Ideal Syllabus

After surveying a number of Digital Humanities (DH) syllabi and textbooks I came away with the impression that there are a wide variety of interpretations of exactly what DH are and how they should be taught.

In the ideal worlds of places like Stanford and UC Berkeley there are very broad and deep technical talent pools as well as experts in nearly every field of the humanities.  The model there trends to collaboration between domain experts across the technical divide.  This enables centers there to drive innovation by extracting the most value and meaning from layring on

An “Ideal” syllabus in the Digital Humanities is predicated on the existence of a particular audience.  In this case


  • Give a high-level but essential, comprehensive and integrated coverage of Digital Humanities
  • To give humanities students the mental models, tools and ability to express their ideas within the framework of data science and add another powerful dimension to their ability to conduct original research
  • Think creatively and critically about data within the context of a broad range of humanities studies
  • Awaken and enable student as to the immense potential and practical aspects of leveraging technology in their humanistic fields of study
  • Make it interesting by tying study of “hard”/”dry” tools to personal academic passion and larger threats/opportunities of society as well as cultural memes
  • Humanity the only chance to save us from our digital selves
  • Connect technology to central cultural and political ideas today
  • Provide motivation through clarity of purpose to conduct follow-up study in a wide range of foundational and related fields like studio art, statistics/math and programming

Digital Humanities




  • Computational Theory
  • Programming Language Hierarchies
  • Evolution to multi-core, distributed/parallel, FP
  • Imperative
  • Programmer expected to deliver 50 lines of debugged code/day
  • Object Oriented
  • Functional
  • Logic
  • Python Anaconda
  • Software Design: Data Flow, IPython/Pandas, Spark RDD/DataFrames
  • RegEx
  • POS Parser
  • Serialization/De-(Re)hydration – Python Pickle
  • Security (Eval, Pickle, etc)
  • Compression, Encryption,
  • PyLint
  • Python Call Graph
  • Google SyntaxNet (TensorFlow) Parsey McParseface
  • Voter Fraud/Hacking (YouTube Demo)
  • Autonomous Killer Robots (2001, Terminator Skynet)






Application Dev

ex Machina, I Robot, Transcendent Man, Wall-E





Effectiveness of Social Media in Disaster Fundraising

Gamification for Health Activism

Shaping/Censoring Data

Social Justice & Math/Stat

Weapons of Math Destruction

Autonomous Agents/AI Rights


Elections (analysis & fraud detection)

ML Classic Exercises

  • Iris categorization – illustrate Data Science Flow
  • Visual Categorization









HIST680 Introduction to Digital Humanities – online course


Introduction to Computational Tools and Techniques for Social Research

Distance Literary Analysis

Graphs, Maps, Trees: Abstract Models for Literary History Paperback – September 17, 2007

Programming Languages:


Related Classes:

  • Math
    • Statistics
    • Linear Algebra
    • Pure Math/NumberTheory/Group/Set/Category/FirstOrderLogic/TypeTheory/Russle’sParadox
  • Programming
    • Python
    • JavaScript
    • C
    • FP
  • Philosophy
    • Logic
  • Design
  • Econ
  • LBIS/Center of Innov Pedgo






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