Big Data thinking

I have not learned anything this week. Cloud computing courses related to yourself are still arranged on Sunday. Let's talk about what Big Data thinking is.

In general, it is a way of thinking. It is also a logical structure to consider the problem. Simply put, it is the process of inferring the future based on known knowledge. The complexity is that, based on a large number of known conditions, we can use the corresponding false evidence to determine the answers we want to know based on these conditions.

For a simple example, I don't know whether it is big data thinking. I hope you can judge it. I went to dinner with two friends at noon. A said that he would like to meet a girl who has never seen me for nine years in the afternoon. I will ask B to prepare for it. I can try it in the afternoon, maybe it will be a good marriage. The judgment is based on: A and B are from it code farmers. A is married and has been educated. He has never seen a girl who came to him for nine years. He must have something to ask for help. For those of us of this age, the greatest need for help is the marital problem. In addition, many students are more reliable in introducing objects. While Mr. B is working in a Fortune 500 company, although he often lives on his own. But his desire for marriage is also well known among our friends. Therefore, the first assumption is that the female students not seen in June 9 must be unmarried. The second assumption is that the female of A Jun must have a close relationship with a Jun. The success rate of introducing a Jun to each other as a bridge is also high. Third, for the young man B Jun, he also needs to have the opportunity to contact the girl all day in the face of too much code and a girly working environment. Therefore, they are more likely to succeed.

I don't know what we mentioned above is beyond the scope of big data thinking. In fact, sometimes, how can we train our big data thinking. Read more books and learn about algorithms, but do not stick to algorithms. Any formula is used to improve the analysis results. As we have said sometimes, companies that have more than 1 Pb of data are still very few. Companies that actually use SAS and SPSS to analyze data are still very few. There are many other places we want to use for data analysis and data mining. In analysis tools, Excel may be a better choice for data volumes smaller than 1 GB, as I said in my previous blog, data analysis and mining should be done in the TXT document first, and then U1 should be used to see if you can find what you need, as the data volume increases, Excel is used, Oracle or access database is used, and SAS and SPSS are used. nosql is now used.

What does Big Data thinking bring to us. I think there are mainly the following points:

1. determine the accuracy of the problem. Remember that during the World Cup ***, there is a saying that everyone who understands the ball has lost. Most people who do not understand the ball and understand data analysis may not lose. As if I have read a report, everyone who understands the game is buying their favorite team to win, while those who know the data analytics are buying their favorite team to win, while buying another team to lose. It is based not on the number of matches between the two teams in history, but on Baidu and Google to see the number and odds of the two sides.

2. Have a brand new understanding of marketing activities and business models. Let's take a friend's example. He is going to translate all the examples of related technology development in the past few years and some good blogs he has seen abroad for marketing in China. The number of registered and purchased users on the day exceeds the expected value. In addition, marketing has all switched to real-time interactive applications such as Weibo. Through the large amount of data collected every day, we can easily completely subvert the effects of some original business models.

3. positioning itself. In The Big Data era, mobile Internet emerged. Specifically, the mobile Internet gave birth to the emergence of big data. Our learning speed is far behind the information update speed. We have a limited time to completely learn a technology and then go to work. I am still working to find out the practical effects of the technology.

Well, this blog will be written this week! Welcome to discuss

This article is from the "Data Mining and Visualization" blog. For more information, contact the author!

Big Data thinking