Table of Contents
Part 1: Getting started
Chapter 1: Introduction
The role of computational analysis in social science
Why Python and/or R?
How to use this book?
Installing R and Python
Installing third-party packages
Chapter 2: Fun with data and visualizations
Fun with tweets
Fun with textual data
Fun with visualizing geographic information
Fun with networks
Chapter 3: Programming concepts for data analysis
About objects and data types
Simple control structures: loops and conditions
Functions and methods
Chapter 4: How to write code
Re-using code: how not to re-invent the wheel
Understanding errors and getting help
Best practices: beautiful code, GitHub, and notebooks
Part 2: Cleaning and analyzing data
Chapter 5: From file to data frame and back
Why and when do we use data frames?
Reading and saving data
Data from online sources
Chapter 6: Data wrangling
Filtering, selecting, and calculating
Calculating values
Grouping and aggregating
Merging data
Reshaping data: wide to long and long to wide
Restructuring ‘messy’ data
Chapter 7: Exploratory data analysis
Simple exploratory statistics
Visualizing data
Clustering and dimensionality reduction
Chapter 8: Statistical Modeling and Supervised Machine Learning
Statistical modeling and prediction
Concepts and Principles
Classical Machine Learning: From Naive Bayes to neural networks
Deep Learning
Validation and best practices
Part 3: Text Analysis
Chapter 9: Processing text
Text as a string of characters
Regular expressions
Using Regular expressions in Python and R
Chapter 10: Text as data
The bag of words and term-document matrix
Cleaning, weighting, selecting features
Advanced representations f text
Natural language processing
Chapter 11: Automatic analysis of text
Overview of text analysis methods
Dictionary approaches to text analysis
Supervised text analysis: automatic classification and sentiment analysis
Unsupervised text analysis: topic modeling
Part 4: Beyond Structured Data
Chapter 12: Scraping online data
Using open web APIs
Retrieving and parsing web pages
Authentication, cookies, and sessions
Ethical, legal and practical considerations
Chapter 13: Introduction to Network Data
Representing and visualizing networks
Social Network Analysis
Chapter 14: Introduction to Image and Video Data
Beyond text analysis: Images, audio, and video
Using existing APIs for analysing image data
Storing, representing, and converting image and video data
Deep learning for image analysis
Part 5: Next Steps
Chapter 15: Scaling up and distributing
Storing data in SQL and noSQL databases
Using cloud computing
Publishing your source
Distributing your software as container
Chapter 16: Where to go next?
How far have we come?
Where to go next?
Open, transparent, and Ethical Computational Science
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0 有用 女人经痛时 2025-05-24 23:19:22 荷兰
textbook 虽全但乱