Shulou(Shulou.com)06/03 Report--

Data analysis refers to the process of using appropriate statistical analysis methods to analyze a large number of collected data, extract useful information and form conclusions at the same time, that is, to study and summarize the data in detail.

Data analysis needs to master mathematical knowledge and analysis tools, mathematical knowledge includes statistics, probability theory and mathematical statistics, multivariate statistical analysis, time series, data mining; tools generally should master Excel, SQL, R, Python and so on. You need to learn basic data processing and analysis methods, master advanced data analysis and data mining methods (multiple linear regression, Bayesian, neural network, decision tree, cluster analysis, association rules, time series, support vector machine, ensemble learning, etc.) and visualization techniques.

Big data is a collection of data that cannot be captured, managed, and processed with conventional software tools within an affordable time frame. It is a massive, high-growth, and diverse information asset that requires new processing models to have stronger decision-making power, insight, and process optimization capabilities. Big data analysis is defined in the book Big Data Age as follows: instead of using shortcuts such as random sampling survey analysis, all data are analyzed and processed, regardless of the distribution state of the data, because sampling data needs to consider whether the sample distribution is biased and consistent with the population, and hypothesis testing is not considered. This is also a difference between big data analysis and general data analysis.

The core difference between data analysis and big data analysis is that the scale of data processed is different, resulting in different skills of practitioners in the two directions. In the CDA talent competency standard, data analysts and big data analysts are defined from five aspects: theoretical basis, software tools, analysis methods, business analysis and visualization.

Accumulate experience and transform business objectives into data analysis objectives; be familiar with common algorithms and data structures, and be familiar with enterprise database architecture construction; be skilled in dimensional analysis for different analysis subjects, and be able to collect and extract information from massive data; complete the processing and analysis of massive data through related data analysis methods and in combination with one or more data analysis software.

Write a report that reflects the overall process of data mining, describing the collection of information, the construction of models, the verification and interpretation of results, and the evaluation, optimization and decision-making of the industry.

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