Big Data Technology Integrated Training Lab
Introduction
The Lab is mainly for data science and big data technology majors, through 10 core courses 130+ level 3-5 projects, 5 level 1-2 projects advanced training of big data development engineers, big data acquisition and processing engineers, big data analysis and visualization engineers and other positions. To help students effectively practice their ability to deal with and solve complex engineering problems.
Enterprise Positions: big data development engineer, big data acquisition and processing engineer, big data analysis and visualization engineer, etc
Applicable Majors: university data science and big data technology, big data management and application, etc
Course Products: fundamentals of programming (Python language), data structure (Python), big data technology architecture, data visualization, high-performance system architecture, data mining, distributed computing framework, distributed real-time computing, practical machine learning, deep learning
Project Products: professional guidance and career planning (Data science and big data technology major), data acquisition and pre-processing practice project (comprehensive website data acquisition and pre-processing practice), big data storage and analysis processing practice project (website log data analysis practice), big data analysis and visualization practical project (e-commerce big data analysis practice), data science comprehensive project training The project can also be combined with 12 industry expansion projects such as e-commerce big data, transportation big data, financial big data, and telecom big data for training.
Application Scenarios: professional teaching, comprehensive practical training, competition training
Feature
Project system advanced
Ten sets of supporting resources, course standards, teaching courseware, instructions, teaching plans, teaching videos, test papers, exercises, experimental environment, material packages, source code, etc.
The Lab is mainly for data science and big data technology majors, through 10 core courses 130+ level 3-5 projects, 5 level 1-2 projects advanced training of big data development engineers, big data acquisition and processing engineers, big data analysis and visualization engineers and other positions. To help students effectively practice their ability to deal with and solve complex engineering problems.
Enterprise Positions: big data development engineer, big data acquisition and processing engineer, big data analysis and visualization engineer, etc
Applicable Majors: university data science and big data technology, big data management and application, etc
Course Products: fundamentals of programming (Python language), data structure (Python), big data technology architecture, data visualization, high-performance system architecture, data mining, distributed computing framework, distributed real-time computing, practical machine learning, deep learning
Project Products: professional guidance and career planning (Data science and big data technology major), data acquisition and pre-processing practice project (comprehensive website data acquisition and pre-processing practice), big data storage and analysis processing practice project (website log data analysis practice), big data analysis and visualization practical project (e-commerce big data analysis practice), data science comprehensive project training The project can also be combined with 12 industry expansion projects such as e-commerce big data, transportation big data, financial big data, and telecom big data for training.
Application Scenarios: professional teaching, comprehensive practical training, competition training
Feature
Project system advanced
The five-level project runs through all the core courses of the major, and can cover the teaching of core positions such as big data collection and processing, big data analysis and visualization, and large number development.
Ten sets of supporting resources, course standards, teaching courseware, instructions, teaching plans, teaching videos, test papers, exercises, experimental environment, material packages, source code, etc.
