Why we confuse Analytics with coding?

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Can You Think? Can You Make a Decision? If your answer is yes to both these questions ... Well you are an expert in Analytics

What is Analytics?
Analytics is the discovery, interpretation, and communication of meaningful patterns in data.

Well, this definition can be a bit confusing to many people. So, what in actuality is Analytics?
Let us take a simple example to understand it properly. Imagine you are stuck at your home because of Covid19 lockdown and every shop you know is also closed. There is police personnel with their batons waiting for someone to come out.
Now, you got to know that your ration supply is getting low day by day. So, what will be your plan of action?
Obviously, you will go out and buy some more supplies and for this, you will consider several factors, such as:
1. How many people do you have to feed?
2. What is the closest shop that will be open?
3. Should you go on a bike to get 'aata'(flour) and bring 'suji', as the police would start lathi-charge?
4. What are the things necessary to eat when you are at home?
5. Do you also need medicines with a normal food supply?

If you have answers to these all questions, well you are an expert in Analytics. How?
Well, Analytics is nothing but thinking about all the scenarios that have happened, are happening, and might happen.
If you will take the above example, the first question is more of asking for a description i.e., the number of people in your family or in your room during the lockdown. Therefore, this is a part of 'Descriptive Analytics.Now, you can directly say there are 4 people in my family or you can make a beautiful chart and represent all people with some complicated character to make it look geeky.
The second question is also representing the same. It is asking for a description of the distance of 'n' number of shops in your locality. So, it is also part of Descriptive Analytics.
Now, look after the third scenario, it is asking, should you go on the bike and risk yourself with lathi charge of police or should you go by walk? Well, in this scenario, you can take the example of several other people who have gone by walk or via bike and create a time series analysis to estimate if people who are gone via bike have got more lathi charges by police or people who are gone by walk. So, when we are trying to predict whether or not we are going to get lathi-charged on the basis of historical data, it is known as Predictive Analytics.
Now, there is one more, if you are prescribing your friend that looks at people who are gone out longer distances on their bikes, and have had some instances with police, then this is more of Prescriptive Analytics.

Have you seen one thing? You were using Analytics from the day one you were born.
Shall I cry, then only I will get food to eat?
Shall I put tantrums, then only I will get my favorite game or comic book?

The reason why people are afraid of Analytics is that some people have made sure that we will drag Analytics into a programming language.

All the World's Analytics is Common Sense:
Have you ever heard, you should be an expert in statistics to understand Analytics? Well, that is true, but this makes people more fearsome. This is because we have only seen statistics as a chapter in a maths book. Remember, statistics is a part of applied mathematics. For Example, there are 4 Apples, 3 Oranger, and 6 Potatoes in a basket, so what is the frequency of fruits in the basket?
The answer is 7. And it was not rocket science right?

Why do we use tools like RStudio, Python, and Tableau in Analytics? It confuses me a lot and I don't have a programming background, how do I cope with it?
This is one of the most interesting questions whenever someone thinks of taking Analytics as his or her subject further. To answer this simply, one needs to get fear out of their mind. This is because, if you know Analytics is just common sense, and to analyze each and every scenario, you will understand, we can't do it with our bare mind.
If you are good at finding solutions, you can solve huge Analytical issues in a simple Excel Sheet.

The reason why we use some analytical tools like RStudio and Python is that we can calculate each and every scenario on a sheet of paper, but, It is not practical. Imagine calculating one Analytical issue and then not being able to reuse the model to apply to another problem.
Using these tools enables us to reuse already built models to solve complex issues. Imagine schools making you again create the theory of relativity. E=mc^2.
But that is just a waste of time and money.

And for people that say they are afraid to code, remember one thing, you were never able to speak Hindi or English or your mother tongue when you were born. And to make it more obvious, we don't really have to code in Analytics, we just need to see, which formula to use where. But slowly and steadily you start understanding the codes and then you start modifying these codes and generating different results altogether.

Now, if fear of analytics is out of your mind, I will like to describe why it has become more difficult for people to differentiate between Analytics and coding.

For many years, Btech students with good programming knowledge have started exploring streams where programming could be used. Now, students from computer backgrounds have started entering into Finance, Marketing and now in HR as well.
As they already know to program, therefore they are able to make awesome apps and systems for their departments, which keeps them in highlight and others automatically go in the limelight. This is dangerous as many skills are also becoming extinct because of this issue and because of this team managers now want someone who can code and make an easy system for a day-to-day issues.

But this should not make you demotivated. This is the time when you can stand up and say, 'A good programmer can give you a system as per your requirement, but a person who has core knowledge of the subject can provide solutions that a programmer can't', and we can hire anyone to create an app or system if the problem is recurring in nature.
This is definitely time to upskill ourselves with programming knowledge, but one should not see it as a drawback as people who have spent their whole lives in a particular department, have a gold mine of knowledge and he is more valuable to the organization rather than introducing someone who can code and create systems.
Remember, you need an architect first to create a plan and provide data to create the system. Any engineer or construction worker cannot solve an issue that he has seen first time in his or her life. There are experiences that matter most and this is something that weighs more than technical knowledge.

By saying this I am not trying to demean any subject or background. But this is the reality. Most of the students become confused because we are not able to assist them with proper guidance regarding a particular subject. We should teach students to see the practical use of Analytical Tools and not just raw coding.
We should not show how to create Word Clouds first, but try to explain why Word Clouds and Sentiment Clouds are important and how we can create them in the easiest way possible.

Has anyone told you, that you can make a Word Cloud using MS Word?
This is just an example of why we make a particular thing so complex. We make students make Word Clouds without making them understand they can make it easy with just two clicks on MS Word.
Also, we never try to make them understand what is used in each and every line of the code if we are using tools like RStudio or Python.

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