Introduction to Data Science
Imagine you are manager of a shopping mall. Can you arrange items that you sell in such a way that you will attract more customers? More so, can you deduce which are those unseen/unheard or seemingly absurd trends in user behavior that can possibly improve your revenue? Do you have any intelligence which can tell you that on Friday night, a single male customer buying baby nappies most probably will buy beer cans too so that on weekend he would enjoy beer while baby-sitting?
The key to such intelligence is the word for which most of the world perceives different meaning. Data Science. Some say that they do it without knowing what is it while some others fear to utter the word.
There is much debate among scholars and practitioners about what data science is, and what it isn’t. Does it deal only with big data? Is data science really that new? How is it different from statistics and analytics?
One way to consider data science is a next generation for interdisciplinary field like business analysis that includes computer science, modeling, statistics, analytics, and mathematics. Data science makes use of tools, techniques and domain experience to enable extraction of more useful knowledge from the available data. According to Mike Loukides, VP, O’Reilly Media, Data scientists gather the data, massage it into a tractable form, make it tell its story, and present that story to others.
At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. With such automated methods turning up everywhere from genomics to insurance, data science is creating new branches of science and influencing areas of social science and the humanities. The trend is expected to accelerate in the coming years as data from mobile sensors, sophisticated instruments and web grows.
Typically, a Data Scientist signifies an evolution from the business/ data analyst role. Both should have a solid groundwork in computer science and applications, modeling, statistics, analytics and mathematics. What sets the data scientist apart is robust business insight, along with the capability to communicate findings to both business and IT leaders so as to help organization to deal with business challenge.
According to the McKinsey Global Institute, the U.S. alone could face a shortage of 140,000 to 190,000 professionals with data science skills by 2018. McKinsey found that sectors such as electronic products, information technology, finance and insurance, and government will likely gain the most value from using big data, and thus employ many of the world’s data scientists.
So, this was all about what is data science exactly.
Have you ever wandered what is data analytics then? What are the differences between this two terms or are they synonyms for each other?
Well, read this article to understand answers to this questions.
Also, read about Machine learning fundamentals, here: