—— Tianbao Talent, a platform for the growth of application ability of big data talents, provides systematic solutions from platform, course content to teaching management for the cultivation of big data talents in colleges and universities through model innovation and technological innovation from the perspective of industry talent demand. The core of the platform revolves around the cultivation of talents' application ability. Based on practice, the platform integrates the knowledge, skills and methodology needed for the cultivation of big data talents. The core is to cultivate students' data thinking and problem-solving ability through hands-on practice.
Tianbao Talents-The core of the big data application ability growth platform is oriented to big data management applications, data science and big data technology and other big data-related majors, as well as interdisciplinary subjects, which are applied to teaching practice, centralized training, online competitions, learning exchanges and other scenarios.
The core characteristics of Tianbao talents
1, DT-CMPA talent ability map, so that the learning objectives are clear and definite.
Based on the talent standards of the big data industry and the analysis of the recruitment needs of more than 10,000 big data-related jobs, the post quality model is defined, and the learning path and learning route are planned from the position competency. Based on the talent ability map, colleges and universities can plan the curriculum system according to their own discipline construction goals and talent training direction. Students can also plan their own learning path according to their employment goals, so that students can learn more purposefully and know what to learn and why to learn.
2. Professional curriculum practice resources to meet different types of teaching and experimental needs.
1) system, a specialized course for famous teachers.
Through exchanges and cooperation with many college teachers, around the two foundations and one chain of the learning route of big data, we have created nine directions and hundreds of classifications, and developed and designed more than 1000 atomic courses, which provided rich curriculum resources for practical teaching in colleges and universities.
2) Innovating the course design of atoms and integrating knowledge with practice.
Q: What is an "atomic class"?
A: The technical points and knowledge points involved in the course of Atomization, from the basic principles and characteristics to the final application, are gradually guided by the mode of breaking through barriers, with the aim of enabling students to thoroughly understand, master and apply every knowledge point.
Based on atom course, "personalized customized class" can be realized. Teachers can freely choose from atom course library according to the talent training needs, subject characteristics and teaching materials used, flexibly match knowledge atoms with appropriate difficulty and flexibly combine them to realize "personalized customized classroom".
3) Personalized classes, teaching students in accordance with their aptitude.
Customize the "teaching classroom", customize the teaching plan, record students' learning behavior and evaluation results, gain insight and analyze students' learning paths and achievements, pay equal attention to both process and results, and explore the best scheme to achieve teaching goals.
3. More than a thousand projects apply practical experience to cultivate students' data thinking and problem-solving ability.
Based on the experience of Merrill Lynch data's big data construction projects for head customers in thousands of industries, the training of six industries 100+ projects is embedded based on real project cases, so that students can understand the latest practices and application scenarios in the industry and improve their ability to solve practical problems through practical exercises.
For big data learning, the most difficult thing is not a piece of code implementation of Python or the mastery of algorithm principles, but to digitize business problems in specific business scenarios and use analytical tools and big data knowledge to find solutions.
For each training project, the whole process of project landing is deeply analyzed and the whole process of project landing is restored. The analytical methodology, the thinking mode of transforming business problems into mathematical problems and the application skills of knowledge and skills are all integrated into specific project training cases, so that students can master methods and improve their thinking mode through training.
4. Comprehensive practical operation platform, providing rich experimental training environment.
1) technical innovation, intelligent and efficient experimental environment management.
Based on container and virtualization technology, it provides experimental training environments such as online programming, remote command line, interactive programming and remote desktop, and replaces complex experimental environment management with insensible experimental resource allocation and recovery, making experimental management intelligent and efficient.
2) coding and dragging dual environment, giving consideration to application and development.
There is not only a coding environment for teaching principles and technologies, but also a dragging environment for applications. Drag-and-drop data visualization analysis and machine learning modeling platform, aiming at application, is fully integrated with coding environment to meet the application practice of big data analysis and provide environmental support for cultivating the application ability of interdisciplinary big data talents.
5. Stimulate students' enthusiasm for learning and create a "self-driven" platform for ability growth.
Break through the data playground of barriers, competitions and independent exploration, break the traditional learning mode, create a learning experience that combines professionalism and fun, fully stimulate students' enthusiasm for independent learning, and create a "self-driven" ability growth platform.