big data

I. Employment rate

I believe that the purpose of learning big data is to obtain employment, so the employment rate has become the most intuitive reference standard. However, some organizations with ulterior motives will exaggerate or falsify employment information, which makes many scholars unable to judge the authenticity of their information. This has caused a crisis of confidence. Even if the information published by reliable institutions is true, I am afraid it is difficult to fully convince scholars.

How to judge whether the employment rate announced by an organization is true? We can check the entity, view and access the internal follow-up documents or follow-up information of the employment information of students in its employment department, so as to confirm that the information is absolutely true.

Second, the teaching staff.

The biggest difference between training institutions and traditional university education is employment-oriented. The study of university education is mainly oriented to taking exams and obtaining academic qualifications, and employment is not its main teaching purpose. More work ability needs to be improved in the work. The only goal of training and education is employment.

Therefore, training institutions and universities will have very different background requirements for lecturers. The lecturers of reliable training institutions are all big data developers from large Internet companies, and their practical ability is very strong. Even some lecturers hold the positions of project manager and technical director during their tenure. Starting from actual combat and taking employment as the foundation is the purpose of training institutions.

So the background of big data lecturers is very important!

Third, the curriculum.

As long as it comes to learning, it is inseparable from the curriculum. That is, the knowledge and technology we need to learn. Whether the curriculum is reasonable or not directly determines the students' knowledge structure and learning effect.

Some outsiders in the industry who have no big data background have entered the field of big data training with a "hot blood" and opened a "big data training class". I once had two students transferred to our class in a training institution, because the "big data technology" mentioned by this institution is only a database course and has nothing to do with big data.

There may not be many such institutions at present. After all, the industry of big data training has entered a stage of fierce competition. At present, from the perspective of curriculum outline, it may be that the courses taught by various institutions are similar, and there is not much difference. At this time, it is necessary to ask training institutions or have detailed course contents in official website as a reference to identify the gold content of the comparative courses.

Fourth, training programs.

Above, we talked about the importance of curriculum. Whether the curriculum is reasonable or not affects the knowledge structure and learning effect, and the project experience will directly affect our employment situation. At present, our big data industry is in a period of rapid growth, and more and more small and medium-sized enterprises are starting to set up big data. In other words, the biggest demand for big data talents is not those mature Internet or big data enterprises, but these small and medium-sized enterprises, whose talent demand is far greater than that of mature enterprises. Moreover, these SMEs do not have the time and energy to cultivate big data talents internally. They need talents with rich project experience to enter the enterprise and directly participate in the development work.

Therefore, rich project experience is an indispensable weapon for you to join the enterprise. Some unprofessional training institutions don't want to provide students with real big data projects for training, even the most basic cluster servers can't be provided. Just install a few more virtual machines on the computer to simulate the cluster environment. You can imagine how to carry out real project training in such a cluster environment, and you can only design some unrealistic "big data projects" based on the knowledge you have given. How do illusory projects, illusory data sources and illusory cluster environments enter the enterprise work?

Therefore, it is reliable that big data training institutions can provide real cluster servers for students and real big data projects for first-line Internet companies.

Training programs generally include JAVA projects, big data projects and enterprise big data platforms. Different learning stages cooperate with different projects to deepen students' understanding and application of what they have learned.

Verb (abbreviation of verb) registration threshold

If you are already investigating big data learning, I believe you have also seen the enrollment requirements of many big data training institutions, and some institutions can participate in training as long as they give money. There are no requirements for students. You don't need to consider directly passing this training institution. Because there is a certain threshold for enterprises to recruit big data developers, the minimum academic requirement is to recruit junior college (individual minority enterprises may relax their requirements). Therefore, a reliable training institution will definitely set a requirement for enrollment: college degree or above.

Sixth, course selection

More and more people want to enter big data, but they don't want to pay too much. In order to cater to everyone's needs, some training institutions have set up "weekend classes", "fast classes" and "online work" courses. I believe many students are attracted by this kind of study class, which does not delay their work and has a favorable price. Faced with such temptation, more and more people forget their original intention.

For such students, I have only one sentence to say to you: If such a class can learn big data technology, where does the gap of 2 million+big data talents in China come from?

Seven, live audition

Through the above six conditions, I believe that you have screened out your favorite institutions among many big data training institutions, so the next step is to audition. Confirm whether you are suitable for learning big data and whether you can learn big data well through audition.

Through the evaluation of the above seven conditions, I believe that you have chosen a reliable big data training institution that suits you, and the next step is persistent learning. I believe that you will become a member of China's big data talent gap in five months. I wish you success in your studies!