About This Course
The course of Big Data Analysis Technology in English builds a learner competency hierarchy according to the BLOOM 's Taxonomy. It systematically explains the basic knowledge and necessary skills of big data analysis. It develops students' application and analytical skills based on the knowledge memorizing and understanding, in further develop the evaluating and creating ability. It combined the theoretical explanation and engineering practice training.
1. Theory:
Big Data Analysis Technology theoretical knowledge is carried out following the general architecture: 1 data storing system, data processing system and data application system. Shown as the figure.
In 1 data storing system, it can be divided into 4 parts
1.1 Data collection and modeling；
1.2 Distributed file system；
1.3 Distributed Database and data warehouse；
1.4 Unified Data Access Interface
In 2 data processing system, it can be divided into 3 parts
2.1 Data analysis algorithm
2.2 Computing model;
2.3 Computing Engine and platform;
In 2 data application system, it can be divided into 3 parts
3.1 big data visualization
3.2 big data product and services
3.3 big data application
In the big data application, the relevant principles and algorithms are explained, taking two typical big data applications of recommendation system and social network as an example.
And in Theoretical Explanation, the whole course content roadmap helps students to systematically establish a knowledge system of big data analysis. When explaining the complex knowledge points, the excellent explanation videos selected from the whole network facilitate the students clearly understand what they have learned. Because the excellent video is not only a summary of the rich experience of the explainer, but also equipped with an explanation animation display, which explains the principle of the relevant knowledge very intuitively and clearly.
2. Experiments
Five experiments were designed in this part. includes:
1. Dynamic web crawler
2. Spark MLlib learning and applying
3. TensorFlow learning and applying
4. Recommendation system understanding and construction
5. Social Network Analysis and Visualization
For the above five topics, several experiments from simple to complex, from the shallow to the deep are designed to train students' hands-on ability of big data analysis. All experiments are equipped with complete case explanations, including experimental design ideas and steps, experimental manuals and source codes.
Through the theoretical explanation of big data analysis technology and engineering experiment training, students can build a knowledge system，In-depth understanding of the concepts, principles, platforms, technologies, etc. in big data analysis technology. Through the hands-on practice of the experiment, the practical application of the theoretical knowledge of big data is realized, the understanding of the principle and concept is deepened, and the analysis and solution ability to solve engineering problems of big data analysis is improved. At the same time, the big data analysis relevant English ability can be greatly improved.
Requirements
Through the theoretical explanation of big data analysis technology and engineering experiment training, students can build a knowledge system，In-depth understanding of the concepts, principles, platforms, technologies, etc. in big data analysis technology. Through the hands-on practice of the experiment, the practical application of the theoretical knowledge of big data is realized, the understanding of the principle and concept is deepened, and the analysis and solution ability to solve engineering problems of big data analysis is improved. At the same time, the big data analysis relevant English ability can be greatly improved.
Course Staff
Haiying Che
Haiying Che, associate professor in School of Computer Science, Beijing Institute of Technology, China, Ph.D. in Beijing Institute of Technology, Post Doctor in AIFB institute of Karlsruhe Institute of Technology, Germany, and Visiting Scholar of Stevens Institute of Technology, USA. Council member of the German and Austrian Branch of the European and American Scholars Association.
Experienced in teaching in English more than 10 years, already built 3 Online courses by herself, including Big Data Analysis in English and Chinese, Software Engineering, and jointly built Big Data Technology Instruction online course together with other colleges.
Research interests include Big Data Analysis, Artificial Intelligence related field, Blockchain Application etc., published 20+ high level-academic papers in related fields, owns 3 patents and has extensive practical engineering experiences.
