Is it difficult to study big data?

It can be seen that the major of big data needs to cover many fields such as computer science, statistics and data analysis. Therefore, it is not easy to learn, which requires students to have strong mathematical, computer and logical thinking skills.

In addition, with the continuous development of big data field, new technologies and methods are constantly emerging, and students need to constantly update their knowledge and skills to keep up with the development of the industry.

Generally speaking, the big data major requires students to pay a lot of effort and time, but it is a major full of challenges and opportunities for students who like data and analysis.

The curriculum of big data specialty covers many aspects, such as data structure and algorithm, database principle and application, big data technology and application, data mining and machine learning, data visualization and analysis. Although learning is difficult, it is a major full of challenges and opportunities for students who are interested and enthusiastic. In the future, with the continuous expansion of the application field of big data, the employment prospects of big data graduates will be broader and broader.

What courses are there for big data majors?

Basic courses of big data specialty: computer introduction and programming, circuit and electronic foundation, discrete mathematics, digital logic and mathematical system.

Basic courses of big data specialty: data structure, computer system foundation, compilation principle and technology, computer composition principle, computer system structure, computer network, database system principle, software engineering, data warehouse and data mining, machine learning, big data foundation, introduction to data science.

Big Data Professional Courses:

Data acquisition and management module: multimodal information processing, information and knowledge acquisition, stream data analysis technology, Linux development environment and application.

Data analysis and calculation module: Python programming language and R language, algorithm design and analysis, parallel computing and GPU course, distributed computing and cloud computing.

Data service and application module: introduction to service science and service engineering, data-driven management and decision-making, data visualization, Web development technology.