Materials
Bulk download
As the goal is to compile a selection of materials into a course, the most convenient is to bulk download all the materials from the repository and select the blocks from the folder.
Alternatively, you can check the class notes directly, and download the materials of each block:
Introduction (I)
content: - Introduction to Critical Quant - Introduction to R
Introduction (II)
content: - Introduction to Critical Quant - Introduction to R - Version control and Documentation
Version Control
content: - GIT - Rmarkdown
Descriptive Statistics
content: - Probability and Statistics overview - Tests, correlations, confidence intervals and p-values
Linear Regression
content: - Simple and multiple linear regression - Model assumptions and coefficient interpretation
Logistic Regression
content: - logistic function - generalization: multinomial logistic regression - model assumptions
Shiny Apps
content: - server and UI - interactive visualizations with ggplotly - note: this should be tought after 8 (tidyverse) and 9 (data storytelling)
Tidyverse
content: - data wrangling - joins - Pivots - functions
Data Storytelling
content: - grammar of graphics - types of figures - multiple aesthetic attributes
Tidy-dataviz
content: - This material is a mix of 8 (tidyverse) and 9 (dataviz), for a fast-paced course
Web Scraping
content: - html - Extracting data - API
Preprocessing
“normalization imputation. Categorical, continous”
Supervised Learning
Non-parametric classification methods. KNN.
Parametric methods. Bayes theorem for classification
Unsupervised Learning
Clustering: K-means, hierarchical clustering
Evaluation
Train test overfitting metrics: precision, recall, F1
Text Mining
“regex text processing Quantitative representation of text”
Topic Modeling
Topic modeling, LDA, BERTOPIC
Embeddings
Word Embeddings Pre-trained language models BERT
Deep Learning
FCNN, Optimization, CNN
Social Network Analysis
Elements of a network, types of networks, Networks representation an metrics, social networks