Data science -You must know!

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“Data science”, A huge world in the future opportunities.

Harl verian said that “ Statistics is the next sexy job.”

According to Tim “Data is the next Intel Inside.”

Before considering data science, let us consider what is data? DATA is any image, text, documents, videos, numbers and any stuff you see on internet in everyday life. How many times you share your pictures,text or videos on Facebook, twitter, Instagram? Think about that. There’s a database behind a web front end, and middle ware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). Web is full of database and data applications. Using data is not “ Data Science” Applications use this data and utilize them to make data products.

Around 100 hours of videos are uploaded to YOU Tube every minute and it would take 15 years to watch every video uploaded on You Tube.

Every minute we send 204,000,000 emails, generate 1,800,000 Facebook likes, send 278,000 twits and Upload 200,000 photos on Facebook.

570 new websites launch in every minute of everyday.

AT&T is thought to hold the world’s largest volume of data in one unique database – its phone records database is 312 terabytes in size, and contains almost 2 trillion rows.

(Information through third party research)

Data science enables the creation of data products. It is the science of how data is stored, related, analysed and visualized.

Database Making Activities

Social Media

File sharing

File Sharing

Search Engine

Driving (GPS)


Cell Phone Location

Streaming Music or Live

Google is a master at creating data products. Here’s a few examples:

Google’s breakthrough was realizing that a search engine could use input other than the text on the page. PageRank algorithm was among the first to use data outside of the page itself, in particular, the number of links pointing to a page. Tracking links made Google searches much more useful, and PageRank has been a key ingredient to the company’s success.

Spell checking isn’t a terribly difficult problem, but by suggesting corrections to misspelled searches, and observing what the user clicks in response, Google made it much more accurate. They’ve built a dictionary of common misspellings, their corrections, and the contexts in which they occur.

Speech recognition has always been a hard problem, and it remains difficult. But Google has made huge strides by using the voice data they’ve collected, and has been able to integrate voice search into their core search engine.

During the Swine Flu epidemic of 2009, Google was able to track the progress of the epidemic by following searches for flu-related topics.

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