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[BOOK+SCREENCASTS] Digital Research Methods with Mathematica

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This summer (2020) I revised the second edition of my open content, open access and open source textbook Digital Research Methods with Mathematica. It is freely available as a Mathematica notebook which can be accessed with either the Mathematica software or with Wolfram's free Wolfram Player. The content remains mostly the same as the previous version with a few minor edits. New in this edition are more than 100 short screencasts which I created to support online learning during the COVID-19 pandemic.

https://williamjturkel.net/digital-research-methods-with-mathematica

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The book focuses on learning to read code to the point where one can modify it to solve related research problems. Here are the topics that are covered.

  1. Introduction to Mathematica. Interacting with notebooks.
  2. Reading Code. Word frequency, word clouds and stopwords.
  3. Computable Knowledge. Entities, tables, timelines and maps.
  4. Text Content. Mathematica notebooks and expressions, strings and natural language processing.
  5. Data Structures. Lists, associations and datasets.
  6. Reusing Code. Defining and developing functions, keyword in context (KWIC).
  7. Networks. Metadata, matrices and social network analysis.
  8. Indexing and Searching. Pattern matching, topic classification and term distribution.
  9. Geospatial Analysis. Geographic information: raster, vector and attribute data.
  10. Images. Computer vision, face detection, feature extraction and image mining.
  11. Page Images. Optical character recognition (OCR), figure extraction and classification.
  12. Crawling. Browser automation, batch downloading, web archives and WARC files.
  13. Linked Open Data. Resource description framework (RDF), SPARQL queries and endpoints, JSON-LD.
  14. Markup Languages. Scraping and parsing, XML, really simple syndication (RSS) and text encoding initiative (TEI).
  15. Studying Societies. Computational social science, search data, social media and social networks.
  16. Extracting Keywords. Information retrieval, term frequency-inverse document frequency (TF-IDF) and rapid automatic keyword extraction (RAKE).
  17. Word and Document Vectors. Feature extraction, dimension reduction, word embeddings and global vectors.
  18. References, web services, bibliographic linked open data and citation networks.
  19. Natural Language. Multilingual analysis, computational linguistics and sentiment analysis.
  20. Web Services. Entity networks, publication search, dashboards, manipulating JSON.
  21. Databases. Parts, selections and transformations, computations and querying, relations.
  22. Measuring Images. Photogrammetry, georectification, handwriting and facial 3D reconstruction.
  23. Machine Learning. Unsupervised clustering, classify, predict and transfer learning.

SAMPLE PAGES: Lesson 21.3. Measuring Images: Handwriting

6 Replies

This is simply a fantastic resource thank you!

Not even included in the table of contents above is an additional (nearly 50 page) section at the end of the textbook with ideas for further digital humanities projects and experiments often linking to existing data and sample code to get one started.

Thanks for the kind words, Arno!

@William, this is an awesome book! From my contacts I know that it is very appreciated by many and that you have a great readership. The book is also used in classrooms. Thank you very much for the announcement of the screencast companion! 100 videos is quite a production. How long did it take you to record them? Wonderful work!

Thanks, @Vitaliy Kaurov! I think it took the better part of a month to make the screencasts. I was using ScreenFlow software and found that the file sizes tended to get pretty large for anything over about 10-15 minutes. That is fine, because many experts on online learning suggest that things like videos and screencasts should not be much longer than that.

I just signed into the Community for the first time and found this. What a treat!

Thank you so much for this wonderful resource.

W

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