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. For data mining engineers, in addition to statistics, various algorithms also need to be skillfully used, and the requirements for mathematics are the highest.

2. Analytical tools.

For junior data analysts, you need to be able to play Excel and skillfully use pivot tables and formulas, VBA is better. In addition, you must learn a statistical analysis tool, and SPSS is better as an introduction.

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. Programming language.

For junior data analysts, you can write SQL queries, Hadoop and Hive queries if necessary, basically. For senior data analysts, besides SQL, they also need to learn Python, which is used to acquire and process data, and get twice the result with half the effort. Of course, other programming languages are also possible.