Sample Employee Data

The time is 1948, the location is Northeast China. Liaohsi into the critical phase. For the commander of the four field army Lin Biao, the most important goal after the Jinzhou is to defeat the Kuomintang new six troops. The method used by Lin Biao is to listen to the "intelligence report" every day, and the officers on duty should read out the situation and seizure of the troops. It's almost uniform data, and it's boring. Until one day, Lin Biao suddenly discovered that in a two-armed encounter in the Hu Shack, the ratio of the spear to the spear was slightly higher than the other battles, and the proportion of the car and the cart was slightly higher than the other battles.

The time is 1948, the location is Northeast China. Liaohsi into the critical phase. For the commander of the four field army Lin Biao, the most important goal after the Jinzhou is to defeat the Kuomintang new six troops. The method used by Lin Biao is to listen to the "intelligence report" every day, and the officers on duty should read out the situation and seizure of the troops. It's almost uniform data, and it's boring. Until one day, Lin Biao suddenly discovered that in a two-armed encounter in the Hu Shack, the ratio of the spear to the spear was slightly higher than the other battles, and the proportion of the car and the cart was slightly higher than the other battles.

The time is 1948, the location is Northeast China. Liaohsi into the critical phase. For the commander of the four field army Lin Biao, the most important goal after the Jinzhou is to defeat the Kuomintang new six troops. The method used by Lin Biao is to listen to the "intelligence report" every day, and the officers on duty should read out the situation and seizure of the troops. It's almost uniform data, and it's boring. Until one day, Lin Biao suddenly discovered that in a two-armed encounter in the Hu Shack, the ratio of the spear to the spear was slightly higher than the other battles, and the proportion of the car and the cart was slightly higher than the other battles.

The time is 1948, the location is Northeast China. Liaohsi into the critical phase. For the commander of the four field army Lin Biao, the most important goal after the Jinzhou is to defeat the Kuomintang new six troops. The method used by Lin Biao is to listen to the "intelligence report" every day, and the officers on duty should read out the situation and seizure of the troops. It's almost uniform data, and it's boring. Until one day, Lin Biao suddenly discovered that in a two-armed encounter in the Hu Shack, the ratio of the spear to the spear was slightly higher than the other battles, and the proportion of the car and the cart was slightly higher than the other battles.

The U.S. Foreign Policy magazine website recently published the article titled "Rethinking Big Data-why the rise of machines is not exactly what people boast about", the author of Microsoft Research chief researcher, the MIT Civic Media Center guest Professor Kate Claufford. "Big Data" is the current buzzword, the article says, and is a versatile way for the technology community to solve the world's most intractable problems. This term is generally used to describe the art and science of analyzing massive amounts of information to find patterns, to collect insights and to predict answers to complex questions. It may sound ...

The U.S. Foreign Policy magazine website recently published the article titled "Rethinking Big Data-why the rise of machines is not exactly what people boast about", the author of Microsoft Research chief researcher, the MIT Civic Media Center guest Professor Kate Claufford. "Big Data" is the current buzzword, the article says, and is a versatile way for the technology community to solve the world's most intractable problems. This term is generally used to describe the art and science of analyzing massive amounts of information to find patterns, to collect insights and to predict answers to complex questions. It may sound ...

Many companies are exploring how to tap into the value of big data, but one type of company has to be good at doing it: data analysis providers.     The business model of these companies determines that they must be able to effectively and consistently provide the data products and services that customers are willing to pay for. Here's a great data analysis provider-ComScore, how to achieve the right results through 3 key factors (a convenient, scalable platform, an employee team that understands data analysis, and a deep understanding of the customer).

Many companies are exploring how to tap into the value of big data, but one type of company has to be good at doing it: data analysis providers. 　　The business model of these companies determines that they must be able to effectively and consistently provide the data products and services that customers are willing to pay for. 　　Here's a great data analysis provider-ComScore, how to tap into the value of large data through 3 key factors (a convenient, scalable platform, an employee team that understands data analysis, and a deep understanding of customers). 1 easy, scalable data flat ...

Big data, first of all, means massive amounts of data, and radically alters the idea of data utilization. It is possible to analyze all the data that may be produced, which will make people change the sampling methods used in the past for a long time, and be good at responding to the mass (all) data quickly and even in real-time. 　　The sampling analysis in the past leads to the deviation of the sample distribution, which distorts the conclusion of the finite data, while the data mining analysis in the large data age does not mean that the analysis is less difficult, and the contrary may be higher. Of course, in the big data age, there is usually no need to analyze all the data. "Large number ...

Big data, first of all, means massive amounts of data, and radically alters the idea of data utilization. It is possible to analyze all the data that may be produced, which will make people change the sampling methods used in the past for a long time, and be good at responding to the mass (all) data quickly and even in real-time. The sampling analysis in the past leads to the deviation of the sample distribution, which distorts the conclusion of the finite data, while the data mining analysis in the large data age does not mean that the analysis is less difficult, and the contrary may be higher. Of course, in the big data age, there is usually no need to analyze all the data. "Big Data Age ...