I. Programming and database
Generally speaking, data scientists need to have a professional background in programming and computer science, and have the Hadhop needed to deal with big data. It is necessary to master the large-scale parallel processing technology and skills related to machine learning such as Mahour. Huo Ying IT training suggests that python is generally used to obtain data, organize data and display data with matplotlib.
Second, mathematical statistics and data mining
Besides mathematics and statistics, you need to master more skills and use mainstream statistical analysis software, such as SPSS and SAS. Among them, the open source programming language for statistical analysis and its running environment "R" have attracted much attention in recent years. The advantage of R lies not only in its rich statistical analysis library, but also in its visualization and high-quality graphics generation function, and it can also be run by simple commands. In addition, Huo Ying IT Training found that it also has a package extension mechanism called cran, which can use functions and data sets that are not supported in the standard state by importing extension packages.
Third, data visualization.
The quality of information depends largely on the way information is expressed. By analyzing the meaning of digital list data, the Web prototype is developed, and the services such as charts, maps and dashboards are unified by using external API, so that the analysis results are visualized. This is one of the most important skills that data scientists need to master.
Fourth, leadership and soft skills.
Data scientists should not only have the brains of hackers, but also be curious about data. In addition, Huo Ying Computer Training believes that they need to be enthusiastic, influential and creative about their business in order to solve problems.