1, mathematical knowledge

Mathematical knowledge is the basic knowledge of data analysts. For primary data analysts, understand some of the basic content related to descriptive statistics, have a certain formula calculation ability can be, understand the common statistical model algorithm is a plus.

For senior data analysts, statistical modeling-related knowledge is a must, linear algebra (mainly matrix calculation-related knowledge) is also best to have some understanding.

For data mining engineers, in addition to statistics, all kinds of algorithms need to be skilled in the use of math is the highest requirement.

So data analysis is not necessarily very good math skills to learn, as long as it depends on which direction you want to go, data analysis also has a bi? The data analytics also has a literary side to it. s side, especially for girls, you can go in the direction of document writing.

2, analytical tools

For primary data analysts, play around with Excel is a must, pivot tables and formulas must be skilled in the use of VBA is a plus. In addition, it is necessary to learn a statistical analysis tools, SPSS as a start is better.

For advanced data analysts, the use of analytical tools is the core competency, VBA is basically a must, SPSS/SAS/R should be skilled in the use of at least one of them, other analytical tools (such as Matlab) depending on the situation.

For data mining engineers? Well, you can use Excel, but you have to write code to do the main work.

3, programming language

For junior data analysts, will write SQL queries, write Hadoop and Hive queries if needed, basically OK.

For senior data analysts, in addition to SQL, learning Python is necessary, used to obtain and process data are twice as effective. Of course other programming languages are also available.

For data mining engineers, Hadoop must be familiar with, Python/Java/C++ must be familiar with at least one, and Shell must be able to use? In short, programming language is definitely the most core competency of data mining engineers.

4, business understanding

Business understanding is the basis of all the work of data analysts is not too much, the data acquisition program, the selection of indicators, and even the final conclusions of the insight, are dependent on the data analysts on the business itself.

For junior data analysts, the main work is to extract data and make some simple charts, as well as a small number of insight conclusions, with a basic understanding of the business can be.

For senior data analysts, you need to have a deeper understanding of the business, and be able to extract effective insights based on the data, which can help the actual business.

For data mining engineers, a basic understanding of the business is enough, and the focus still needs to be on utilizing their technical skills.

Business ability is a must for good data analysts, if you have been very familiar with a particular industry before, and then learn data analytics, it is a very correct approach. Fresh graduates without industry experience can also be slowly developed, no need to worry.

5, logical thinking

This ability to mention less in my previous article, this time alone out to say.

For primary data analysts, logical thinking is mainly reflected in the process of data analysis of each step has a purpose, know what kind of means they need to use, to achieve what kind of goal.

For senior data analysts, logical thinking is mainly reflected in the construction of a complete and effective analytical framework, to understand the correlation between the analysis of the object, and to be clear about the causes and consequences of the change of each indicator, which will bring the impact of the business.

For data mining engineers, logical thinking is not only reflected in the analysis work related to business, but also includes algorithmic logic, program logic, etc., so the requirements for logical thinking is also the highest.

6, data visualization

Data visualization is very high, in fact, including a wide range, do a PPT on the side of the data charts can be considered as data visualization, so I think this is a universal need for the ability.

For primary data analysts, the ability to use Excel and PPT to make basic charts and reports that clearly show the data will achieve the goal.

For senior data analysts, they need to explore better data visualization methods, use more effective data visualization tools, and make data visualization content that is simple or complex according to the actual needs, but suitable for the audience to view.

For data mining engineers, it is necessary to understand some data visualization tools, and also to make some complex visualization charts according to the needs, but usually do not need to consider too much beautification.

7, coordination and communication

For junior data analysts, understanding the business, looking for data, and explaining the report, all need to deal with people from different departments, so communication skills are important.

For senior data analysts, they need to start leading projects independently or do some collaboration with the product, so in addition to communication skills, some project coordination skills are needed.

For data mining engineers, the technical aspects of communication with people are more, the business side is relatively less, and the requirements for communication and coordination are relatively low.