Shulou(Shulou.com)11/24 Report--

In the past, people may have the misconception that logical thinking ability only exists in science, but this is not the case. In fact, regardless of liberal arts or science, no matter what industry, what field, the ability of logical thinking runs through every moment of our work and life. For example, in the case of writing a composition, if you want to write a good article, you need strong analytical ability, inductive ability, generalization ability and overall systematic thinking.

Recently, Mr. Feng Junchen, a spark thinking data scientist, was invited to accept an exclusive interview on "Volcano engine x gets per capita teacher". In the interview, Mr. Feng, combined with the application of logical thinking, shared in detail "how to improve children's thinking ability" and the application and exploration of spark in big data.

Q1: why should we cultivate children's thinking ability?

In the process of educating their children, some parents will more or less encounter the following problems: children's words do not reach the meaning, speak illogically, resulting in incomprehension by others; poor practical ability, slow movement; being at a loss when they encounter difficulties, poor concentration, and so on. These problems are the embodiment of the lack of logical thinking ability. In fact, children's logical thinking can be improved through acquired training. The younger the child is, the greater the role of acquired education will be. A child's brain is a period of rapid growth before the age of 8, which is the golden period for the cultivation of logical thinking. Bloom, a famous educational psychologist in the United States, said that if a person's intelligence growth is 100% by the time he is 17 or 18 years old, he will have completed 80% before he is 8 years old.

Our brain is malleable, and external stimulation will stimulate neurons in the cerebral cortex to continuously establish links and promote the development and stability of the corresponding function. The essence of training logical thinking is to train our brains to make people smarter and smarter.

If there is a lack of corresponding education, the thinking ability of people themselves will not be maximized, and the differences between people will become bigger and bigger, which is bound to affect the development of the following stages.

Q2: what difficult problems did the volcanic engine help spark thinking solve?

We agree with the idea of byte jumping when it comes to data-driven, and we are practicing it by working with volcanic engines. Let me share three systems that we are using:

The first one: DataWind (Intelligent data Insight product). The biggest use for us is to improve efficiency. Before we use DataWind, all data analysis must be done by writing SQL, otherwise we can't do it at all. Since Spark thinking bought this product, analysts have not increased, but the number of monthly active users of our system has more than doubled, and our chart production has also doubled.

The second: DataFinder (growth analysis product). User behavior analysis tool. Sparks used to have a big problem with burying points. As the saying goes, "those who count don't count, and those who do numbers don't count."

What does it mean? In Internet companies, the product manager should bury the demand, and the product manager should also look at the number himself, so the number of people is counted, but the spark is not needed, because the number of people is the analyst. So the analyst doesn't really know what the product manager is doing, and the product manager doesn't know what the analyst's needs are. Then the people who do the math in the middle are R & D and testing. they just do the requirement, and they don't know where the requirement comes from or where it is going to be used.

In the past, there were more than 10,000 burial sites for sparks, but they were basically useless. After we worked with the volcano engine, the process was greatly simplified, reducing the number of burial sites to less than 1000, but now there are a lot more people using it. Because Finder allows the product manager to read the numbers by himself, there is no need to write SQL for him. Even our operation classmates can read the numbers and circle the crowd by themselves. When the boss asks why this number has changed, he can quickly see where the funnel is, and he can also quickly see what the portraits of these people look like.

The third: DataTester (A / B test product). The biggest problem faced by small and medium-sized enterprises is the lack of traffic. It may take two months to scientifically do an experiment of halving traffic to verify two pages of material. This time is unacceptable for a monthly growth team. Between science and speed, the rational business side will choose speed.

Volcano engine DataTester is the only software on the market in China that has the ability to use Dobby slot machine principle for intelligent traffic allocation, it is an online flow allocation optimization mechanism, it can scientifically weigh decision speed and decision quality, regardless of traffic size can make a relatively optimal choice, so our business can use it.

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