Nowadays large workplace situation is not very good, a lot of people think of traditional industries by learning a technique ---- such as large data or AI. In order to achieve the transition to the big data industry. Cooked I do not know, different work content and nature of the work will lead to you will still be at a disadvantage in terms of show business and familiar technology, personal development is very unfavorable.
Here's to a few key questions to ask itself and to deepen understanding, and then decide whether to large data transition.
Whether motivation (1) the transformation of this industry is to see the high salaries?
In general, people who want to switch to higher salary levels are seen in this industry, a large demand for talent. But on the other hand, the industry overall capability for talent is demanding. Not only requirement you need to know some technical details. At the same time, you also need to know some business logic.
In contrast, more than 30 years of age is not recommended transition to the industry. As the saying goes, "three years to switch to the poor" still have some truth. Moreover, the industry is an emerging industry, the market fast response. After basically have business needs that day, product manager for the development of demand after the conversion to begin implementation, so 996 is a standard routine.
So, consider paying the same time, to consider the high input, high output. Only two unite, in order to be worthy to pay the company issued.
(2) whether the transformation can be achieved by three to six months of training?
1, see the money in your pocket to see how much money, and secondly to see how much this training, put the unit price is calculated every day.
2, see payroll employment, core training is to make your job, the other is the slogan. The average salary of students for employment after the training to see the past, when new units long.
3, look at the situation of teachers, tutors see their big data project experience and projects.
(3) Big Data industry is now leaving a shortfall of people do, how about employment prospects ?
1, any industry are the 20/80 law, the industry is now very large data missing the top 20% of compound talents.
2, as a large data interbank people a curriculum, and then look back, training allows you to find a job, but better development on your own.
3, a simple criterion, look at the money! Look median wage, see the median charge of training, and then combined with time, weigh your self-discipline force, go to Application Training (or first to consult me, Micro Signal: 383 116 569).
(4) under the cover of human future of artificial intelligence, the employment trend is what?
Simple, duplication class will be largely replaced. Then some needed experience, working sense of service will rise a lot. In the future there will be a machine repair the machine is like, but how to manage, how to maintain these machines will create many jobs. As the current network of about truck drivers, takeaway little brother ...... new technology will drive new requirements, if not improve their technology, thinking, vision, it could have been eliminated by the times.
(5) Big Data jobs would be "moving bricks yard farmers" do?
Most are not, but we must learn to constantly upgrade Daguai, and add equipment at any time ......
Big Data Employment is a very broad concept, divided into technology and business direction.
(A) technical direction simply divided into early, middle and high. Mainly around the technology.
Primary is that some data collection, labeling, SQL statements, Python ..... this kind of work.
Intermediate general is will some of modular development on this solid foundation, better able to implement a feature, data completion of more advanced features.
Advanced belong to architecture or director level, not only to understand the technical details, but also from the perspective of architecture to optimize the code and implementation patterns. To be able to translate business requirements into R & D needs.
(B) the direction of the business also has a simple beginning, middle, and high level. Mainly around business transformation.
Primary main business is to understand, familiar with the product, may be related to user research, product design. But not too much depth degree.
Intermediate principal in-depth understanding of the business, to discover customer needs through a number of methods and statistical knowledge to meet the needs of the user during user.
Senior business is mainly operational relationship, bidding, solutions ability to know the boundaries of technology and business, and know the details of the implementation process of the project.
Do not just big employment data by one point, thought he saw a face, a lot of content inside. And then put a little bit of learning and the development of other capabilities after skilled master.
1，要树立正确认识，大数据与人工智能，只是一种技术手段。对金融的业务理解才是你在大学阶段所重点要了解跟认识的。
2，一直觉得大学是通识教育，对于大数据，AI这类技术，知道怎么使用，匹配金融的相关场景就行，具体的技术实现等到工作中再去实践也为时不晚，再者说，这些技术变化现在也日新月异。而大学阶段，数学基础，图论，概率论，金融衍生品，宏观，微观经济这些通识类的内容建议多读，多理解。
3，凡事树立“道”与“术”的观念，在大学最好的年纪，多在“道”上下功夫，“术”的层面，培训机构跟自学社群也会帮你完成。
（7）高校里大数据专业未来的从业方向？是否应该先工作还是先读研？
建议先找工作，然后一边工作一边读在职研究生，研究方向为工作中遇到的算法相关的问题或AI相关的问题。理由有三点：
1，经济方面：俗一点，就是钱。毕竟本科出来找一个8K～1W左右的大数据标注工程师应该是没问题的。
2，视野：只有在工作中，你才会知道大学里学的知识什么是无用的，什么是有用的。当只有工作经验之后，再通过研究生的理论知识来深度理解你的工作内容，会对你以后的职场发展大有帮助。
3，职业发展：现在大数据发展跟业务贴合越来越紧密，所以对人的综合能力要求也越来越高，只有代码的大数据人才势必会被新生力量所取代，多优化自己的思考模式，多贴近业务，多帮助他人，多看书，多学习！
以上，就是自己之前知乎上的一些回答，挑出一些比较好的总结成今天的这一篇问答贴。希望能帮到想要转型的你，少走些弯路。首先把“道”选对，然后再去精进自己对于“术”的理解与认知。最后无论你是否要转型，都希望听到你的好消息！
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