What to learn as a python data analyst

Data analyst is a type of data engineer Datician ['detɪʃən], which refers to professionals in different industries who specialize in collecting, sorting, and analyzing industry data, and making industry research, evaluation, and predictions based on data.

1. Mathematical knowledge (Recommended learning: Python video tutorial)

Mathematical knowledge is data analysis Basic knowledge of teachers. 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. It just 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, if they can write SQL queries, and if necessary, write Hadoop and Hive queries, they are 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 basis of all the work of a data analyst, including the data acquisition plan, the selection of indicators, and even the final conclusion. Insights rely on data analysts’ 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 abilities.

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.

4. 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.

5. Data visualization

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

6. 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. 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.

7. Quick learning

No matter which direction you are doing data analysis, whether junior or advanced, you need to have the ability to learn quickly, learn business logic, learn industry knowledge, Learn technical tools, learn analysis frameworks... There is endless content to learn in the field of data analysis, and everyone needs to have a heart to learn at all times.

Learning quickly is very important. Only by entering this industry quickly can we seize the opportunity and gain more experience and opportunities. If you have absolutely zero foundation and want to enter the data analysis industry as soon as possible, choosing a professional big data training institution is a good choice. Shorten the learning cycle and improve learning efficiency. Time is money!

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