Data Science is the new buzz in the tech world. Every other startup is looking to hire ‘Data Scientists’, putting them in great demand. The main aim of data science is to interpret complex and rich data sources to get useful information out of it.
Data Science uses theories from many other fields like mathematics, probability models, machine learning, statistical learning,data engineering, visualisation, uncertainty modelling, data warehousing, high performance computing and many others.
What really makes Data Science so fascinating is that it can be applied in so many diverse fields like linguistics, history, culture, statistics giving very interesting results. A good example of data science that I came across recently was looking through digitised books for the mood of authors writing throughout time. How was that done? Google has converted all the books from the 20th century in n-grams(phrases of n words). Using 1-grams assigning mood score to each word from WordNet.(For example laugh is a joy word and sorrow is an extreme sadness word.) Then using simple math, taking the count of the mood words normalised over the word ‘the’ and taking the sum of it. The conclusion is on the graph where we see a dip, indicating sadness in books after the World War 2 which a very interesting result in history! This definitely is not Rocket Science.
How I understand Data Science? For me data science is all about the data coming in from sensors, going to the cloud and it being processed/stored using big data techniques. This is where data science comes into play. Data Science is responsible to find a meaningful conclusion or results from all this data. These metrics are very useful for making decisions. I see data science growing more relevant with more and more accessible data through the Internet of Things. IoTs will definitely lead the surge for data science!