Figure 1: data analysis concept map
First of all, the common analysis skills:
Data analysis and data processing itself is a very large field, and there are many analysis methods and techniques. The following are some commonly used data analysis techniques.
Fundamentals of mathematical statistics:
Before data analysis, it is necessary to master some methods and principles of mathematical statistics. For example, understand the types of data and the characteristics of various data types. In the process of data processing, the methods applicable to impassable data types are also different. Because the purpose of our data analysis is to draw some conclusions by analyzing the data, we also need to understand the meaning behind some statistical characteristics of the data. For example, when using IBM SPSS to statistically analyze data, we need to know the meaning of significance indicators in the analysis results.
Database principle and related knowledge:
In data analysis, in most cases, there will be a lot of data to be processed. Especially today when big data technology is widely used, the use of database is an indispensable part in the process of data analysis. Many data analysts do the work from sorting out the original data, to all kinds of decomposition and summary, and then through analysis, and finally form a complete analysis report. Even though many times the original data don't need to be sorted by yourself, having knowledge of database principles and related knowledge and a certain coding ability will definitely add icing on the cake to your work.
Skillful use of Excel, IBM SPSS statistics and other data analysis software;
In data analysis, we often need the help of computer software. After all, the data processing ability of the human brain is far less than that of computer programs. Excel is Microsoft office software.
One of the components of Office. As one of the most common office software, the function of Excel we use is just the tip of the iceberg. Excel has many advanced application scenarios besides making ordinary spreadsheets. For example, the data perspective function can transform a set of data into many tables that meet different analysis requirements through a data perspective performance, so that the data can be analyzed from multiple angles. But when we need professional statistical analysis, we need to use more professional software to assist, such as IBM.
SPSS statistics.
Second, the commonly used analysis software
In addition to the common analysis skills mentioned above, a useful and powerful analysis software can also make data analysts get twice the result with half the effort. Although Excel is very common and powerful, when we want to make statistical analysis of a large number of data, Excel will be unable to do so. At this time, we need to use a more professional statistical software-IBM.
SPSS statistics? . The position of SPSS in the field of data analysis is self-evident.
Figure 2: SPSS data interface with SPSS statistics, you can:
Analyze and better understand your data and solve complex business and research problems through a user-friendly interface.
With the help of advanced statistical programs, we can quickly understand large and complex data sets and help ensure high-precision and high-quality decision-making.
Using extensions, Python? What about r? Programming language code, integrated with open source software.
Choose and manage your software more easily through flexible deployment options.
summary
Mastering data analysis skills and skillfully using data analysis software are the two core competitiveness of data analysts. If you want to be an excellent data analyst, you can start by mastering commonly used data analysis methods and SPSS software.