The course bridges the gap between traditional social science research and contemporary AI capabilities, empowering students to gather, transform, and analyze textual data at scale - turning them into active investigators who can extract meaningful patterns from the digital noise that surrounds us.
Day 1: Welcome, intro, course scope, logistics; state of LLMs; hands on : data analysis on google colab
Day 2: LLMs, JSON and pandas
Day 3: APIs
How the Web works; APIs; The Wikipedia API; The NYT API, Duckduckgo API
Day 4: NLP with Spacy: NER, POS, topic modeling, text classification, tokens, stop words, lemmatization, ...; NYT API; more python;
Day 5: embeddings; LLMs; RAG; Spacy.io;
Day 6: deploy streamlit app on github
Day 7: from LLMs to Agents
Day 8:
Day 9: Data visualization and inference
day 10 machine learning, deep learning, reinforcement learning
day 11 buffer
day 12 projects presentation