CBDA data analyst big data is willing to learn

One. Data analysts learn in both face-to-face and remote ways.

Face-to-face. The project data analyst training course involves economics, marketing, financial management, econometrics, science and finance, etc., and requires students to have comprehensive theoretical basic knowledge. We have conducted an in-depth analysis of the knowledge points to be used in the project analysis in each discipline, and explained them in detail in the lecture notes, so that students can quickly grasp the knowledge and apply it in relatively accurate fields.

It enables students to turn what they learn in the textbook into an effective tool that they can use.

Distance learning. The time is one year, and advanced synchronous teaching methods are adopted to ensure the quality of learning, and the specific characteristics are as follows:

a. During the face-to-face teaching period (8 days of face-to-face teaching), the course will be updated five times, and through the weekly update course, students can not only preview the basic knowledge in advance before the face-to-face teaching, but also better grasp the knowledge through comprehensive projects such as submitting homework, self-testing of knowledge points, exam review, exercise problem solving, Q&A, case participation, etc.

b. After the end of face-to-face teaching, students still have 11 months of distance learning time, and the courseware will be updated once a month, so that students can not only smoothly adapt to the certification exam of project data analysts, but also master the expanded knowledge and skills of various data analysis, laying a solid foundation for analysts to be competent in professional analysis work in the future.

d. The distance learning center provides students with functions such as learning plan formulation, class communication, and continuing education to help students learn consciously and achieve better learning results.

Two. There are four books in the data analysis course: Fundamentals of Data Analysis, Quantitative Management, Quantitative Investment, and Strategic Management.

3. Data analysts have authorized management centers all over the country, such as Beijing, Shanghai, Guangdong, etc., depending on what you are in.

In the "New Generation of Artificial Intelligence Development Plan", a safe and convenient intelligent society is proposed, which includes the development of convenient and efficient intelligent services in the field of artificial intelligence and education, and the use of intelligent technology to accelerate the reform of talent training models and teaching methods, and build a new education system that includes intelligent learning and interactive learning. Carry out the construction of intelligent campuses, and promote the application of artificial intelligence in the whole process of teaching, management, and resource construction. Develop a three-dimensional comprehensive teaching field and a first-class learning and education platform based on big data intelligence.

Develop intelligent education assistants and establish an intelligent, fast and comprehensive education analysis system. Establish a learner-centered educational environment, provide accurate education services, and realize the customization of daily education and lifelong education.

At the policy level, it provides an environment for the development of intelligent education. The era of widespread application of big data in education is coming. There are many applications of educational data mining, including:

Whether the student will drop out of school or will successfully complete their studies; Automatically detect students' learning engagement, emotion, and learning strategies to better achieve personalization; better reporting for teachers and other stakeholders; Fundamental research and discovery in educational science.

Artificial intelligence in education based on artificial intelligence and machine learning can discover behavioral patterns in data, helping teachers more easily gather actionable insights from student performance and make informed and effective decisions to help students and steer them in a better direction. In addition, by collecting data, machine learning algorithms can identify areas where a student has a large number of problems, and then help them fill those gaps with customized materials, exercises, and lessons.

We are in an era of rapid development of big data and artificial intelligence, and the way to open up education has changed. Whether you want to or not, whether you are used to it or not, you will eventually embrace the belief in science and embrace a better future of education!

It should be possible to apply to the survey of the level of teaching in schools or the level of education of citizens.

I believe that students who develop in the IT field are familiar with big data. It has always been in the leading position in the ranking of big data programming languages, which can directly reflect the importance of big data. Therefore, many students are ready to participate in the systematic learning of big data training institutions.

So, which big data training institution is better? Let's take a look.

With the popularization of big data, more and more people understand big data, and companies will also put forward higher requirements for job seekers, they want to recruit some people who can start working immediately, so they often recruit some people with project development experience. That's why so many computer science majors can't find jobs, so more and more college students will choose to take some professional big data training courses before and after graduation to increase their practical experience. Only by strengthening oneself can one be invincible.

