Three main employment directions of big data: big data system R&D talents, big data application development talents and big data analysis talents. In these three directions, their basic positions are generally big data system R&D engineers, big data application development engineers and data analysts.
Introduction to Big Data Specialty
Computer science and technology (data science and big data technology direction) mainly trains compound senior technical talents in the field of big data science and engineering. Graduates have the basic knowledge and skills of information science, management science and data science, master the basic theories and knowledge of computer, network, data coding, data processing and other related disciplines required by big data science and technology, master the technologies of big data collection, storage, processing and analysis, transmission and application, have the ability of system integration of big data engineering projects, the ability of application software design and development, and have certain research ability of big data science and the basic ability and quality of data scientists. After graduation, you can engage in the analysis, processing, service, development and utilization of big data in various industries, the integration, management and maintenance of big data systems, and you can also engage in research, consultation, education and training of big data.
Big data refers to a collection of data whose contents cannot be captured, managed and processed by conventional software tools in a certain period of time. Big data has five characteristics, namely volume, speed, diversity, low value density and authenticity. It has no statistical sampling method, but only observes and tracks what happens.
The use of big data tends to be predictive analysis, user behavior analysis or other advanced data analysis methods.
Gartner, a research institute of "big data", gives such a definition. "Big data" is an information asset, which needs a new processing mode to have stronger decision-making, insight and process optimization capabilities to adapt to mass, high growth rate and diversification. [ 1]
The definition given by McKinsey Global Institute is that the scale of data sets far exceeds the capabilities of traditional database software tools in acquisition, storage, management and analysis, with four characteristics: massive data scale, rapid data flow, diverse data types and low value density. [2]
The strategic significance of big data technology lies not in mastering huge data information, but in specialized processing of these meaningful data. In other words, if big data is compared to an industry, then the key to the profitability of this industry lies in improving the "processing ability" of data and realizing the "value-added" of data through "processing". [3]
Technically, the relationship between big data and cloud computing is as inseparable as the front and back of a coin. Big data cannot be processed by a single computer, and it must adopt a distributed architecture. It is characterized by distributed data mining of massive data. But it must rely on the distributed processing of cloud computing, distributed database, cloud storage and virtualization technology. [4]