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. 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. Analytical thinking

For example, structured thinking, mind mapping, or Baidu brain mapping, McKinsey-style analysis, and some knowledge such as smart, 5W2H and SWOT will be better. You don't have to master everything, but you must know something.

4. 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.

5. Develop tools and environment

For example: Linux OS, Hadoop (storing HDFS and computing Yarn), Spark, or some other middleware. There are many development tools currently used, such as Java, python and other language tools.

Regarding the study of data analysts, you can learn from the certification body of CDA. Global CDA licensees adhere to the new concept of advanced business data analysis, follow the new norms of CDA professional ethics and code of conduct, give full play to their own data professional ability, promote scientific and technological innovation and progress, and help the sustained economic development.