2026-03-02

ACENET Online Digital Training Series for Humanities and Social Sciences

The following online training sessions may be of interest to faculty and students, and are free of charge. Registration is through our training portal — https://www.acenet.training/courses

Introduction to Python for Humanities and Social Sciences (HSS) (Parts I & II of Python Series for HSS)

3, 5 March, 1300-1600hrs Atlantic | 1330-1630hrs NL (online)

This is the first workshop of a beginner level four-part series for humanities and social sciences researchers (HSS) and librarians based on Library Carpentries lessons. We will use the Python programming language due to its vast popularity, easy syntax, and powerful extensions, while working in the user-friendly and convenient JupyterLab environment. Parts I and II focus on introducing participants to basic coding concepts and fundamentals to help them confidently participate in high-level conceptual discussions with computer programmers or technical team members. These general concepts will be reinforced and illustrated with the hands-on development of simple programs that can immediately help with text-based research and analysis. 

Text Analysis with Python Using TextBlob (Part III of Python Series for HSS)

10 March, 1300-1600hrs Atlantic | 1330-1630hrs NL (online)

Text Analysis using TextBlob enables participants to apply basic coding concepts to text-based analysis. We will use a Python library to import, analyze, explore, and manipulate textual datasets and learn about common natural language processing (NLP) techniques like n-grams and NLP tasks such as word tokenization, parsing, frequency detection, spelling correction, sentiment analysis, classification, and more to explore meaningful trends in language patterns.

Prerequisite: Introduction to Python & Coding for HSS Parts I and II, or equivalent knowledge

Modern Text Analysis with Python: From Conventional Language Models to LLMs (Part IV of Python Series for HSS)

12 March, 1300-1600hrs Atlantic | 1330-1630hrs NL (online)

Modern Text Analysis with Python explores the evolution of linguistic computation, moving beyond static rules of conventional natural language processing (NLP) techniques toward the era of Large Language Models (LLMs). Participants will navigate the shift from simple word representations to sophisticated context-aware embeddings, exploring practical applications of LLMs such as automated summarization, sentence completion, and advanced sentiment analysis using popular language models like GPT, BART and BERT. The session concludes with hands-on insights into state-of-the-art models like Gemini, Claude, and GPT-5, focusing on how to integrate these powerhouses into workflows via application programming interface (API).

Prerequisites: Parts I through III of the Introduction to Python & Coding for HSS series, or equivalent knowledge