Analyticshala | In discussion with Mr. Shekhar Awasthi | Lead Data Scientist, Statlabs

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Analyticshala In discussion with Shekhar Awasthi

Data Analytics leads to taking better decisions. Unknowingly a lot of us make decisions based on the occurrences that involve our result. For that result, we go through a lot of calculations and take decisions based on them. It is wise to say that we are all data scientists in real life. From the time we wake up, we start our day by making decisions based on how we want our day to go. The overall productivity of the day is assured by our planning and our response to the events that occur throughout the day.

In addition to that, we try to put our prior experience into place to avoid hindrances. Conceptually, we run the whole day without knowing that we are making decisions based on previous data, precisely the data scientist’s job. Data Science is all about taking the right decision. Right decisions are always driven by the right processes. Hence, it is more important for one to ensure that the procedure is right and then one must ultimately reach the final goal of the right decision. In this blog, we cover one of our most exciting interviews in Episode 8 of Analyticshala, with Mr. Shekhar Awasthi, Lead Data Scientist, StatLabs. 

Mr. Shekhar focuses on the fact that it is very essential for a data scientist, or a data analyst to identify the right business objective. Often stakeholders do not understand the objective of the business but start working on data. This can often lead to confusing paths and one would not feel content with the research or analysis. Once the right objectives are set, it gives way to analysts for gathering the data, cleaning the data, start working on the data, and finally be able to draw actionable insights from it.

Facts and Myths about Data Science : 

Data Science starts with having the right data at the right place. Analyzing and reporting data is not necessarily Data Science. Identifying key performance indicators of the business is the data science part of reporting. Some people confuse reporting as data science, while reporting is one part of data science. Data Science is a complete picture of various data-related works coming together to solve a problem. Hence, if a person gathers data and draws insights, he is making his way to its completion as a successful data science project, where clearly, it is not yet complete. There could be the addition of machine learning models, artificial intelligence, etc. to understand the core of data science. 

The dynamic nature of Artificial Intelligence : 

It is unfair to say that Artificial Intelligence is a newborn concept. While the term is being widely used in today’s world, the concept has been prevailing for a long time. The word artificial intelligence does not predominantly focus on the ability of a man-made tool to learn how to operate on its own, without human intervention. It primarily focuses on inspiring the technology to such an extent that it reduces a lot of complex formats of data gathering, data wrangling, etc.

The definition of artificial intelligence keeps changing and so is data analysis. As Mr. Shekhar wonderfully describes Artificial Intelligence with a beautiful example, OCR (Optical Character Recognition) was considered a work of Artificial Intelligence long ago, but now the definition of artificial intelligence has gained much more exposure. Now, creating a chatbot could be a work of artificial intelligence in today’s world. Artificial Intelligence can never be considered a static technology. It is and always has been a dynamic concept for the world of data experts. 

Machine Learning: Learning from the historical data: 

It won’t be wrong if we say that machine learning existed long years ago. We may say it existed about 30,000 years ago. Machine learning concepts could be about making the machine learn on its own. However, the reality is that machine learning is done only after learning from historical data. The plots, the trend lines everything comes from the historical data. Hence, repeatedly learning from the historical data teaches about machine learning in a better manner.

However, it does not necessarily mean that there could not be a chance of machine learning if there is no historical data. One can certainly create dummy data and start creating all possibilities that may exist. One starts with a rule-based engine and begins to analyze the predictions and gather data. After gathering the data, one can create statistical and mathematical models that give the correct predictions. There is another term for looking at something mathematically and then predicting its occurrence, without actually working and that is Simulation. 

Skills required by a data science aspirant and how is data science becoming a choice for people from various backgrounds:

Data Science is a multidisciplinary field. As mentioned earlier, data science is all about making the right decisions. Hence, people who have a background in science and other streams can deliver well in the data scientist field. The art lies in creating actionable insights. Understanding the marketplace and competition is one of the vital things that a data scientist must understand. It is very important to know the real business objective. While programming skills are of course needed to collect and extract data, it is also well known that concepts can make anyone learn programming languages easily. It is not necessary to be a computer engineer to understand a basic workflow.

There is unity in diversity when it comes to performing different sets of work involving a whole data science project. Data engineers involve in data cleaning and analyze it by putting it in a presentable form. There are Subject Matter Experts, Consultants, who help in understanding the concepts, working on the concepts. The final motive is to explain the same to the stakeholders in a language that they shall be able to understand. That is the reason for the variety in the industry, domain, and functional area experts. There is space for everyone, and there is a role for everyone. One does not need to have a specific skill set or to be in a specific domain to get into data science.

This codependency not only helps running a successful business but also makes way for better opportunities within the organization in terms of data. For marketing analytics, there will be a need for someone from marketing, for HR analytics, you need someone from HR, and so on. Mr Shekhar explains data analytics to be like multiple islands, living together. He also narrates a beautiful story that makes our understanding of the subject much clearer. Listen to the story here.

Technology is just a facilitator, it is not Data Science:

Some people think that knowing the technology for performing data operations makes one a data scientist. While this is clearly a myth, the role of technology is only about finding the right kind of analysis. For example, an Excel formula finds the average much sooner than manual calculation or the calculation done by calculators. So as we advance, new things will come up and technological advancement will be huge. However, understanding how to operate a machine will never suffice unless you are not fundamentally strong about the concept. Using the technology for the right kind of analysis takes you to the path of data science. You don’t need to master the technology to learn data science. The concepts are more important.

Does the pandemic have any effect on the Data Science industry?

Surprisingly, instead of affecting it badly, it did a lot of good for the industry. As rightly mentioned by Mr Shekhar “the importance of taking right decisions increases when a tragedy strikes.” The need to make the right decisions has gone up with the rise in the pandemic. This gives way to do data analytics the right way. Also, with the rise in the professionals favouring working from home, data analytics became one of the most sought jobs. People who were not in this field earlier want to learn it as data science has become the talk of the town.

To summarise, Mr. Shekhar, data scientist, talked about if a person can tell a story and communicate well with non-technical people. His experience with a lot of data-related things is really unfathomable. He also explains his favorite data science interview question and one of his interview experiences. A consumer of data analysis wants to know more about the plight of the business rather than the burden of data jargon. Nowadays, a lack of communication has developed among few data scientists. It is important to value a client-facing role and focus on the delivery rather than vocabulary.

Watch the complete video to know more about him and learn more about how to prepare yourself for a data science journey. If you are such an enthusiast who wants to kickstart his/her career in data science, then Ivy Professional School has come up with a lot of options and certifications that will help you land your dream job. Contact our wonderful team to start your bright career.

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