machine learning and data science. The reality is quite the opposite. Ask yourself this question, why would you unnecessarily analyse data? There has to be a problem which some tool can solve.

So let’s look at some problems that data analytics does solve

Recommendation Engines

So before I discuss the technical terms here, you need to understand the basic concept. Since we live in a world with too many options, finding options is made more accessible by using some traits. So, for example, if I like supplements related to, let’s say, de-ageing, a recommendation could probably help save time.

Some examples include; Amazon, Netflix, Udemy, Bumble Bee, Tinder etc.

However, the recommendation here is not sci-fi, as opposed to what you might think. It’s just categorising your activities into a cluster and then matching it with what I might have in my inventory.

Fraud Analytics

In finance, especially banks, I could use a system which identifies what is not normal behaviour. For example, what location of the ATM withdrawal do I usually make? Also, maybe, What location am I in usually? Or Maybe what is the size of the transaction I do online?

Hence, we analyse such behaviours over time and then use a formula like the Knearest algorithm to find what is not normal and then flag it.

Medicine & Drug Development

Here again, what we are trying to do is to take lots of actual patients’ data. Also, in the process, identifying which chemical works on what kind of disease trait. Combined with whether a patient will react positively to the medicine. Instead of doing this life on actual patients, we could simulate a drug on a computer. In the process, saving a lot of time.

So, I hope you are getting the point here. We solve problems now more efficiently by using data. That is also possible because we now actually have data.

Is Data Scientist a Glorified Coder?

Believe it or not, I wouldn’t wholly disagree with the notion. See for yourself below; this is a job description for a data scientist in the Pharma sector.

Pharma Data Scientist Job Description
Pharma Data Scientist Job Description

The pharma company doesn’t do the data analytics but outsources to companies like these to get the job done. So if I asked you a question, Is the pharma scientist influential or the data scientist? Then what would be your answer?

In a similar way, is a company which develops and uses software more critical or an IT services company? I’ll leave that answer to you.

Time spent on data science work as per categories
Time spent on data science work as per categories

The Real Start in Data Science- The Truth

Having a little background on how the data science industry works, don’t get all pumped up. A chef he doesn’t start his career as a chef but as an assistant who has to cut a hundred kgs of onion daily. Also, in data science, you don’t start your career with big projects where you test models but the onion, in this case, is (Just structuring data).

I don’t want to say anything more; look at the amount of time spent on “Cleaning & Organising data”. While building the actual algorithm is just 7%. So, 80% of the time, a data science team ensures the data is there. Now, do you think you need more data cleaners and organisers or data scientists? I could comfortably assume that we need more data organisers, and that’s where the jobs are.

The sad truth, though, is that freshers expect the 7% glorious work and most likely quit the industry. Similarly, most of you would be fascinated with what the data scientist earns but leave the salary earned by the cleaners.

Here is a job description for a data analyst whose job is to clean.

Expectation versus reality of data science jobs
Expectation versus reality of data science jobs

The Real Salary in Data Science

Now, I know those big salaries are given by courses online and offline. Also, the ultimate tagline, which makes me laugh, is “Data science is the sexiest Job of the century”.

Really? What’s so sexy about it?

Median Salary As per experience in Data Science
Median Salary As per experience in Data Science

The question to ask is, how are these salaries any different compared to an IT Professional or finance professional?

So, to conclude, there is nothing different about salaries on an average level in any of the industries. Unless you are some genius, who gets hired for his algorithm creation by google, Facebook or linked in. Just like any other field where outliers exist.

The Real Education Required in Data science, or is it a hoax?

The last myth to break, and then I am gone. Do you need a fancy degree or a course which costs almost as expensive as a home loan down payment?

To break the myth again, the answer is no.

Let’s look at some actual data again on how companies hire and what they require.

To conclude the above data, as a data scientist, you should know how to. No matter what education you do, the main deciding factor is experience. Look at the MBA from the top tier with 0-3 years experience; he earns around 10-12 Lacs. Similarly, look at a non-tier 1 MBA with 3-6 years of experience who ends up making the same salary.

Also, an interesting observation is all types of education with more than 12 years of experience earn above 30 lacs.

The Real Skills Required( Not a course)

As a data science entrant, no one course can help. For a straightforward reason, data understanding requires much more than a data science course. First things first, let’s start with some data.

source: analyticsIndiamagazine
source: analyticsIndiamagazine


You have to learn to code, which language? Well, for now, python or maybe R. However, you must keep learning new languages as they come and become famous in this field. However, learning python and learning python for data science are two different things.

You need to learn python for data science and be able to do that easily. Just like a language, it’s not fun when you struggle to pronounce the words.

Linear Algebra or mathematics

All the algorithms are readily available to deploy in python. Seldom can people understand how that algorithm works. So take up a maths book or course which digs deep inside mathematics.

Basic Machine Learning

Please don’t go overboard with machine learning algorithms; you will hardly use them. However, have a basic understanding of actually using it with cases.

Problem Identification and Solution

Practice with cases on identifying newer problems and, first, without coding them solve them on paper. The more you practice this, the more you become better.


You should not spend more than INR 5000 or $100 to train for these skills, no matter what education you come from. The rest needs to be learned while you earn.