1.Look at the content of the teaching course

The most important thing to learn big data technology is to keep pace with the times, and the technical points mastered can meet the employment needs of today's enterprises. If you want to know whether the courses offered by a training institution are new, you can also go to the official website of the institution to understand the course outline of the subject you want to study. Let's see how the learning roadmap is arranged, whether there is a system built from zero to one, whether there is a proportion of strengthening practical training and practical operation, and there are as many projects as possible.

Because enterprises have high requirements for the technical ability and hands-on ability of big data practitioners.

2.Look at the faculty

Because the technical knowledge of big data development is highly professional, it is easy to fall into misunderstandings if you blindly learn it. On the contrary, having a lecturer to lead and standing on the shoulders of giants often results in twice the result with half the effort. After all, in this day and age, you only need to communicate with others more to get more valuable information, and beginners must not work behind closed doors.

3.Look at word of mouth

The reputation in the industry is better, and students are more recognized by the training institutions, and this kind of institutions focus on the students, which is the proper attitude of education.

4.Look at the employment situation

Training institutions that aim to find students employed are now the most important. It is important to know that employment is also the embodiment of teaching achievements, and good employment cannot be achieved without a good teaching guarantee.

5.Door-to-door free trial

The purpose of the audition is to better feel the course content, lecture style, class atmosphere, etc. of the training institution, and at the same time, to communicate with the students in the class, to further understand whether the training institution meets their needs in all aspects.

1. Teaching course content.

The most important thing to learn big data technology is to keep up with the pace of the times, and the technical points can meet the needs of current enterprises.

2. Faculty.

Because the technical knowledge of big data development is very professional, it is easy to fall into misunderstandings if you blindly learn it. On the contrary, if there is a lecturer by your side to guide and lead, the learning efficiency can achieve twice the result with half the effort.

3. The reputation in the industry is better, and the students are more recognized by the training institutions.

4.Door-to-door visits.

The purpose of the on-site inspection is to better experience the course content, lecture style, and class atmosphere of big data training institutions.

According to the above 4 aspects, you can basically understand the situation of big data training institutions, you also have to go to the field to visit, listen for a few days, I hope you find a good big data training institutions.

After all, everyone has a different way of receiving information, and no matter how good the teacher's skills are, if they can't effectively convey it to students, it is useless for students, so it is recommended that the most important thing is to find a suitable one for themselves after auditioning.

When choosing a training institution, you can look at many aspects, 1 Look at the brand, the big one is more reliable, 2 Look at the word of mouth, it is trustworthy to have a good evaluation, 3 Look at the teaching, the lecturer is very important, look at the employment service, 4 Have a perfect employment security system is conducive to solving the employment problem.

The biggest difference between training institutions and traditional education is that training institutions are employment-oriented, while traditional education is more oriented to exam-taking or academic qualifications. Therefore, it is very important whether the lecturer of the big data training institution has many years of work experience in big data development in large Internet enterprises, which will determine the pass rate of the interview after graduation and whether the trainees can be qualified for the job after entry.

The data shows that in the more than 20 years of computer training, there are early Peking University Jade Bird and Dane, as well as multi-professional chain Qianfeng, Huizhong, Chuanzhi, and more professional salary and power nodes that focus on their own fields. To be able to continue to develop in the red sea of big waves is based on a few points:1

Excellent teaching products; 2.The faculty is strong enough; 3.The concept of running a school should be correct; 4.

employment exports should be stable; 5.Educating people should have a good reputation. Such talents can be cultivated and sustainable development.

Choosing a good IT school should look at the faculty of the school, after all, IT major is a subject with high knowledge requirements.

The level of the training instructor will have a direct impact on the training, therefore, when participating in the IT training course, the teacher of the training institution should focus on whether the teacher has rich practical skills, but also to see whether he has rich experience and level of lecturing, some teachers are full of economy, but the level of teaching is not high, this kind of training will waste everyone's time and money.

When choosing a training provider, you should consider the following points.

1. Teaching quality.

2. Teaching facilities.

3. Teaching environment.

4. Teaching services.

Among them, good institutions include Qianfeng, Dane, and Excellent Employment.

