KACHARAGADLA

“Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”  This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them. Here, we focus on one of the more important distinctions as it relates to your career: the often-muddled differences between data analytics and data science.  Data Analytics vs. Data Science  While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

What does big data know about  you ? Quite a lot. Every time we use a computer, access our phones, or open an app on a tablet, we’re leaving a digital trail. Most people are vaguely aware that Google knows what they’ve searched for, or that Facebook knows who their friends are, but it goes much, much deeper than that. I’ve compiled a list of 21 things  Big Data  knows about almost every one of us — right now: Of course,  Google knows what you’ve searched for . So do Bing, Yahoo!, and every other search engine. And your ISP knows every website you’ve ever visited. Ever (even in private browsing). Google also knows your age and gender  — even if you never told them. They make a pretty comprehensive ads profile of you, including a list of your interests (which you can edit) to decide what kinds of ads to show you. Facebook knows when your relationship is going south.  Based on activities and status updates on Facebook, the company can predict (with scary accuracy) whether or

Startups, in order to stay competitive in the dynamic business world, they need to have an unconventional wisdom of marketing. With the availability of limited budget for marketing, startups have to deal with the highest conversion rate of consumers. The key differentiator between two startups is ‘pace’. Things need to be done in a very fast pace for startups to be competitive against large companies. And in order to react to market conditions and changing consumer trends, startups today rely heavily on big data analytics. Big data can provide numbers backed by insights on consumer behaviour and can play a critical role in helping startups in areas like marketing, advertising, operations, inventory management, and customer service etc. For example, for a startup organisation, product marketing act as a growth catalyst in establishing brand value in the market, which is very costly and usually eats up a huge part of the budget. Big data can help startups in identifying and r