DH Computational Research Methodologies and Technologies

Lecturer: Augustine Farinola

Course Outline

The course encompasses five comprehensive modules, carefully crafted to cater to the diverse research needs of participants, considering their disciplines, research subjects, and preliminary computational backgrounds.

A. DH Computational Methodologies

• Word_Cloud_from_Corpus.ipynb
• Dependency_Parsing_.ipynb
• Language_Detection_and_Identification.ipynb
• Machine_Translation.ipynb
• Sentiment_Analysis.ipynb
• Text_Classification.ipynb
• Text_Cleaning.ipynb
• Text_Clustering.ipynb
• Text_Generation.ipynb
• Text_Summarization.ipynb
• Topic_Modeling.ipynb
• Coreference_Resolution.ipynb
• Word_Embeddings.ipynb
• Named_Entity_Recognition.ipynb
• POS_Tagging_.ipynb
• SVO_Analysis.ipynb

B. Python for Data Analysis

• Understanding DH Data
• Linux Command and Git Control
• python_basics_1.ipynb
• python_advanced_2.ipynb
• python_conditional_questions.ipynb
• python_practice_questions_Loop.ipynb

C. DH Digital Tools and Technologies

• Voyant Tools
• Web Scraping
• Scripting: HTML, CSS,TEI, and XML
• Nvivo
• Docanno
• QGis
• Open Refine
• Tableau
• Recogito
• Web Archiving: WordPress & Drupal

D. DH Labs, Publications and Research

• Basic Introduction to DH and NLP
• Introduction_to_Python.ipynb
• NLP_Techniques.ipynb
• Voyant Tools for Textual Interpretation

E. DH and Artificial Intelligence

1. NLP Applications:

• Machine Translation (e.g., Google Translate)
• Sentiment Analysis (e.g., determining sentiment of text)
• Chatbots (e.g., ChatGPT, Bard, Claude)
• Speech Recognition (e.g., Siri, Alexa)
• Information Retrieval (e.g., search engines)

2. Machine Learning Techniques:

• Word Embeddings: Neural networks (Word2Vec, FastText)
• Language Modeling: RNNs, LSTM, Transformers (GPT, BERT)
• Sentiment Analysis: SVMs, Naive Bayes, Neural Networks, Deep Learning
• Named Entity Recognition: CRFs, RNNs, Transformers
• Machine Translation: Seq-to-seq models using RNNs, Transformer architecture (BERT, GPT)
• Speech Recognition: DNNs, CNNs for audio features, RNNs for sequencing
• Coreference Resolution: Decision Trees, Clustering, Deep Learning
• Text Summarization: Seq-to-seq models, Attention mechanisms, Transformers
• Topic Modeling: LDA, NMF

Practicals

Instructor's Demonstrations:

• Using Wole Soyinka Corpus: Corpus_of_African_Literature.ipynb
• Using Amos Tutuola's Corpus: Word_Frequency_Trend.ipynb

Quizzes

Programming

• data_structures_quiz_.ipynb
• Variables_&_Types_coding_challenges.ipynb

Maths

Logic