This repository houses a full introductory course consisting of self-explanatory teaching modules in the Jupyter Notebook format. The course consists of four sections with sessions that will allow users to collect, preprocess, and analyze data with a minimum of coding skills, but gradually lead participants to acquire more skills in Python. These resources are provided as part of the Social ComQuant project. Notebooks are developed for the Anaconda distribution 2022.10 which can be downloaded here. For a complete guide how to set up your computing infrastructure and execute the course materials locally or in the cloud, please consult Session A1: Computing infrastructure.

Section A: Introduction

• Session 1: Computing infrastructure (Ready for testing)
• Session 2: Data management with Pandas (Ready for testing)
• Session 3: Scientific computing and data visualization (Ready for testing)

Section B: Data collection methods

• Session 1: API harvesting
• Session 2: Data parsing and static web scraping
• Session 3: Dynamic web scraping

Section C: Data preprocessing methods

• Session 1: Network construction and visualization (Ready for testing)
• Session 2: Multilayer and multimodal network construction (Ready for testing)
• Session 3: Natural Language Processing (Ready for testing)

Section D: Data analysis methods

• Session 1: Micro-level network analysis and community detection (Ready for testing)
• Session 2: Macro-level network analysis and network modeling (Ready for testing)
• Session 3: Unsupervised machine learning
• Session 4: Topic modeling (Ready for testing)
• Session 5: Supervised machine learning