If I am a ship that rides the wind and waves in the ocean of data, then the clear career goal is the direction of sailing, the knowledge of statistical business thinking and other knowledge is the rigorous structure of the hull, and the use of tools such as Excel and Python is the driving force for sailing. Different from the previous two articles, today will combine the content of statistics, focusing on how to use Excel for practical operation, in the process of practical operation will be accompanied by the correction and divergence of ideas. First of all, we need to clarify the steps of data analysis. Without order, it is easy to fall into a mess in the massive data. Teach you to use Excel to do e-commerce data analysis Secondly, please let me describe how I use Excel for exploratory analysis based on the above steps? The first 4 steps of this issue are mainly (clarification of problems, understanding data, data cleaning and data analysis, the rest please pay attention to the follow-up push). Practical report for this issue: transaction details and user information table of users who purchased babies on Taobao and Tmall; Data source.
After getting the data at hand, don't rush to do cleaning and analysis, but first brainstorm based on the information you have. Through this data, I/we can roughly determine what problems are, which can be listed in the brain map (such as Xmind). After mobile number list many conjectures, they are sorted according to their importance. Why should we do it? The old saying goes: sharpening a knife is not a mistake for chopping wood, first understand the problem clearly, which is conducive to later analysis, rather than rushing to get started, spending a lot of effort, and in the end it is sad to find that the conclusion drawn is in the opposite direction of the direction to be analyzed. Based on the existing information, the following problems to be verified can be assumed.
Teach you to use Excel to do e-commerce data analysis 2. Understand the data The short video in Monkey Talk Data Analysis made a deep impression on me. She likened "understanding data" to "scallions, garlic, ginger" and other condiments prepared before cooking. For the big meal of data analysis, in the table For different fields, the meaning behind it must be clearly understood, otherwise the food will not taste right. Teach you to use Excel to do e-commerce data analysis 3. Data cleaning Remember: data cleaning should not be processed directly on the original table. You can copy the table and generate a copy to prevent the original data from being damaged and affect work efficiency. Select a subset: You can follow the 28 principle, and choose the core field in the face of many fields.