There are so many data analysis tools, which one should be used for analysis?
This is a problem that many people often encounter when doing data analysis. Especially when newcomers are just getting started, they see the following data analysis tools
Excel、sql、spss、python、tableau、powerbi、finebi、R、Hadoop、spark……
Maybe the head is big, thinking that all of these have to be learned.
In the choice of data analysis tools, my suggestion is to prescribe the right remedy and adapt measures to local conditions.
According to different aspects of data application
In fact, the picture above is very clear. Each data analysis tool has its focus on function points. In different data application scenarios, choosing the right tool can maximize efficiency.
For example, in data acquisition, you can extract data from the database through SQL, or crawl data through python, but you must at least be proficient in SQL;
In data processing, if the amount of data is not large, Excel is actually the most convenient and quick data processing tool. In the case of large amounts of data, the use of SQL and python is more common; in batch processing jobs with a large amount of immutable data, hive Is most suitable;
In terms of analysis and modeling, simple analysis, Excel and BI tools are sufficient. When it comes to modeling, professional statistical analysis software such as R and SPSS can give full play to their advantages. After all, modeling thinking and statistics are strong. Related, of course, python is also a good choice;
When doing visualization, it is recommended that you use professional visualization tools to do it. There are also many subdivisions of visualization tools. Ordinary business data analysts and business personnel can use common BI tools such as tableau, PowerBI, FineBI, etc. Report engineers are more suitable to choose professional reporting tools such as finereport, and developers can also choose some open source visualization tools such as echart
Analysis of 3 common tools
1、Excel
In the eyes of many people, Excel is just an office software, but in fact, most people's knowledge of Excel is less than 50%. Excel can be regarded as one of the most versatile data analysis tools, including table making, pivot tables, VBA and other functions to ensure that people can analyze according to their needs.
Of course, some people think that they are very proficient in computer programming languages and disdain to use tools like Excel, because Excel can't handle big data. But think about it in another way. Does the data we use in our daily lives exceed the limit of big data? Moreover, our country's small, medium and micro enterprises account for the majority, and Excel is enough to handle most of the data analysis needs.
To sum up, Excel is a good choice for data processing and analysis below a million level. If newcomers want to learn data analysis, Excel is definitely the first choice, and it is a must!
2、Python
It is undeniable that python is indeed a powerful language tool in the field of data analysis. You can write code as you want to perform what you want. Although the difficulty of getting started is higher than Excel and BI, as a necessary tool for data scientists, it is definitely higher than Excel and BI tools from a professional perspective.
Especially in statistical analysis and predictive analysis, programming languages such as Python have unparalleled advantages over other tools.
Newcomers may not learn Python at the beginning, and many junior positions do not even need Python, but if you want to develop on this road for a long time, it must be an inescapable tool.
3, BI tool
BI is business intelligence, and this type of tool is born for data analysis. You will find that the product design of BI tools is almost designed in accordance with the process of data analysis. It starts with data processing, sorting and cleaning, then data modeling, and finally data visualization. The whole process focuses on the idea of data guiding operation and decision-making. Due to the focus on functions, the product is very simple to operate, and most of the requirements can be completed by dragging and dropping, and business personnel without programming foundation can quickly get started.
Take FineBI as an example
Data can be obtained by importing data sets or connecting to the database
If you need to process data, click to create a self-service data set. In the self-service data set, you can perform data processing operations such as filtering, grouping and summarizing, adding data columns, and merging multiple tables according to business logic, so as to realize data processing, analysis, and modeling clustering. And other functions, you can use the mouse to complete data processing operations, without rote memorizing function formulas.
After the data is processed, click save and enter the dashboard creation component to perform data visualization analysis. As a key function of BI tools, visualization is a key function of BI tools. There are dozens of visualization chart models built in, which is very comprehensive and convenient.
Finally, you can output a visual dashboard similar to the figure below, which is suitable for sharing analysis results and reporting.
The above comparison illustrates the differences between several software. What I want to conclude is that existence is reasonable. Excel\BI\ programming language, these tools have overlaps and complements in application. For overlapping areas, no matter what kind of tool, as long as you can use it to solve the problems you encounter, it is the best.
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