Digital Research Methods with Mathematica, 2nd Rev Ed
Author
William Turkel
Title
Digital Research Methods with Mathematica, 2nd Rev Ed
Description
The book focuses on learning to read code to the point where one can modify it to solve related research problems.
Category
Books & Supplements
Keywords
URL
http://www.notebookarchive.org/2020-09-9pnltch/
DOI
https://notebookarchive.org/2020-09-9pnltch
Date Added
2020-09-21
Date Last Modified
2020-09-21
File Size
19.11 megabytes
Supplements
Rights
Redistribution rights reserved
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Digital Research Methods with Mathematica, 2nd Rev Ed
Digital Research Methods with Mathematica, 2nd Rev Ed
William J. Turkel
Professor of History
University of Western Ontario
william.j.turkel@gmail.com
Professor of History
University of Western Ontario
william.j.turkel@gmail.com
v2.0, revision 01
Summer 2020
Summer 2020
This is a Mathematica notebook
This is a Mathematica notebook
If you have never used one before, you can expand and collapse sections clicking on the cell group opener above. When a section is closed, it looks like this » and when a section is open the two arrows point downwards.
Each cell has its own bracket, which you can see on the right. Brackets are nested into larger groupings.
The cell beneath this one contains code. You can evaluate it by selecting its cell bracket and pressing ↵
2+2
Table of Contents
Table of Contents
You can click on a heading to jump directly to that chapter.
◼
About this Book
◼
Preface
◼
Introduction
◼
Lesson 00. Introduction to Mathematica. Interacting with Mathematica notebooks.
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Lesson 01. Reading Code. Word frequency, word clouds and stopwords.
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Lesson 02. Computable Knowledge. Entities, tables, timelines and maps.
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Lesson 03. Text Content. Mathematica notebooks and expressions, strings and natural language processing.
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Lesson 04. Data Structures. Lists, associations and datasets.
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Lesson 05. Reusing Code. Defining and developing functions, keyword in context (KWIC).
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Lesson 06. Networks. Metadata, matrices and social network analysis.
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Lesson 07. Indexing and Searching. Pattern matching, topic classification and term distribution.
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Lesson 08. Geospatial Analysis. Geographic information: raster, vector and attribute data.
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Lesson 09. Images. Computer vision, face detection, feature extraction and image mining.
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Lesson 10. Page Images. Optical character recognition (OCR), figure extraction and classification.
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Lesson 11. Crawling. Browser automation, batch downloading, web archives and WARC files.
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Lesson 12. Linked Open Data. Resource description framework (RDF), SPARQL queries and endpoints, JSON-LD.
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Lesson 13. Markup Languages. Scraping and parsing, XML, really simple syndication (RSS) and text encoding initiative (TEI).
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Lesson 14. Studying Societies. Computational social science, search data, social media and social networks.
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Lesson 15. Extracting Keywords. Information retrieval, term frequency-inverse document frequency (TF-IDF) and rapid automatic keyword extraction (RAKE).
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Lesson 16. Word and Document Vectors. Feature extraction, dimension reduction, word embeddings and global vectors.
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Lesson 17. Citations. References, web services, bibliographic linked open data and citation networks.
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Lesson 18. Natural Language. Multilingual analysis, computational linguistics and sentiment analysis.
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Lesson 19. Web Services. Entity networks, publication search, dashboards, manipulating JSON.
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Lesson 20. Databases. Parts, selections and transformations, computations and querying, relations.
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Lesson 21. Measuring Images. Photogrammetry, georectification, handwriting and facial 3D reconstruction.
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Lesson 22. Machine Learning. Unsupervised clustering, classify, predict and transfer learning.
◼
Project Ideas
About this Book
About this Book
Preface
Preface
Introduction
Introduction
Lesson 00. Introduction to Mathematica
Lesson 00. Introduction to Mathematica
Lesson 01. Reading Code
Lesson 01. Reading Code
Lesson 02. Computable Knowledge
Lesson 02. Computable Knowledge
Lesson 03. Text Content
Lesson 03. Text Content
Lesson 04. Data Structures
Lesson 04. Data Structures
Lesson 05. Reusing Code
Lesson 05. Reusing Code
Lesson 06. Networks
Lesson 06. Networks
Lesson 07. Indexing and Searching
Lesson 07. Indexing and Searching
Lesson 08. Geospatial Analysis
Lesson 08. Geospatial Analysis
Lesson 09. Images
Lesson 09. Images
Lesson 10. Page Images
Lesson 10. Page Images
Lesson 11. Crawling
Lesson 11. Crawling
Lesson 12. Linked Open Data
Lesson 12. Linked Open Data
Lesson 13. Markup Languages
Lesson 13. Markup Languages
Lesson 14. Studying Societies
Lesson 14. Studying Societies
Lesson 15. Extracting Keywords
Lesson 15. Extracting Keywords
Lesson 16. Word and Document Vectors
Lesson 16. Word and Document Vectors
Lesson 17. Citations
Lesson 17. Citations
Lesson 18. Natural Language
Lesson 18. Natural Language
Lesson 19. Web Services
Lesson 19. Web Services
Lesson 20. Databases
Lesson 20. Databases
Lesson 21. Measuring Images
Lesson 21. Measuring Images
Lesson 22. Machine Learning
Lesson 22. Machine Learning
Project Ideas
Project Ideas
Cite this as: William Turkel, "Digital Research Methods with Mathematica, 2nd Rev Ed" from the Notebook Archive (2020), https://notebookarchive.org/2020-09-9pnltch
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