What about data science and big data technology? What are the prospects? thank you

The majors of data science and big data technology are very good, and the prospects are promising. Graduates can engage in the research, application and development of big data management in government agencies, enterprises and companies. At the same time, you can get a postgraduate degree in software engineering, computer science and technology application statistics or go abroad for further study.

Big data major is similar to computer major, and it is a practical major. Students need to independently write programs, modify debugging programs, and pay attention to every detail in order to find errors and run programs smoothly.

This major requires students' mathematical ability, so it is recommended to apply carefully for students who are not sensitive to mathematics. This major requires students' computer use ability. Students must learn this knowledge well during their study in school, and they can choose to obtain relevant computer qualification certificates to enhance their competitiveness.

Extended data

Data science is divided into three categories: data analysis, data mining and big data. Data analysis is mainly business-oriented, that is, using some data analysis and statistical tools, such as Excel, Spass, SAS, SQL, etc. , to analyze and display data to help the company's business decisions.

Data mining pays more attention to modeling ability than data analysis. Generally given some data and a problem, you can use some machine learning algorithms to build a model from it, and then use this model to predict something. Therefore, machine learning algorithm can be said to be the core of data mining.

Work closely related to big data includes big data platform development, big data application development, big data analysis, big data demonstration and big data education. Different jobs need different knowledge structures, and the working scenes they face are also very different. The development of big data platform belongs to the R&D level, which requires practitioners to have strong R&D capabilities.