What should a big data analyst learn?

Big data analyst refers to the process of scientific analysis, mining, display and use of big data for decision support based on various analysis methods. Big data analyst is the name of practitioners engaged in this profession. There is a Ministry of Commerce in China Level certification for big data analysts.

The role of analysts

Big data analysts can enable companies to clearly understand the current status of the company, the competitive environment, and risk assessment and decision-making support, which can make full use of the value brought by big data. After data mining and display, a clear, accurate and data-supported report will be presented to corporate decision-makers. Therefore, big data analysts are no longer simple IT staff, but core figures who can participate in corporate decision-making and development.

Compared with traditional data analysts, big data analysts must learn to break information silos and utilize various data sources, find data patterns in massive data, and discover data anomalies in massive data. Responsible for the planning, development, operation and optimization of the big data data analysis and mining platform; develop data models, data mining and processing algorithms according to project design; conduct analysis through data exploration and model output, and provide analysis results.

What should big data analysts learn?

1. Mathematical knowledge

Mathematical knowledge is the basic knowledge of data analysts. For junior data analysts, it is enough to understand some basic content related to descriptive statistics and have a certain ability to calculate formulas. Understanding common statistical model algorithms is a bonus.

For senior data analysts, knowledge related to statistical models is a necessary ability, and it is best to have a certain understanding of linear algebra (mainly knowledge related to matrix calculations).

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

So data analysis does not necessarily require very good math skills to learn. As long as it depends on which direction you want to develop, data analysis also has a "literary" side, especially girls, they can go in the direction of document writing. develop.

2. Analysis Tools

For junior data analysts, it is necessary to be able to play with Excel and be proficient in using pivot tables and formulas. VBA is a plus. In addition, you also need to learn a statistical analysis tool. SPSS is a good introduction.

For senior data analysts, the use of analysis tools is a core competency. VBA is a basic necessity. SPSS/SAS/R must be proficient in using at least one of them. Other analysis tools (such as Matlab) depend on the situation.

For data mining engineers...well, just being able to use Excel is enough. The main work needs to be solved by writing code.

3. Programming language

For junior data analysts, they can write SQL queries, and if necessary, write Hadoop and Hive queries, which is basically OK.

For senior data analysts, in addition to SQL, it is necessary to learn Python, which can be used to obtain and process data with twice the result with half the effort. Of course other programming languages ​​are also possible.

For data mining engineers, they must be familiar with Hadoop, at least one of Python/Java/C, and be able to use Shell... In short, programming languages ​​are definitely the core competency of data mining engineers.

4. Business understanding

It is not an exaggeration to say that business understanding is the foundation of all the work of data analysts. The data acquisition plan, the selection of indicators, and even the final conclusion of the insight all rely on A data analyst’s understanding of the business itself.

For junior data analysts, the main job is to extract data and make some simple charts, as well as a small amount of insights and conclusions. It is enough to have a basic understanding of the business.

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

For data mining engineers, it is enough to have a basic understanding of the business. The focus still needs to be on exerting one's technical capabilities.

Business ability is a must for a good data analyst. If you are already very familiar with a certain industry before, then learning data analysis is a very correct approach. Even if you have just graduated and have no industry experience, you can develop slowly, so there is no need to worry.

5. Logical thinking

This ability was rarely mentioned in my previous articles, so I will talk about it separately this time.

For junior data analysts, logical thinking is mainly reflected in the purpose of every step in the data analysis process, and knowing what means you need to use to achieve what goals.

For senior data analysts, logical thinking is mainly reflected in building a complete and effective analysis framework, understanding the correlation between analysis objects, and knowing the causes and consequences of each indicator change and the impact it will have on the business.

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

6. Data visualization

Data visualization sounds very high-level, but in fact it covers a wide range. Putting a data chart in a PPT can also be regarded as data visualization, so I think this is A generally required ability.

For junior data analysts, if they can use Excel and PPT to make basic charts and reports, and can clearly display data, they will achieve their goals.

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 actual needs, but suitable for the audience to watch.

For data mining engineers, it is necessary to understand some data visualization tools, and to make some complex visual charts according to needs, but usually there is no need to consider too many beautification issues.

7. Coordination and communication

For junior data analysts, understanding the business, finding data, and explaining reports all require dealing with people from different departments, so communication skills are very important.

For senior data analysts, they need to start leading projects independently or do some cooperation with products. Therefore, in addition to communication skills, they also need some project coordination skills.

For data mining engineers, there is more technical content in communicating with people, relatively less in business aspects, and the requirements for communication and coordination are also relatively low.

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