What is big data analytics? What are the specific contents of big data analysis?

Big data is all the data that can be collected on the network, the apps you install are collecting your information, and there is also some published information on the network. For example, you can know your consumption level through the information of your online shopping, and big data killing is one of the applications.

The work content of big data analysis can be roughly divided into four steps: data acquisition, data processing, data analysis, and data presentation

1.Data acquisition

Data acquisition seems simple, but it is necessary to grasp the business understanding of the problem and transform it into a data problem to solve, to put it bluntly, what data is needed, from which angles to analyze, define the problem, and then carry out data collection. This part requires data analysts to have structured logical thinking.

2.Data Processing

Data processing requires mastering efficient tools: Excel basics, common functions and formulas, pivot tables, VBA program development equations; followed by Oracle and SQL Sever, which are indispensable skills for enterprise big data analysis; There are also distributed databases such as Hadoop, which also need to be mastered.

3.Analyze the data

Analyzing data often requires various statistical analysis models, such as association rules, clustering, classification, and models. SPSS, SAS, PYTHON, R and other tools, the more the merrier.

4.Data presentation

Visualization tools are available in open-source Tableau and some commercial BI software, which can be mastered according to the actual situation.

Regression analysis is a series of influencing factors and outcomes.

A fitting is to fit an equation and then apply this equation to other events of the same kind.

The so-called regression is the development of a certain ideal state or equilibrium state, through which we can find out what influencing factors and the law of influence on the result.

Big data means that new processing models are needed to have greater decision-making, insight, and process optimization capabilities to accommodate massive, high-growth, and diverse information assets.

1. Visual analysis.

2. Data mining algorithms.

3. Ability to analyze sexuality.

4. Semantic engine.

5. Data quality and data management.

1.Visual analysis: The users of big data analysis include big data analysis experts and ordinary users, but the most basic requirement for big data analysis is visual analysis, because visual analysis can intuitively present the characteristics of big data, and at the same time, it can be very easy to be accepted by readers, just like looking at pictures and talking.

2.Data mining algorithm: the theoretical core of big data analysis is the data mining algorithm, a variety of data mining algorithms based on different data types and formats can more scientifically present the characteristics of the data itself, and it is precisely because of these statistical methods recognized by statisticians all over the world that can go deep into the data, dig out the value of fairness, and on the other hand, because of these data mining algorithms can process big data faster.

3.Sexual analysis ability: One of the most important application areas of big data analysis is sexual analysis, mining characteristics from big data, through the scientific establishment of models, and then new data can be brought in through the model, so as to improve the future data.

4.Semantic Engine: Big data analysis is widely used in network data mining, which can analyze and judge user needs from user search keywords, tag keywords or other input semantics. The result is a better user experience and ad matching.

5.Data quality and data management: Big data analysis is inseparable from data quality and data management, high-quality data and effective data management, whether in academic research or commercial applications, can ensure the authenticity and value of analysis results.

The foundation of big data analysis is the above five aspects.

Literally, big data analytics is defined as "the process of examining large data sets (i.e., big data) containing various data types to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information." ”

Big data analytics companies and businesses often see more business benefits, including more effective marketing campaigns, new revenue opportunities, improved customer service, more efficient operations, and competitive advantage. Companies implement big data analytics because they want to make more informed business decisions. Big data analytics provides data analytics professionals, such as data analysts and modelers, with the ability to analyze big data from a number of different sources, including transactional data and other structured data.

Big data analytics refers to the analysis of huge amounts of data.