General info

Topics

1. Web3: blockchain, cryptocurrencies, tokens, decentralized organizations, decentralized education, and more

2. Generative art: old and new forms of generative art

3. The flow of data science: a brief intro/recap of R and the tidyverse approach to data science

4. Network science: centrality and power, similarity and heterogeneity, community detection and clustering, connectivity and resilience, small world, scale-free networks, epidemics on networks, and more

5. Text mining: sentiment analysis, word and document frequencies, n-grams [Not included in the course edition 24/25]

Teaching methodology

We will experiment with DAE - Decentralized Autonomous Education. DAE is an ongoing educational project adopting the philosophy and the applications of Web3 and AI to propose a novel learning model.

Resources

1. Web3

2. Generative art

3. The flow of data science: R for Data Science / Dear Data / Old Dear Data

4. Network science: Networks / Networks, crowds and markets / Dear Data

5. Text mining: Text mining with R / Dear Data

Software

• R - a free software environment for statistical computing and graphics

• RStudio - an integrated development environment (IDE) for R (and Python)

• Processing and p5.js

• ChatGPT

R packages

1. tidyverse: analyze and visualize tabular data

2. igraph, tidygraph, and ggraph: analyze and visualize network data

3. tidytext: text mining

Install with:

install.packages(c("tidyverse", "igraph", "tidygraph", "ggraph", "tidytext"))

Data sets

• Cambridge Bitcoin Electricity Consumption Index

• Electricity maps

• Kaggle

• Our World In Data

• Stanford Network Analysis Platform

• The Colorado Index of Complex Networks

• Dune Analytics

Exam

To pass the exam the student needs to complete an individual project of data science and discuss it orally:

• the project is done individually and uses the methods and languages learnt during the course

• the project analyzes a meaningful case study chosen by the candidate

• the project, i.e., the used dataset and the analysis done (code and prose) in R Markdown format, is archived on a GitHub repository created by the candidate. The repository link must be emailed to the lecturer at least 48 hours before the exam date

• the project is discussed orally on the day of the exam using a presentation of at most 30 minutes. See the presentation as the tip of the iceberg of your work (in particular, do not add code to the presentation)

• during the presentation, the student will be asked to show and comment the code of the project

• the student is also asked to answer two randomly choosen pre-given general questions about the topics of the course