To be a good data analyst you need to have the following skills:

Mathematical knowledge

For junior data analysts, then you need to understand the fundamentals related to statistics, formula calculations, statistical modeling and so on. When you get a dataset, you need to perform descriptive statistics first to understand the quality of the dataset.

And for advanced data analysts, it is necessary to have the ability to perform statistical modeling, and linear algebra should also be understood.

Analytical tools

For analytical tools, SQL is a must, as well as to be familiar with Excel pivot tables and formulas, in addition, but also to learn a statistical analysis tools, SAS as a start is better, VBA basic essential, SPSS/SAS/R at least one of the proficiency of one of the use of other analytical tools (such as Matlab) can depend on the situation. ) can depend on the situation.

Programming languages

The two most popular languages in the field of data analytics are R and Python, which involve the invocation of various statistical functions and tools, and R undoubtedly has an advantage. However, it lacks the power to handle large data volumes and has a steeper learning curve.Python is highly applicable and can script the process of analysis. So learning Python is also quite necessary if you want to make a career in this field.

Of course, other programming languages need to be mastered as well. You need to be able to independently make data work for you, and SQL is the most basic of these. You need to be able to query data in SQL, and write programs to analyze data quickly. Of course, programming skills do not need to reach the level of software engineers. For more in-depth analysis of the problem you may also use: Exploratory analysis skills, Optimization, Simulation, Machine Learning, Data Mining, Modeling and so on.

Business Understanding

Understanding the business is the foundation of a data analyst's work, and the solutions for acquiring data, selecting metrics, and the insights for the final conclusions all rely on the data analyst's understanding of the business itself.

For the junior data analyst, the main work is to extract data and do 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 be helpful to the actual business. For data mining engineers, a basic understanding of the business can be, the focus still needs to be put on the play on their technical skills.

Logical thinking

For junior data analysts, logical thinking is mainly reflected in the data analysis process, each step has a purpose, know what kind of means they need to use, to achieve what goal. For senior data analysts, logical thinking is mainly reflected in the construction of a complete and effective analysis framework, understand the correlation between the analysis of the relationship between the object, and be clear about the causes and consequences of the changes in each indicator, which will bring the impact of the business. For data mining engineers, Luo thinking is reflected in addition to the analysis work related to business, but also includes algorithmic logic, program logic, etc., so the requirements of logical thinking is also the highest.

Data visualization

Data visualization mainly with the help of graphical means to clearly and effectively convey and communicate information. Sounds very lofty, in fact, includes a wide range, do a PPT put on the side of the data charts can also be considered data visualization.

For primary data analysts, they can use Excel and PPT to make basic charts and reports that clearly show the data, and then reach the goal. For slightly more advanced data analysts, they need to use more effective data analysis tools to make data visualization content that is simple or complex according to the actual needs, but suitable for the audience to view.

Coordinating communication

Data analysts not only need to be able to decipher data, but are also often asked to advise project managers and department heads on certain data points, so you need to have strong communication skills.

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