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.
全英文大数据分析技术这门课程按照Bloom学习掌握分类法，构建学习者能力层级.课程系统地讲解了大数据分析基础的知识和必备的技能。在知识的记忆和理解的基础上，培养学生的应用和分析的能力，并进一步培养学生的评价和创新的能力。课程采用理论讲解和工程实践训练相结合的方式。
一、理论部分：
大数据分析技术理论知识的讲解是在大数据技术框架1数据存储系统2数据处理系统和3数据应用系统的层次架构下对大数据分析技术的内容进行系统化梳理和了详细的阐述，如图所示
在1数据存储系统中分为4个子部分
1.1数据收集与建模；
1.2 分布式文件系统；
1.3 分布式数据库和数据仓库；
1.4 统一数据存取接口
在2大数据处理系统中分为3个子部分
2.1 数据分析算法；
2.2 计算模型；
2.3 计算引擎和计算平台；
在3 大数据应用系统中分为3个子部分
3.1 大数据可视化
3.2 大数据产品和服务
3.3 大数据应用
在大数据应用部分以推荐系统和社交网络两个大数据典型应用为例讲解了相关原理，算法等。
在理论讲解中，梳理了整个课程内容知识体系Roadmap帮助学生系统化的建立大数据分析知识体系。在比较复杂的知识点的讲解时，精选了各个知识点的全网优秀讲解视频辅助学生清晰理解所学内容，因为优秀的视频中不仅是讲解人的丰富经验的总结，而且配有讲解动画展示，非常直观清晰地解释了相关知识的原理。
二、实验部分：
在技术动手实践部分设计了5个部分的实验，包括:
1. 动态网络爬虫程序设计
2. Spark MLlib 学习与应用
3. Tensorflow学习与应用
4. 推荐系统理解与构建
5. 社交网络分析与可视化
以上5个专题分别设计了几个由浅入深的实验训练学生大数据分析的动手能力。所有实验均配备有完整案例，包括实验设计思路和步骤，实验手册和源代码等。
通过全英文大数据分析技术理论讲解和工程实验训练，学生可以建立知识体系，深入理解大数据分析技术中的概念，原理，平台，技术等。通过实验的动手练习，实现大数据理论知识的实际运用，加深对原理概念的理解，并提高了动手解决大数据分析工程问题的分析和解决能力。同时对大数据分析领域英语的能力可以有很大程度提升。
