Shulou(Shulou.com)06/03 Report--

Many people in the martial arts require that they can use all kinds of weapons, but everyone will have a weapon that they are best at. In the past, these four kinds of software were like "knives, guns and sticks" in big data. Weapons are only part of it, and what is important is our own understanding of big data, which is equivalent to internal skill. After all, the battle between the two sides, the weapon to win the part is very big, but not the decisive factor! Just imagine, a man with deep internal skills can compete with a man who can only use a gun. maybe picking leaves can hurt the man who can only use a sword.

Okay! We will unveil these four "weapons" one by one!

Let's start with R, which is more of a software than a language. His more application is in the application of data volume in small and medium-sized companies. Personally, it will also be the next hot language in China. From big data's point of view, what kind of data is the most valuable, the first to bear the brunt is operator data, followed by bank data, Wechat data, e-commerce data. For all departments of the data, most of these data are stored in prefectures and cities. It is equivalent to dividing the data into pieces one by one, which is beneficial to the development of R. When doing data mining and visualization, my mentor said that in China, it is best to let customers see the value of your data mining within two weeks. In order to achieve such a goal, using R will have a very good effect. Especially when it comes to data presentation.

As for the learning of R, it is necessary to have certain code logic and calling specification. Because of the minority, it has to constantly connect with other languages, which is equivalent to one speaking Chinese and the other speaking foreign language, and the intermediate translation is very important.

Let's talk about Python. Some people say that this language will be used by operators sooner or later, because it has too many application scenarios in the era of big data. It is based on LINUX. This is first of all convenient for everyone to use, he can and any language can also call each other interface. This greatly facilitates the work of operation and maintenance personnel in the era of big data. This involves a question: do operators need to master one or two development languages? The operation and maintenance in the new era will be automated operation and maintenance in a large area, changing passive maintenance into active protection. In this way, in addition to installing the machine, the operation and maintenance personnel should be able to simply develop and customize the server and related network equipment.

For Python, my study plan is to start learning after the devil of R has finished training. Strive to avoid the phenomenon of learning too much but not being proficient, first learning one language, and then learning another by analogy.

For SAS, just put this aside for a while! After all, this software is charged, it has more built-in algorithms, and has a better statistical effect on some data. It is suitable for the collection and statistical use of a large amount of data in some scientific research institutions. This software, which I installed on my virtual machine before, takes a lot of memory to run. And his code, on the whole, feels similar to C. It is good for big data to use it to deal with it, but the charge for this software is higher. According to the current domestic situation, startups are not recommended to use it.

Finally, let's talk about SPSS, this IBM software. Some people say it is on a par with SAS, but this software, personally, feels it is best to use it to process EXCEL data, or to demonstrate your data mining process to leaders and customers. But this software has not been used specifically, just see the teacher chain good line, to run the data, it has higher requirements for the original data. Therefore, it is also possible to combine full R and SAS after dealing with the original data, and then using SPSS to follow the process will be better.

The above is the understanding of the four software that I know. In big data's field, these four software will be used more or less. And how to use it depends on us personally.

I have little talent and learning, if there are people in the same way, if there is any offense, I hope you will not hesitate to give me advice! Learn from each other and grow together!

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