General info

Learn to transform raw data into understanding, insight, and knowledge

Topics

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

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

3. 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

Teaching methodology

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

Resources

1. Web3: DAE Lab

2. the flow of data science: R for Data Science / Dear Data / Old Dear Data

3. network science: Networks / Networks, crowds and markets / 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

Packages

1. tidyverse: analyze and visualize tabular data

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

Install with:

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

Data sets

• Cambridge Bitcoin Electricity Consumption Index

• Electricity maps

• Kaggle

• Qualità della Vita - Il sole 24 Ore

• Stanford Network Analysis Platform

• The Colorado Index of Complex Networks

• Climate Change AI

• The Centre for AI & Climate

Exam

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

• 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 dataset used and the analysis done (code and prose), 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 an online presentation of at most 20 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 might be asked to reply to general questions about the topics of the course as well as to show and comment the code of the project

The final mark is determined as follows:

• 1/3 using the karma score accumulated by the student during the course according to the DAE teaching methodology

• 2/3 based on the teacher's judgement on the student's project, evaluating both the content and the presentation of the work