‘We Use Machine Learning Algorithms To Save Millions Of Dollars’

Give me an overview of how big data and analytics is being leveraged by Indian enterprises?

Indian Big Data market would touch $ 1 billion by 2015 from nearly $ 0.2 billion in 2011 and global Big Data market would grow at the same rate and touch nearly $ 25 billion by 2015, according to NASSCOM and CRISIL. In only a few big companies, big data analytics is being applied to understand, help and lure customers and to maximize the revenue while others are just storing Big Data in Data Warehouse and trying to create some basic reports.

Gradually, e-commerce, telecommunication, giant retailers, media, oil-exploration and mobile tech business entities are recruiting data science professionals and that too, with hefty packages that indicate the boom of data science and big data analytics.

What are some of the top trends currently in the big data and analytics domain?

Leveraging machine learning algorithms and graph theory: Data scientists leverage machine learning to predict and strategise to get a compact and clear picture of the future business trends by using past data. Every business stakeholder including manager, executive, investor is interested in finding weekly, monthly, quarterly or yearly forecasts in aggregate and detailed form in cross-tabular or/and graphical analysis so that they can optimize their resources and save millions of dollars.

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Using R/Python programming languages: Every bit of data science including machine learning, data visualization, data manipulation, and web based and interactive visualization is done on R or python, mostly. Industry verticals including finance, telecommunications use R or python.

Working on distributed computing including Hadoop clusters: With the emergence of Internet of Things, mobile devices and the increasing interaction among these, data size and its analysis are increasing more than exponentially. So the need of faster real- time analytics and large mobile data storage has been increasing exponentially.

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