Quantitative Information Analysis_南京大学

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Quantitative Information Analysis

开课时间： 2023年09月15日 ~ 2023年12月30日

学时安排： 3-5小时每周

当前开课已结束已有 222 人参加

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课程详情

课程评价(5)

spContent=This is a world of quantitative information analysis (QIA) specially designed for students from Library and Information Science, and related fields. The four lecturers of this course, including Professor Jiang LI from Nanjing University, Professor Lin ZHANG from Wuhan University, Dr Chao MIN from Nanjing University, and Dr Yi BU from Peking University, have overseas degrees or research experiences and are outstanding scholars in the field of QIA. By learning this course, you are expected to learn basic techniques of quantitative information analysis, such as correlation and causality, text mining, the design of indicators, etc. If you have any questions during the study process, please do not hesitate to contact the teaching assistant Ms Zhikun Xue at 522022140127@smail.nju.edu.cn.

This is a world of quantitative information analysis (QIA) specially designed for students from Library and Information Science, and related fields. The four lecturers of this course, including Professor Jiang LI from Nanjing University, Professor Lin ZHANG from Wuhan University, Dr Chao MIN from Nanjing University, and Dr Yi BU from Peking University, have overseas degrees or research experiences and are outstanding scholars in the field of QIA. By learning this course, you are expected to learn basic techniques of quantitative information analysis, such as correlation and causality, text mining, the design of indicators, etc. If you have any questions during the study process, please do not hesitate to contact the teaching assistant Ms Zhikun Xue at 522022140127@smail.nju.edu.cn.

—— 课程团队

课程概述

This is a wonderful course that will bring you to the world of quantitative information analysis. By learning this course, you are expected to acquire the basics in Information Science and Data Science, and the techniques in quantitative information analysis. Then, you will be qualified for both business work and research tasks involving data analysis. The content of this course features a strong connection with the practice of academic research. Each method is exemplified using a published academic paper to better prepare the participants for doing their own academic research.

授课目标

The aim of this course is equipping students in Library & Information Science and other Social Sciences/Humanities with essential knowledge and skills in analyzing quantitative information.

课程大纲

Chapter 1. Introduction to QIA

1.1 What is QIA?

1.2 Software used for QIA

Short test for 1.2

Short test for 1.1

Chapter 2. QIA methods: Indicators and measurements

2.1 Collaboration indices I

2.2 Collaboration indices II

2.3 The h-index I

2.4 The h-index II

2.5 Network indicators

2.6 Diversity indicators

Short test for 2.3

Short test for 2.4

Short test for 2.5

Short test for 2.1

Short test for 2.6

Short test for 2.2

Chapter 3. QIA methods: correlation and regression

3.1 Exploratory data analysis (I)

3.2 Exploratory data analysis (II)

3.3 Correlation coefficient

3.4 OLS regression

3.5 Other types of regressions

3.6 Examples of correlations and regressions

3.7 Endogeneity

Short test for 3.6

Short test for 3.7

Short test for 3.1

Short test for 3.2

Short test for 3.3

Short test for 3.4

Short test for 3.5

Chapter 4. QIA methods: Causality

4.1 Introduction to causality

4.2 Counterfactual framework

4.3 Propensity Score Matching

4.4 Difference in difference

4.5 Example of PSM & DID

4.6 Regression Discontinuity Designs

4.7 Example of RDD

4.8 Granger causality

4.9 Example of Granger causality

Short test for 4.5

Short test for 4.6

Short test for 4.7

Short test for 4.2

Short test for 4.8

Short test for 4.3

Short test for 4.9

Short test for 4.4

Short test for 4.1

Chapter 5. QIA methods: Information networks & visualization

5.1 Introduction

5.2 Basic Elements

5.3 Small Worlds

5.4 Hubs

5.5 Direction & weights

5.6 Network models

5.7 Communities

5.8 Dynamics

Short test for 5.7

Short test for 5.8

Short test for 5.1

Short test for 5.2

Short test for 5.3

Short test for 5.4

Short test for 5.5

Short test for 5.6

Chapter 6. QIA methods: Text mining

6.1 Introduction to text mining

6.2 Five steps of text mining

6.3 Data preprocessing

6.4 Sentiment analysis

6.5 Text clustering and text classification

6.6 Sentiment analysis

Short test for 6.4

Short test for 6.5

Short test for 6.6

Short test for 6.1

Short test for 6.2

Short test for 6.3

Chapter 7. Future of QIA

7.1 More fields will use QIA

7.2 More methods of QIA

Short test for 7.1

Short test for 7.2

展开全部

预备知识

Basics on calculus, linear algebra and statistics.

常见问题

Please complete the quizzes and the assignments before 31 December, 2023, after you watch the videos. The scores you get from the quizzes will amount to the final score determining the certificate of completion.

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李江

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