hwZhihaoLin的副本 2.rtf

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\outl0\strokewidth0 \strokec2 Future of Data science:\
I believe that in the future ten years, Data Science will develop it's own ability and make itself able to work more independently. I do believe.that data scientists will learn more courses in the future in order to cooperate better with their colleagues. This is urgent in order to change the only adjacent position of data scientists. The definition should be much more solid than what it is now and I have my faith on that.\
In many cases, Data scientists are not able to use only statistical methods to predict things. As an example, the rate of people who has correct knowledge about aids increase as the rate of aids increase. However, if the people who is working on it does know the relationship between them, mistakes can come up. So any people who claim themselves as a data scientist should has some knowledge about what he is working. And what's more, he should be able to cooperate with someone else. However it is the collision of ideas that make things better, so I do believe data scientists needs to learn what they are going to face before they decide which company to join.\
Secondly, I believe there would be less so called data scientists, at least less than the amount of so called data scientists nowadays. I have made this prediction because, so far, almost none project I have done can be simply done with mathematical methods. It is always urgent and essential to know something about what this model is. And sometimes this career needs more knowledge about the career itself than statistical knowledge. Therefore, I do believe that once the skills of how to deal with data frames has been widely known, people would be more likely to hire few data scientists that can provide some advice when needed rather than hire lots of data scientists and train them how to fit their position.  There shouldn't be many positions for the data scientists, but they should take a more important role in the world than what they are today.\
Another impact to this career is that too many people claim themselves to be data scientists.  Let me introduce what I believe many people think about data scientists-- people who deal with many data should be called as data scientists. Like what Many simply learned something related to data science, which simply provide them the ability to work with some data but without strong theoretical background. And this makes data scientists unable to be different from other people. So far in many companies they simply want somebody to provide presentations easy for the managers to understand but these are, in many cases not very useful. This would surely damage the prestige of this major. It is no wonder than once the method of data science proved it to be useful everyone wants to abuse it's advantages. However many people do not even know how to define data science. Therefore sometimes the companies simply hire those people without essential statistical background and think this is the way data science works. This major really needs it's own label.\
For many years people have been waiting for methods to earn quick and easy money. Now they think they've got the idea. As I have mentioned before, data science is a complicated thing, sometimes we use linear regression, sometimes even artificial intelligence can be used. I have to say that in many cases, people just use simple ways that their own experience is much more useful than the methods mentioned on book. I mean they just simply use data science as something to convince themselves that they have made a good decision. This would surely make the data scientists feel good because people would think they can not do much with improving their works. Maybe phd students can be much more useful than master students in the future while bachelors would be almost impossible to learn enough thing to provide themselves  a background strong enough to really improve something.\
So in my mind I believe this thing would not be as premature as what it is now. Data scientists should be able to know what method to use rather than simply applying methods as what they were told by their colleagues or even simply provide some easy understanding presentations. Of course it is necessary to make reports easy to understand but to make thing more efficiently should be much more important than these nonsense. After all if talking is that important, why not just learn rhetoric and philosophy.  The structure of data science should be changed, and the way data scientists work should be much more difficult to be replaced than what it is today. So that is my personal prediction.\
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