Data analytics is mainly to do data collection, mining, cleaning, analysis, and finally the formation of analytical reports with business value.? Big includes the large volume of data, but also includes a wide range of data dimensions.

Big Data Engineer is a very important job to analyze data to find out the characteristics of past events. By introducing key factors, big data engineers can predict future consumer trends. On AliMom's marketing platform, engineers are trying to help Taobao sellers do business by introducing weather data.

For example If it's not hot this summer, it's likely that certain products won't sell as well as they did last year, and in addition to air conditioners and fans, tank tops and swimsuits may be affected by it. Then we'll establish a relationship between weather data and sales data to find categories that are relevant and warn sellers in advance to turnover inventory.

Depending on the nature of the business of different organizations, big data engineers can use data analytics for different purposes.

And there are five skills that a big data analyst needs to master

Know the business. Engaged in data analysis work will need to understand the premise of the business, that is, familiar with industry knowledge, company business and processes, better have their own unique insights, if out of the industry knowledge and company business background, the results of the analysis will only be out of the line of the kite, there is not much use value.

Knowledge of management. Aspect is to build the data analysis framework requirements, such as determining the analysis of ideas need to be used to guide the marketing, management and other theoretical knowledge, if you are not familiar with the theory of management, it is very difficult to build a data analysis framework, the subsequent data analysis is also very difficult to carry out. The other aspect of the role of the data analysis conclusions to put forward a guiding analysis of the recommendations.

Know how to analyze. It refers to the mastery of the basic principles of data analysis and some effective data analysis methods, and can be flexibly applied to the practical work, in order to effectively carry out data analysis. Basic analysis methods include: comparative analysis, group analysis, cross analysis, structural analysis, funnel plot analysis, comprehensive evaluation analysis, factor analysis, matrix correlation analysis. High analysis methods include: correlation analysis, regression analysis, cluster analysis, discriminant analysis, principal component analysis, factor analysis, correspondence analysis, time series and so on.

Knowledge of tools. Refers to the mastery of common tools related to data analysis. Data analysis method is the theory, and data analysis tools is to realize the theory of data analysis method tools, in the face of more and more huge data, we can not rely on the calculator to analyze, we must rely on powerful data analysis tools to help us complete the data analysis work.

Knowledge of design. Understand the design refers to the use of charts and graphs to effectively express the analytical views of data analysts, so that the results of the analysis of the eye clear. The design of the chart is a big question, such as the choice of graphics, layout design, color matching, etc., all need to master certain design principles.