Big Data Collection & Processing Training Lab
Introduction
The Lab mainly serves the Big Data Technology major, covering over 20 knowledge and skill points related to big data collection and processing. It trains big data collection and processing engineers through 2 core courses with over 70 5-star projects and 4 2-star projects, progressively. The lab supports the full process management of both online and offline experiments, enabling institutions to conduct diverse mixed-mode course project experiments and comprehensive project training. It also provides complete preservation of the experiment process results and data traceability. It provides data and practical skill points, students, and target job positions with self-adaptive matching during the experiment process; it provides multi-dimensional, fine-grained statistical analysis and visualization, supporting student ability modeling; it achieves process monitoring, talent evaluation, and job matching, and can provide visualized results and traceability for the summary and reporting of practical teaching outcomes.
Enterprise Position: Big Data Collection and Processing Engineer
Applicable Major: Higher Vocational Big Data Technology and Other Related Majors
Feature
Industry-oriented and covering cutting-edge technologies in the industry
Covers offline data collection and real-time data collection scenarios, using enterprise mainstream technologies, including Flume and Kafka, Sqoop, Flink CDC, Python, involving large-scale data collection, real-time processing, and offline analysis, and using appropriate technologies and tools for data processing and storage.
Industry-level case features, teaching-based disassembly
Based on the TOPCARES educational methodology of Neusoft, the industrial-level project is disassembled into a progressive project system, from simple to difficult, with 70+ 1-5 level projects to help students gradually improve their practical skills.
The Lab mainly serves the Big Data Technology major, covering over 20 knowledge and skill points related to big data collection and processing. It trains big data collection and processing engineers through 2 core courses with over 70 5-star projects and 4 2-star projects, progressively. The lab supports the full process management of both online and offline experiments, enabling institutions to conduct diverse mixed-mode course project experiments and comprehensive project training. It also provides complete preservation of the experiment process results and data traceability. It provides data and practical skill points, students, and target job positions with self-adaptive matching during the experiment process; it provides multi-dimensional, fine-grained statistical analysis and visualization, supporting student ability modeling; it achieves process monitoring, talent evaluation, and job matching, and can provide visualized results and traceability for the summary and reporting of practical teaching outcomes.
Enterprise Position: Big Data Collection and Processing Engineer
Applicable Major: Higher Vocational Big Data Technology and Other Related Majors
Course Products: Data Collection Technology, Data Processing Technology
Project Products: Big Data Collection and Processing Practice Projects (E-commerce Big Data Real-time and Offline Processing System), Transportation Big Data Collection and Processing Practice Projects (Transportation Travel Big Data Real-time and Offline Processing System), Financial Big Data Collection and Processing Practice Projects (Financial Big Data Real-time and Offline Processing System), Telecommunications Big Data Collection and Processing Practice Projects (Telecommunications Big Data Real-time and Offline Processing System)
Applicable Scenarios: Professional teaching, comprehensive training, competition training.Feature
Industry-oriented and covering cutting-edge technologies in the industry
Covers offline data collection and real-time data collection scenarios, using enterprise mainstream technologies, including Flume and Kafka, Sqoop, Flink CDC, Python, involving large-scale data collection, real-time processing, and offline analysis, and using appropriate technologies and tools for data processing and storage.
Industry-level case features, teaching-based disassembly
Based on the TOPCARES educational methodology of Neusoft, the industrial-level project is disassembled into a progressive project system, from simple to difficult, with 70+ 1-5 level projects to help students gradually improve their practical skills.
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