Course description
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. 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 contents
Click the down arrow icon [ 🔽 ] to expand and collapse the course topics.
🔽 Introduction
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- • Big Data Analysis 1-1 Basic concept
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- • Big Data Analysis 1-2 Structured data and unstructured data
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- • Big Data Analysis 1-3 The fourth paradigm
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- • Big Data Analysis 1-4 Big data characters
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- • Big Data Analysis 1-5 Big data lifecycle
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- • Big Data Analysis 1-6 Processing flow
- • Big Data Analysis 1-7 Architecture
🔽 Data Collection
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- • Big Data Analysis 2-1 Data resources
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- • Big Data Analysis 2-2 Internal data acquisition
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- • Big Data Analysis 2-3 External data acquisition
- • Big Data Analysis 2-4 Deep web
🔽 Data Preprocessing
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- • Big Data Analysis 3-1 Data preprocessing overview
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- • Big Data Analysis 3-2 Data quality
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- • Big Data Analysis 3-3 Data cleaning technology
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- • Big Data Analysis 3-4 Data transform
- • Big Data Analysis 3-5 Data reduction
🔽 Data Storing System
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- • Data modeling
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- • Distributed file system
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- • No SQL database
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- • Characters of No SQL DB
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- • Four types of No SQL DB
- • UDAI
🔽 Data Processing System
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- • Data processing system architecture
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- • Data processing algorithms
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- • Big data analysis algorithms
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- • Linear regression using least square method
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- • K-nearest neighbors
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- • K-means clustering
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- • Naive Bayes
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- • Apriori algorithms explained association rule mining
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- • Support vector machines
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- • CNN
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- • Principal component analysis
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- • Batch processing
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- • Streaming processing
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- • Massively parallel processing for structured data
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- • Memory computing-spark
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- • In memory computing-HANA
- • Distributed graph computing-Pregel
🔽 Big Data Computing Platforms and Applications
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- • Spark MLib
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- • Tensorflow
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- • Recommendation system
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- • Recommendation system 2
- • Social network analysis
This course includes:
Start Date: TBA
  Instructor-led
  Downloadable resources (books and articles)
  60 days access
  Access on mobile and TV
  Advanced Level
  Certificate of completion