Product Launch | The Transportation Big Data Training Lab empowering universities to cultivate versatile big data talents

|2023-07-28 11:39

Transportation Big Data Training Lab is aimed at the transportation industry and is designed for big data and related majors. It provides training in core skills such as big data application development, with a focus on developing core abilities in big data implementation and operations, data collection and processing, and data analysis and visualization. The lab offers practical project resources, platform practice environments, and comprehensive process management support for both online and offline projects.

The design of practical tasks and content in the training lab is based on typical job tasks and professional technical competence requirements in the field of traffic big data. The training lab's platform can define knowledge and skill points for each practical task and provide data analysis and visualized reports on knowledge and skill points, professional technical competence, and job competency achievement based on the completion status of the practical tasks.

Students can experience and learn the entire practical process of the big data platform and full-stack technology in the lab. They can master the core professional skills required for big data collection and processing engineers, big data analysis and visualization engineers, and big data implementation and operation engineers. This will help students to have a better understanding of and insight into smart transportation in the era of data intelligence.

Level 1 Project

Transportation Big Data Full Stack Tech Practice

Using the business scenarios and data of the Neusoft Transportation Spatiotemporal Big Data Statistical Analysis Platform, this project introduces the overall implementation process of the big data platform, as well as the application of the latest technologies and mainstream tools. The project includes five practical modules: building the big data platform with Flink technology as the core, real-time data collection and processing, offline data synchronization and processing, data analysis and mining, and data visualization.

The project designs simulated data generated by traffic monitoring devices and provides information and data analysis results related to traffic flow, illegal behavior, and traffic safety through real-time and offline data analysis methods.

Students will become proficient in using various technologies and tools, including JDK, Flink, FlinkCDC, Kafka, Hbase, Phoenix, ClickHouse, Hadoop, Superset, Redis, MySQL, SpringBoot, Vue, Miniconda, Python, TensorFlow, Nginx, etc. This will help enhance students' problem-solving skills and cultivate their ability to innovate when dealing with practical issues.

Level 2 Project

Transportation Big Data Collection and Processing

The practice of transportation big data collection and processing includes five modules: data collection environment preparation, real-time business data collection, preprocessing of traffic flow data, synchronization of traffic accident data. It involves large-scale spatiotemporal data collection, real-time processing, and offline analysis, and utilizes appropriate technologies and tools to process and store data related to vehicle flow, speed, vehicle types, etc.

Through this project practice, students will be able to use different technologies and tools to acquire real-time and offline data. They will also learn how to clean, transform, and preprocess spatiotemporal data to ensure data quality and consistency. Students will become proficient in using various technologies and tools, including Xshell, Xftp, Navicat, MySQL, Hadoop, Kafka, ZooKeeper, JDK, Flink, FlinkCDC, Hbase, Phoenix, ClickHouse, Superset, Redis, Python, etc. This will enhance their problem-solving skills and proficiency in practical operations.

Level 2 Project

Transportation Big Data Analysis and Visualization

The practice of trasportation big data analysis and visualization includes seven modules: development of traffic flow data, real-time analysis of traffic flow data, analysis of traffic accident data, analysis of crowd spatial trajectory data, data source connection and dataset creation, MySQL data chart design and rendering, ClickHouse data chart design and rendering. It covers data integration and correlation, MySQL data correlation analysis, Flink real-time stream processing and window functions, ClickHouse data storage, Python data analysis, and Superset data visualization display, and other relevant technologies.

Through this project practice, students will understand the business meaning of traffic data and use data mining algorithms to discover valuable information and patterns hidden in the data. They will also learn how to present the findings using appropriate charts and visualizations. Students will become proficient in using technologies and tools such as Xshell, Xftp, Navicat, IDEA, MySQL, Hadoop, Kafka, ZooKeeper, JDK, Flink, FlinkCDC, Hbase, Phoenix, ClickHouse, Superset, Redis, Python, etc. This will enhance their problem-solving skills and proficiency in practical operations.

Level 2 Project

Transportation Big Data Platform Construction and Deployment

The practice of building and deploying the transportation big data platform aims to master the installation and configuration of Flink+ClickHouse and related components. It includes the installation and configuration of distributed storage system Hadoop and HDFS, real-time stream processing framework Flink, distributed columnar storage database HBase, relational database engine Phoenix based on HBase, real-time data transmission and processing tool Kafka, database ClickHouse for storing real-time analysis results, and in-memory database Redis, etc.

Through this project practice, students will gain hands-on experience and understand the installation requirements and steps of various tools. They will become familiar with setting and adjusting configuration files. Additionally, students will acquire the ability to solve problems and troubleshoot issues, enabling them to experience and master the basic tasks and related technologies involved in implementing and operating big data applications. Students will become proficient in using Linux, JDK, Hadoop, HDFS, YARN, Kafka, Redis, HBase, Flink, Phoenix, ClickHouse, and other technologies, enhancing their problem-solving skills and proficiency in practical operations.

An advanced practice teaching system that is compatible with the talent cultivation system for big data and related majors in universities

The design of the practical teaching system in the training room is based on Neuedu's TOPCARES educational methodology. It is compatible with the talent cultivation system for big data and related majors in universities. The system incorporates elements of "new theories, new technologies, new tools, new applications, and new products" to provide a practical platform and industry project resources for the construction and reform of the practical teaching system in the fields of data science, big data technology, and computer science and technology with a focus on big data.

The Supporting Platform: Neuedu Cloud Practice Platform

Support Blended Practical Teaching Process

Students will gradually complete the practical arrangements based on the practical requirements and task steps assigned by the teacher. They can also learn and refer to the provided project documentation, enabling them to "learn by doing and do by learning."

Provide Multi-dimentional Data Statistics and Performance Assessment

Real-time statistics are conducted on various data including process tracking records, result data analysis, learning behavior data, practical training data, and performance data to comprehensively evaluate the teaching effectiveness of the course.
These data are collected and analyzed to provide a more detailed representation of students' mastery of specific skill points. This is achieved through the real-time statistical analysis of tiered scores in practical training, allowing for a more comprehensive presentation of the course's teaching effectiveness.

Offer Precise Job Competency Matching Management

Constructing a practical skill-point and talent-job adaptive matching based on the target job competency model and core competency requirements enables students to have a model-based understanding of their abilities, intelligent learning, visualization of achievements, monitoring of the learning process, evaluation of talents, and matching them to suitable job positions.