1. Mathematical knowledge: Mathematical knowledge is the basic knowledge of data analysts. For junior data analysts, it is enough to know some basic contents related to descriptive statistics and have certain formula calculation ability, and it is better to know commonly used statistical model algorithms. For senior data analysts, knowledge of statistical models is an essential ability, and linear algebra (mainly knowledge of matrix calculation) is best understood.
2. Analysis tools: For junior data analysts, you need to play EXECE |, and you must be skilled in using pivot tables and formulas, and VBA is better. In addition to learning a statistical analysis tool, SPSS is better. For senior data analysts, using analytical tools is the core competence, VBA is the basic necessity, SPSS/SAS/R should be proficient in using at least one of them, and other analytical tools (such as Matlab) depend on the situation.
3. Analytical thinking: such as structured thinking, mind mapping, or Baidu brain map, McKinsey-style analysis, and it is better to know some smart, 5W2H, SWOT, etc. You don't have to master everything, but you must know something. Database knowledge big data big data means a lot of data. When Excel can't solve such a large amount of data, you have to use a database.
4. Development tools and environment: for example, Linux OS, Hadoop (storing HDFS, computing Yarn), Spark or some other middleware. There are many development tools currently used, such as Java, python and other language tools.