The era of data analysis has already arrived. From the state, government, and enterprises to individuals, big data and data analysis have become trends that everyone is familiar with. But you may not have the professional knowledge of data analysis and programming, or you have learned a lot about the theory of data analysis, but you still can't practice it. Here, I will compare the four tools that are most popular with data analysts, Excel, R, Python, and BI, as the basis for getting started with data analysis.
1. Excel
1.1 Usage Scenarios
- Data processing work under general office requirements.
- Data management and storage of small and medium-sized companies.
- Simple statistical analysis for students or teachers (such as analysis of variance, regression analysis, etc.).
- Combine Word and PowerPoint to create data analysis reports.
- Assistant tool of data analysts.
- Production of charts for some business magazines and newspapers (data visualization).
1.2 Advantages
- It's easy to get started with Excel.
- The learning resources are very rich.
- You can do a lot of things with Excel: modeling, visualization, reports, dynamic charts, etc.
- It can help you understand the meaning of many operations before further learning other tools (such as Python and R).
1.3 Disadvantages
- To fully master Excel, you need to learn VBA, so the difficulty is still very high.
- When the amount of data is large, there will be a situation of stuttering.
- The Excel data file itself can hold only 1.08 million rows without the aid of other tools, and it's not suitable for processing large-scale data sets.
- The built-in statistical analysis is too simple and has little practical value.
- Unlike Python, R, and other open source software, there is a charge for the genuine Excel.
2. R
2.1 Usage Scenarios
The functions of R cover almost any area where data is needed. As far as our general data analysis or academic data analysis work is concerned, the things that R can do mainly include the following aspects.