Embedded Files
Schedule
Schedule
10-12 pm Intro to Python (Slides) (Google Colab)
12-1 pm LUNCH - Data Science TAE with Xingye Qiao, Department of Mathematical Sciences and Chair of the DS TAE
1-3 pm Python Applications (slides) (Google Colab) (data file)
3-4 pm Optional Session: Topic Modeling (presentation link)
Session Links: Robots Reading Vogue, Mining the Dispatch, and R Cloud studio link
Installations for Day 4
Individual Consultations
Collaborative Notes
Collaborative Notes
Downloads
Downloads
We will use the Google Colaboratory (Colab) application that is part of your Binghamton Google Suite account. Make sure you are logged in to your Google account for access.
For each session, open the Google Colab file linked above (one for Intro to Python and one for Python Applications).
The file will open as a new tab in your browser with the file
Click File in the upper left corner > Click Save a Copy in Drive
A new tab will open in your browser with a file that begins with "Copy of...."
Use the copied files for each of our sessions today
Since Colaboratory is a Google application, Google Chrome will be our best browser option for these sessions.
If you currently have Google Chrome on your computer, you are all set.
If not, go to https://www.google.com/chrome/ and download for your operating system (Windows or Mac OS)
Readings and Preparation
Readings and Preparation
REQUIRED READINGS and PREPARATION for Wednesday, August 11 (please read before arriving at the session)
Annette Vee, Coding Literacy: How Computer Programming is Changing Writing, "Introduction: Computer Programming as Literacy," (2017): (Link to chapter through Binghamton University Libraries); Full book available through Libraries
Richard Jean So, "Introduction: Contemporary Culture after Data Science," Journal of Cultural Analytics 4 (2021): 39-47, https://culturalanalytics.org/article/22335.
Michael J. Garbade, "A simple introduction to natural language processing," (October 2018): https://becominghuman.ai/a-simple-introduction-to-natural-language-processing-ea66a1747b32
RECOMMENDED READINGS and PREPARATION for Wednesday, August 11
Cathal Horan, "Tokenizers: how machines read," (January 2020): https://blog.floydhub.com/tokenization-nlp/
"spaCy101: everything you need to know," https://spacy.io/usage/spacy-101 (skim through but worth coming back to)
Ritvik Kharkar, "Making 3 Easy Maps With Python, "(April 2019): https://towardsdatascience.com/making-3-easy-maps-with-python-fb7dfb1036
Nick Monfort, "Toroko Gorge," infinitely generated poem: https://nickm.com/taroko_gorge/
FURTHER READING
Sites for continued learning:
Free Python for DH Course: https://pythonhumanities.com/ (provides project ideas as well)
The Python Tutorial: https://docs.python.org/3/tutorial/
W3Schools Python Tutorial: https://www.w3schools.com/python/
Geeks4Geeks: https://www.geeksforgeeks.org/python-programming-language/
Codecademy: https://www.codecademy.com/
Kaggle Notebooks: https://www.kaggle.com/notebooks
Kim Pham, The Programming Historian, "Web mapping with Python and Leaflet": https://programminghistorian.org/en/lessons/mapping-with-python-leaflet
William J. Turkel and Adam Crymble, The Programming Historian, "Working with text files in Python,": https://programminghistorian.org/en/lessons/working-with-text-files
Scott B. Weingart, "Teaching yourself to code in DH," (February 2017), (collection of resources): https://scottbot.net/teaching-yourself-to-code-in-dh/
University of Amsterdam - Faculty of Humanities, "Digital Humanities Workbench,": https://www2.fgw.vu.nl/werkbanken/dighum/tools/programming.php
On topic modelling for the optional 3 pm session: Matt Jockers, "The LDA Buffet: A Topic Modelling Fable," Matthew L. Jockers (blog), https://www.matthewjockers.net/macroanalysisbook/lda/
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