From the theoretical perspective, data analysis can be divided into statistics-based and data mining-based data analysis methods. Obviously, statistics-based data analysis is easier to understand, data mining requires a higher level of advanced mathematics. I believe that students who have graduated for more than a decade may have forgotten a ** x ** 2 + B * x + c = 0, even I'm not sure if I can teach a child mathematics when he goes to junior high school.
From the starting point of analysis, data analysis can be divided into business-driven and pure mathematical-Driven Data Analysis. business-driven data is based on understanding of the business and has some experience, in most cases, it is to verify your own ideas, or it is more convenient to identify business problems and performance. Based on pure mathematical driving, it is basically to find the law between numbers through some mining algorithms, it is theoretically feasible to translate this rule into a business, but there are few success stories in actual work. In most cases, the two are combined to define the input and output of data by shoot the head, and the mining algorithm is only to prove how clever your decision is.
From the analysis output, data analysis can be presented in charts, text, tables, business derivation processes, or a series of advanced mathematical formulas. It is obvious that the charts are the most impressive, the second is the form, the worst text effect, and the last is the process or formula that no one can understand. The advantage of Excel is that tables and charts.
From the perspective of analysis tools, including entry-level excel, recent red-hot r language, professional statistical software tools, and the most powerful SAS, it is clear that most people only use Excel, however, Excel also has a lot of built-in data analysis libraries, and the strength of the Excel app store after the cloudification cannot be underestimated, in the future, it is hard to say whether it is able to compete with professional statistical software data mining tools, and a huge advantage of Excel lies in its large customer base.
From the perspective of charts, Excel is inferior to those professional Bi tools. From the statistical point of view, Excel is inferior to those professional statistical software. However, the functions provided by Excel are sufficient for non-professionals, perform multidimensional analysis through the pivot table, find some dazzling charts through the App Store, and perform some simple data analysis through the plug-in, through VBA and tireless debugging, you can also make some "professional" charts, which is also our purpose.
The chart types provided by Excel include bar chart, line chart, pie chart, bar chart, Area Chart, XY (scatter chart), stock price chart, Surface Chart, radar chart, and combination, in fact, column chart, line chart, pie chart, bar chart, and composite chart are the most commonly used area chart, radar chart, and XY (scatter chart ); the stock price chart and Curved Surface Chart are basically not used.
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We have already said enough about the choice of chart types on the Internet. Here we mention the suspicion of word sharing, but we should emphasize that, the wise choice of chart types is closely related to your understanding of business data and your analysis ideas. If you select an inappropriate chart, it means that you have not prepared for data analysis.
The basic types of the two charts and the selected Chinese and English versions are attached.
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Excel learning notes 1 Excel chart and Data Analysis