Introduction - Data Science -Part 1

Hi everyone, this pandemic gave me time to learn new things & explore some of the new area to enhance my knowledge. One of the areas which I am exploring from past few month is Data Science and I am totally amazed after knowing what Data Science is & how it can affect outcome of any Business.

Data Science is helping companies to predict what customer/consumers wants & accordingly these companies are changing their product line, product features, making promotional offers, giving new product recommendation, and doing so many things. Best thing about Data Science (DS) is you can apply DS concept in any field like Medical Science, Agriculture, Manufacturing, FMCG, BFSI, Aerospace, Infrastructure, SCM, E-Commerce, Telecom, Entertainment, Game, etc.

So, what is Data Science?  

As per the Wikipedia definition “Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.”

As per my learning, "DS is Understanding business problem, collecting data from different sources, analyzing & extracting meaningful insights from data & presenting to the relevant audiences by using Scientific & Mathematical method”. Data science is more than the analysis of large data sets. Data science involves a plenty of disciplines and expertise areas to produce a thorough, holistic, and refined look into raw data.

Some of the business problem where Data Science is helping companies to find solution are:

·        Text/Pattern/Image/Audio recognition

·        Fraud Detection

·        Earthquake Prediction

·        Drug/Medicine Development

·        Augmented Reality

·        Cross sell recommendations

·        Chatbots

·        Sales Prediction

·        Dynamic Pricing in Travel

·        Helping team to plan their game against their opponent 

And there are so many countless fields where Data science is very useful to provide solution.

Another buzz word comes in our mind is “Data Scientist”. So, who is Data Scientist? Data Scientist are big data wrangler & problem solver. They work on data to solve big/small problem. Typically Data Scientist’s work  involves understanding business problem & based on the problem  gathering data and then clean, analyze & visualize the data, build the model & present to the relevant audience, and help them to take business decisions based on their recommendation.Based on the work culture, a Data Scientist should have good business domain knowledge, analytical mindset, ability to work/play on data, computer science knowledge, expertise in statistics & probability, good communication & presentation skills. 

Data Science(DS) can be seen as the incorporation of multiple parental disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, and many more. Due to the omnipresence of DS every companies are looking to hire DS professionals. There are lot of work comes under DS which can be done by many professionals like data collection, data clearing, data mining, data analytics, modelling, predictive analytics etc.

Data Science is very hot cake in every company & every company want to hire good DS professionals. But at the same time, most of the companies are facing a big issue in Hiring good Experienced DS professionals and question arise here why companies are unable to hire good, experienced DS professionals. As per my research & understanding I can say that lack in fundamental of Data Science concept, lack in Domain Expertise, unbalanced Geographical Distribution of the DS Talent and High Salary Expectation are the few gaps which are not helping companies to hire good DS professionals. 

I will suggest to every data science learner, please work on fundamental of the DS concepts & build domain expertise if you want a good career in Data Science field. Along with this companies need to work on their salary structure & need to make strategy on how they can minimize unbalanced geographical distribution of the DS talent.  

There are lot of things need to cover under DS which I will continue in my upcoming articles.

Thanks for reading this article. Please leave your comment (no matter good/bad) in comment box so in my next article I can work on those area.