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The content of this article mainly focuses on data statistics, data mining, what is the difference between big data and OLAP, the content of the article is clear and clear, it is very suitable for beginners to learn, it is worth reading. Interested friends can follow the editor to read together. I hope you can get something through this article!

In big data's field, we often see professional words such as data mining, OLAP, data statistics and so on. If only literally, it is difficult for us to make clear the meaning and difference of each word. Today, we will explain the difference between data mining, big data, OLAP and data statistics through some examples of the application of big data in colleges and universities.

I. data analysis

Data analysis is a big concept. In theory, any process that calculates and processes data to draw some meaningful conclusions is called data analysis. From the complexity of data itself, as well as the complexity and depth of data processing, data analysis can be divided into the following four levels: data statistics, OLAP, data mining, big data.

II. Data statistics

Data statistics is the most basic and traditional data analysis, which has existed since ancient times. It refers to sorting, screening, operation and statistical processing of the data through statistical methods, so as to draw some meaningful conclusions.

For example, the top 10% of students in the whole grade can be exempted from the postgraduate examination according to their average scores from high to low.

Traditional query and reporting tools tell you what is in the database (What happened)

III. OLAP

Online Analytical processing (On-Line Analytical Processing,OLAP) refers to online multidimensional statistical analysis based on data warehouse. It allows users to observe a measure online from multiple dimensions to support decision-making.

For example, when schools enroll students, they should decide on the enrollment targets in Jiangsu this year, not simply with reference to last year's plan, but with reference to the data accumulation of multiple dimensions. Schools should make reasonable decisions with the support of these data.

OLAP will further tell you what will happen next (What next), and what will happen if I take such measures (What if)

4. Data Mining

Data mining refers to finding unknown, possibly useful and hidden rules from massive data. We can find some deep-seated reasons that can not be obtained by observing charts through various algorithms such as association analysis, clustering analysis, time series analysis and so on.

For example, the school found that the failure rate of main courses such as higher mathematics is increasing year by year, which is generally believed to be caused by careless learning, but the effect of doing a lot of work is not clear to the county, at this time through data mining.

Targeted management measures can be taken to solve this problem.

Big data

Big data refers to a very large data set which is difficult to collect, store, manage, analyze and use with the existing computer software and hardware facilities. Big data has the characteristics of large scale, miscellaneous types, high speed, low value density and so on (4V characteristics). Big data's "big" is a relative concept, there is no specific standard, if a standard must be given, then 10-100TB is usually called big data's threshold.

From the perspective of data analysis, at present, the vast majority of school data application products are still in the stage of data statistics and report analysis, few can achieve effective OLAP analysis and data mining, and very few can reach the application stage of big data, at least the effective big data set has not been used.

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