Qianfeng's teaching is a little better than Dane, but the comprehensive strength is more general, Dane mainly relies on advertising, and his teaching strength is not flattering.

Excellent employment, teaching quality, teaching environment, teaching facilities, and teaching services are higher than the above two, so I recommend Zhonggong Excellent Employment.

also because I studied at Zhonggong Excellent Employment at that time; Therefore, you can have a deeper understanding of excellent employment, and you can go and see how they compare with each other.

I've been looking for an institution recently, and I want to change careers to study, and I've read a lot from the Internet, brushing Zhihu, and shopping for stickers, but I feel that the more I watch, the more I feel unsuccessful. I don't know how big data analysis is, it's not a good job, I have friends who are also trained, but I studied product manager, mixed well, and now I earn a lot, she has been recommending me to study product manager, but I'm not very interested in that, I still want to learn data analysis, she is a product manager from AAA education, and also recommended me to go there to learn, I'm going to go there next month to see, to understand, are there any seniors who have learned big data from AAA education? How about it?

I don't know which ones, but I can suggest you:

First, look at the nature of the training institution.

Second, look at which major you want to study.

Third, look at the size of the training institution.

Fourth, look at its scale.

Fifth, look at her faculty.

Sixth, to see if it is national in nature.

As far as I know, you can search for Xinhua, and I feel that this is a little more reliable in recent years

Big data is divided into two directions: development and analysis, and the level of development needs is deeper, and there is no bachelor's degree or above in science to be able to learn and understand, and the time cycle required is relatively long. However, big data analysis is relatively easy to learn, but if you have a bachelor's degree, it is relatively easy to learn, and the employment direction can be those companies that use large data, such as Meituan, Eleme, DingTalk, Didi, etc. I know that the training institutions have CDA and AAA education, and I personally recommend AAA education, where the data analysis teaching ability is stronger.

You can go to Xinhua to take a look at the teachers in it, the teaching quality is very good, and the teachers are very good to the students.

With conventional software alone, there is no way to capture, count, manage, and analyze this massive amount of data in a short period of time. Due to the five characteristics of this kind of data, such as large volume, high speed, diversity, low value density, and authenticity, new processing modes are needed to make it have stronger decision-making power and develop into powerful information assets.

Compared with the big data of the behind-the-scenes heroes hidden behind the "artificial intelligence" at the two sessions in 2017, the big data that appeared from time to time in the 2018 ** work report was a wave of existence and was given infinite expectations. The report also pointed out that the implementation of big data development actions, strengthen the research and development and application of a new generation of artificial intelligence, promote "Internet +" in the field of education, and expand intelligent life.

With the application of new technologies such as big data, cloud computing, and artificial intelligence, the education industry has ushered in unprecedented challenges and opportunities. The traditional education industry is gradually moving towards informatization, and various teaching applications have come into being. However, there are still many problems in how to extract effective information from the big data generated by various applications and turn it into data support for decision-making and action.

The beginning of big data applications in the education industry.

With the development and popularization of social informatization, major universities, vocational schools, most primary and secondary schools, kindergartens, and municipal education bureaus have realized the informatization of education courses and housekeeping. Due to the inconsistency of the informatization process of various educational institutions, the application system is not unified and the consistency is considered from the top-level design, so there are obvious data barriers between applications, between schools, and between localities. The resulting data island phenomenon has undoubtedly created a considerable obstacle to the promotion of intelligent analysis of big data.

In view of these data silos, few enterprises have integrated and designed them, and there is a lack of big data application platforms in the education industry.

Driving technological change in education.

Big data intelligent analysis can play a significant role in the education industry, especially in intelligent risk control and early warning, student growth trajectory tracking, etc.

By building different data models, the platform classifies and sorts the massive information of a large number of users, and abstracts different user images, which can not only push the most suitable high-quality teaching resources for individuals, but also optimize and organize the teaching resources to promote more humane and high-quality teaching resources. For user images, early warning lines can also be set up to make specific observations on specific students, give real-time tutoring, reduce the occurrence of problem students, and promote the healthy development of students in the process of growth.