Financial Big Data Training Lab
Introduction
The Lab is for data science and big data technology majors and big data-related majors, based on the latest technology and mainstream tools of big data application in the financial industry, and focuses on the typical work tasks and professional technical ability requirements of big data technical talents in the financial industry. It provides practical project resources and supporting practical teaching platform aiming at cultivating the core competence of big data collection and processing, big data analysis and visualization, big data implementation and operation and maintenance. Through the project practice, Techniques and tools that students will become proficient with include JDK, Hudi, Flink, FlinkCDC, Kafka, Hive, Spark, Hadoop, Mysql, SpringBoot, Vue, Miniconda, Python, TensorFlow, Nginx, FineBI, etc., Help to improve students' practical problem solving ability and innovation ability cultivation.
Enterprise Positions: big data development engineer, big data acquisition and processing engineer, big data analysis and visualization engineer, big data implementation and operation and maintenance engineer
Applicable Majors: university data science and big data technology
Project Products: financial big data collection and processing practice project (financial big data real-time and offline processing system), financial big data analysis and visualization practice project (financial credit analysis and data visualization system), big data full stack technology comprehensive practice project (financial big data statistical analysis platform) 3 comprehensive projects
Application Scenarios: professional teaching, comprehensive practical training, competition training.
Feature
In line with the industry covers the cutting-edge technology of enterprises
Based on Neusoft's characteristic TOPCARES education methodology, industry-level projects are disintegrated into an advanced project system, from simple to deep, from easy to difficult, helping students to gradually exercise and improve their practical ability. There are 2 post level and 1 post group level projects provided for advanced training of different post abilities.
The Lab is for data science and big data technology majors and big data-related majors, based on the latest technology and mainstream tools of big data application in the financial industry, and focuses on the typical work tasks and professional technical ability requirements of big data technical talents in the financial industry. It provides practical project resources and supporting practical teaching platform aiming at cultivating the core competence of big data collection and processing, big data analysis and visualization, big data implementation and operation and maintenance. Through the project practice, Techniques and tools that students will become proficient with include JDK, Hudi, Flink, FlinkCDC, Kafka, Hive, Spark, Hadoop, Mysql, SpringBoot, Vue, Miniconda, Python, TensorFlow, Nginx, FineBI, etc., Help to improve students' practical problem solving ability and innovation ability cultivation.
Enterprise Positions: big data development engineer, big data acquisition and processing engineer, big data analysis and visualization engineer, big data implementation and operation and maintenance engineer
Applicable Majors: university data science and big data technology
Project Products: financial big data collection and processing practice project (financial big data real-time and offline processing system), financial big data analysis and visualization practice project (financial credit analysis and data visualization system), big data full stack technology comprehensive practice project (financial big data statistical analysis platform) 3 comprehensive projects
Application Scenarios: professional teaching, comprehensive practical training, competition training.
Feature
In line with the industry covers the cutting-edge technology of enterprises
With the latest Hudi data lake technology as the core, Hudi real-time data lake storage mode provides more efficient support for big data analysis and mining, and uses Python, Pyecharts and FineBI self-service data visualization tools to complete data visualization; SpringBoot and React framework are used to realize data visualization on big screen web pages, and train students' big data full stack development skills.
Based on Neusoft's characteristic TOPCARES education methodology, industry-level projects are disintegrated into an advanced project system, from simple to deep, from easy to difficult, helping students to gradually exercise and improve their practical ability. There are 2 post level and 1 post group level projects provided for advanced training of different post abilities.
