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Why we confuse Analytics with coding?

There has been many misconceptions regarding Analytics and how to use it as a tool to improve decision making. Lets bust some of the most common myths about Analytics.

Mar 31, 2020

Can You Think? Can You Make Decision? If your Answer is yes for 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 actual is Analytics?
Let us take a simple example to understand it properly. Imagine you stuck at your home because of Covid19 lockdown and every shop you know is also closed. There are 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 supply and for this you will consider several factors, such as:
1. How many people you have to feed?
2. What is the closest shop that will be open?
3. Should you go on bike to get 'aata'(flour) and bring 'suji', as 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 normal food supply?

If you have answers for 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 description i.e., number of people in your family or in your room during 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.
Second question is also representing the same. It is asking for a description of distance of 'n' number of shops in your locality. So, it is also part of Descriptive Analytics.
Now, look after third scenario, it is asking, should you go on bike and risk yourself with lathi charge of police or should you go by walk? Well, in this scenario, you can take 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 hostorical data, it is known as Predictive Analytics.
Now, there is one more, if you are prescribing your friend that look people who are gone out to longer distances on their bikes, 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 favourite game or comic book?

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

All the World's Analytics is Common Sense:
Have you ever heard, you should be expert in statistics to understand Analytics? Well that is true, but this makes people more fearsomoe. This is because we have only seen statistics as a chapter in 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 basket?
The answer is 7. And it was not a rocket science right!

Why do we use tools like RStudio, Python and Tableau in Analytics? It confuses me alot and I dont have a programming background, how do I cope up 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 need to get fear out of their maind. This is because, if you know Analytics is just common sense, and to analyze each and ever scenario, you will understand, we can't do it with our bare mind.
If you are good in 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 able to reuse the model to apply to another problem.
Using these tools enable 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 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 generate 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.

Since many years, Btech students with good programming knowledge have started exploring streams where programming could be used. Now, students from computer background have started entering into Finance, Marketing and now in HR as well.
As they already know programming, therefore they are able to make awesome apps and systems for their departments, which keeps them in highlight and others automatically go in 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 some easy system to a day to day issue.

But this should not make you demotivated. This is 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, they have gold mine of knowledge and he is more valuable to the organization rather than intorducing 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 which weighs more than technical knowledge.

By saying this I am not trying to de mean 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 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 the easiest way possible.

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

If this topic has got some of your interest, we are starting free online course with Analytics, where you are going to learn some of the fun things to perform using Analytical tools and also good basic understanding about the science behind Analytics. Some of the topics that we are going to discuss are as follows:
1. Basics of Analytics
2. Difference between Analytics and Analysis
3. Applications of Analytics - Finance, Markering, HR and Operations
4. Challenges and Risks associated with Analytics
5. Creating some basic Analytical tools with RStudio, Python, HTML, php and Advance Excel
6. Practical use cases of Analytics
7. Use of Power BI for creating simple dashboards

You can send your consent by mailing your name and phone number on below email id to receive alerts of our each course release:

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