1, visual analytics

Big data analytics users have big data analytics experts, as well as ordinary users, but both of them for big data analytics is the most basic requirements of visual analytics, because visual analytics can intuitively present the characteristics of the big data, and at the same time can be very easy to be readers to accept, as simple as looking at the map to talk as clear and concise.

2, data mining algorithms

Theoretical core of big data analysis is data mining algorithms, a variety of data mining algorithms based on different data types and formats in order to more scientifically present the characteristics of the data itself, but also because of these statisticians all over the world recognized by a variety of statistical methods (which can be called the truth) in order to penetrate the data inside, to mine the recognized value. It is also because of these statistical methods, which are recognized by statisticians all over the world as truths, that we can penetrate into the data and uncover the recognized values. Another aspect is also because of these data mining algorithms can be faster to deal with big data, if an algorithm takes years to reach a conclusion, then the value of big data can not be said.

3, predictive analytics

Big data analytics is ultimately one of the application areas is predictive analytics, from big data mining features, through the scientific establishment of the model, and then can be modeled to bring in new data, so as to predict the future of the data.

4, data quality and data management

Big data analysis is inseparable from data quality and data management, high-quality data and effective data management, both in academic research and in the field of business applications, can ensure that the results of the analysis of real and valuable. Of course, more in-depth big data analysis, there are many, many more features, more in-depth, more professional big data analysis methods.

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