Shulou(Shulou.com)06/02 Report--

This article will explain in detail what are the basic skills of big data. The editor thinks it is very practical, so I share it for you as a reference. I hope you can get something after reading this article.

Big data's basic techniques include data collection, data access, infrastructure, data processing, statistical analysis, data mining, model prediction, and result presentation.

Big data's basic techniques include data collection, data access, infrastructure, data processing, statistical analysis, data mining, model prediction, and result presentation.

1. Data collection: in big data's life cycle, data collection is in the first link. According to the application system classification of data generated by MapReduce, big data's collection mainly comes from four sources: management information system, Web information system, physical information system and scientific experiment system.

2. Data access: big data adopts different technical routes, which can be roughly divided into three categories. Category 1 mainly deals with large-scale structured data. Category 2 mainly deals with semi-structured and unstructured data. The third category is a mixture of structured and unstructured big data.

3. Infrastructure: cloud storage, distributed file storage, etc.

4. Data processing: for different data sets, there may be different structures and patterns, such as files, XML trees, relational tables, etc., showing the heterogeneity of data. For multiple heterogeneous data sets, further integration processing or integration processing is needed to collect, organize, clean and transform the data from different data sets and generate a new data set to provide a unified data view for subsequent query and analysis processing.

5. Statistical analysis: hypothesis test, significance test, difference analysis, correlation analysis, T-test, variance analysis, chi-square analysis, partial correlation analysis, distance analysis, regression analysis, simple regression analysis, multiple regression analysis, stepwise regression, regression prediction and residual analysis, ridge regression, logistic regression analysis, curve estimation, factor analysis, cluster analysis, principal component analysis, factor analysis, Fast clustering method and clustering method, discriminant analysis, correspondence analysis, multivariate correspondence analysis (optimal scale analysis), bootstrap technology and so on.

6. Data mining: at present, it is necessary to improve existing data mining and machine learning technologies, develop new data mining technologies such as data network mining, special group mining, graph mining, etc.; break through big data fusion technologies such as object-based data connection and similarity connection; break through domain-oriented big data mining technologies such as user interest analysis, network behavior analysis, emotional semantic analysis and so on.

7. Model prediction: prediction model, machine learning, modeling and simulation.

8. The results are as follows: cloud computing, label cloud, diagram, etc.

This is the end of the article on "what are the basic skills of big data". I hope the above content can be helpful to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.