《Data cleaning and visualization》Syllabus

Course Number:           2110030812

Course Name:               Data cleaning and visualizationInstructors:                        

Instructors:          Dun Minqi

Required Text:              Huang Yuan, Jiang Wenhao et al. Big Data Analysis: Python crawler, Data Cleaning and Visualization [M]. Beijing: Tsinghua University Press.2019

Course Description:   This course is a compulsory course for marketing majors. The purpose is to enable students to get in touch with and understand the working principle and use method of big data analysis, to enable students to have the ability of Python big data analysis, design and visual development, to have the basic skills of Kettle big data cleaning and storage, and to have a strong ability to analyze and solve problems. Lay a solid foundation for future work in big data-related fields.

                                                                                                   Credit/Teaching Hours

Outline of UG CPC Topics Covered in this Course:   (2.5Credits/40Teaching Hours)

I.  Crawler and Big Data                                            4 teaching hours

-Basic content of big data

-Crawler related knowledge

-Crawler grasping basic operation

II. Scrapy Crawler                                                 4 Teaching hours

-Scrapy The basic principles of crawlers

-Scrapy development and implementation competitor analysis

III. Fundamentals and Applications of Data Visualization                6 teaching hours

-Database links and queries

-A visual overview of the data

-Matplotlib visualizes basic operations

-Matplotlib visual drawing

-Pyecharts visualization application

IV. Storage and Cleaning of Big Data                                  6 teaching hours

-The basic framework of big data storage

-Principles and basic tools of data cleaning

-Concepts and methods of data standardization

V. Data Extraction and Acquisition                                   6 teaching hours

-Basic information about data extraction

-Text extraction and its implementation

-Extraction and implementation of web data

-Data acquisition and implementation

VI.Chapter 6 Pandas Data Analysis and Cleaning                      4 Teaching hours

-Pandas and the use of its syntax

-Pandas reads and cleans data

-Pandas data visualization

VII. Practical Training on Data Cleaning and Visualization          10 hours of instruction

-Data visualization

-Data storage and cleaning                                                                                 

-Data extraction and collection

-Data analysis and cleaning

-Comprehensive practical training

Total (Teaching Hours)                                              40  

Summary of UG CPC Topics Covered in this Course:                   (40Teaching Hours)