A Quote by Gurjeet Singh

Data is cost. It takes money to create data, store it, clean it, and throw resources at it to learn anything from it. — © Gurjeet Singh
Data is cost. It takes money to create data, store it, clean it, and throw resources at it to learn anything from it.
One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.
Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.
We get more data about people than any other data company gets about people, about anything - and it's not even close. We're looking at what you know, what you don't know, how you learn best. The big difference between us and other big data companies is that we're not ever marketing your data to a third party for any reason.
People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.
Tape with LTFS has several advantages over the other external storage devices it would typically be compared to. First, tape has been designed from Day 1 to be an offline device and to sit on a shelf. An LTFS-formatted LTO-6 tape can store 2.5 TB of uncompressed data and almost 6 TB with compression. That means many data centers could fit their entire data set into a small FedEx box. With LTFS the sending and receiving data centers no longer need to be running the same application to access the data on the tape.
The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.
The key to a solid foundation in data structures and algorithms is not an exhaustive survey of every conceivable data structure and its subforms, with memorization of each's Big-O value and amortized cost.
If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.
We know now data is so powerful, and you can learn so much about yourself and creating product with data.
We all say data is the next white oil. [Owning the oil field is not as important as owning the refinery because what will make the big money is in refining the oil. Same goes with data, and making sure you extract the real value out of the data.]
A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.
Scientists learn about the world in three ways: They analyze statistical patterns in the data, they do experiments, and they learn from the data and ideas of other scientists. The recent studies show that children also learn in these ways.
Data!data!data!" he cried impatiently. "I can't make bricks without clay.
In the US, you even lose legal rights if you store your data in a company's machines instead of your own. The police need to present you with a search warrant to get your data from you; but if they are stored in a company's server, the police can get it without showing you anything.
We are now at a point in time when the ability to receive, utilize, store, transform and transmit data - the lowest cognitive form - has expanded literally beyond comprehension. Understanding and wisdom are largely forgotten as we struggle under an avalanche of data and information.
I don't believe in data-driven anything, it's the most stupid phrase. Data should always serve people, people should never serve data.
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