1. has basic statistical concepts.
Let's start with the most basic concepts: average, median, percentile, mode, deviation, variance and standard deviation. I won't go into details here, but simply talk about the difference between the mean and the median. Average value: that is, average value. The advantage is that the average is related to all data, but the disadvantage is that it is easily influenced by extreme values.
For example, you and your three friends and Bill Gates form a team, and then the average value of this team is $20 billion. Do you think you have money? Median: only related to the middle data. The advantage is that it is not affected by extreme values, and the disadvantage is insufficient sensitivity.
2. Avoid data logic errors. Common data logic fallacies 1: Correlation is causality.
"Some research results show that people with high face value earn more." After hearing this conclusion, do you think you should go for plastic surgery? But it may be because people with high face value are relatively confident, and confident people are easy to succeed in the workplace, so their income is high. It is also possible that people with high incomes have the ability to dress themselves up, so they look more attractive. Therefore, the above statement is only about the relationship between face value and income, and does not say that the two are causal.
Second, data communication and expression: how to tell stories with data
If you have enough data literacy and know how to present data and express data at the same time, then you can incorporate enough convincing data into the story, and the story will naturally become very convincing.
1. Understand the purpose and object of communication.
If you persuaded a customer to buy your wealth management products, what would you tell him?
The first type: the wealth management product has a probability loss of 10%;
The second type: this wealth management product has a 90% probability of earning.
The latter, of course. He is willing to buy it, but if it is the former statement, he may be scared. Therefore, when you communicate with different people in the company, you should also present different data.
For example, the top management may be concerned about the company's overall revenue, profit and other related data, the middle management may be concerned about the KPI data of their own departments, and the supervisor is more concerned about the success or failure of an activity or an initiative.
2. Select the appropriate data expression type.
How to use a more suitable data chart type? Here are some dry goods to share with you. The scope of application of common forms is as follows:
O scatter plot (for correlation)
O line chart (for trends)
O Horizontal and vertical bar charts (for comparison)
O waterfall map (suitable for evolution)
O heat map (suitable for focusing)
O radar chart (for multiple indicators)
O cloud map (suitable for distribution) and so on.
3. Follow the principle of data visualization
Visualization of data is also very important, because without visualization, it is just a string of numbers, which is no different from text information.
Several principles of data visualization: don't read too high a threshold, don't use too many colors, highlight key information, and the text echoes the data.