I will teach how to organize, transform, analyse, visualize and communicate data, as well as how to effectively communicate the outcomes of the workflow, with a strong focus on relational data.

The course will be multi-task (learn, make, use, watch, glance, read, dig, listen; see more below) and multi-teacher (I will be assisted by other real and virtual teachers). Some basics in programming, linear algebra and matrix theory, and statistics are desirable.

I will partially adopt reverse instructional design, an educational version of test-driven development for software.

## Play

1. Teasers
2. A hasty tour inside R
3. A not-so-short introduction to data analytics
4. Network science
5. Collaborate and communicate
6. Blockchain & IPFS

You will go through different tasks: learn, make, use, watch, glance, read, dig, listen. A legend is below:

• learn: I teach, you listen (and hopefully learn).
• make: I give you an assignment, you make it during the class. We discuss the solutions during the next class.
• use: you use a software: download, install and run it for the first time. I give you a brief practical introduction to it.
• watch: We watch a video together. By and large, the video acts as a teaser, introducing the next topic in an informal and attractive way.
• glance: You give a brief and fast look at something, generally an informative website. I steer you towards the most important sections.
• read: You read a story, typically at home. We discuss it together during the following class.
• dig: You read a theoretical deepening of the current topic, normally at home. We talk about it during one of the next classes.
• listen: The class is given by an invited speaker, an expert in the field.

## Books

• N10 Networks. Mark Newman. Oxford University Press, 2010.
• EK10 Networks, crowds and markets. David Easley and Jon Kleinberg. Cambridge University Press, 2010.
• WG17 R for Data Science. Hadley Wickham and Garrett Grolemund. O’Reilly. 2017.
• T11 R Cookbook. Paul Teetor. O’Reilly Media. 2011.

## E-learning

As an academic professional, I can sign my class up for an entire semester for free via DataCamp for the Classroom. This has some benefits:

1. you can learn by doing using DataCamp platform;
2. I can assign particular courses or chapters, and see who finished on time and who missed the deadline;

## Data challenges

Data challenges have 3 components:

• Input, which consists of:
1. a dataset of raw data. No data model is assumed. The data should be open so it can be freely distributed.
2. a set of data questions and challenges, formulated in natural language, whose answers might be (but not necessarily are) hidden behind the raw data. Questions should be sufficiently general and compelling to tease the attention and curiosity of scholars.
• Analysis notebook: a stream of analyses and visualizations aimed at approaching the given data questions and challenges. Ideally, the notebook is written in some popular, free language (like R or Python) and it is self-containing so that it can be easily distributed, executed and modified by other scholars. Issues like readability, conciseness, elegance, efficiency of the notebook are relevant, although not crucial.
• Output: these are the suggested answers to the given data questions and challenges. Answers might be partial (not definitive). The same question can be answered with different notebooks. A (modest) degree of subjectivity in the interpretation of the data answers is expected.

The following are examples of data challenges you are invited to try:

1. Which is the best team ever in Italian soccer? challenge
2. Is child mortality decreasing over time? challenge
3. What are the qualities of diamonds? challenge
4. In there a first-mover advantage in chess? challenge
5. Are female dolphins more social than male dolphins? (html, Rmd)
6. Which are the most powerful countries in the European natural gas market? (html, markdown)
7. Detect the most dangerous terrorists involved in Madrid train bombing attack of 2011 (html, markdown)
8. Discover the most interdisciplinary and autarchic disciplines in science (html, markdown)
9. Detect communities in a Karate club friendship network (html, markdown)
10. Attack the resilience of the Madrid train bombing terror network (html, markdown)
11. Are relationships among dolphins assortative by sex? And by degree? (html, markdown)

## Exam

The exam consists of a written exam and a project with oral presentation.

The written part consists of a list of questions, either open questions or exercises, over all the covered syllabus, for a duration of 90 minutes. During the written exam students are allowed to use only sheets covering the syntax of languages (such as dplyr cheatsheet).

The project consists of one significant data challenge chosen by the student. It is done individually and must use methods, languages and software tools seen during the course (not necessarily all, but most of them) in an integrated and fluent way. The student must deliver:

• A brief report in R Markdown format and its HTML rendering (do not render the code in the HTML version). The report should describe the dataset, the objectives, the analyzes and the results obtained.
• The used dataset

At least one week before the date of the oral exam load all material on GitHub and send to the teacher an e-mail with the link of the repository.

During the oral examination of the project, students will discuss, in a maximum time of 30 minutes, the project using a presentation on a personal laptop (bring adapters). The presentation is open to the public. Both the project and the presentation skills will be evaluated.

The final mark will be a weighted average of the written and project parts of the exam. The weight of the project is $$\varphi^{-1}$$, where $$\varphi$$ is the golden ratio. Passable marks are between 18 and 30. Excellent projects will be awarded with a praise bonus from 1 to 3 points to be summed to the result of the weighted average. The final mark is rounded to the closest integer.

The written and oral (project) parts of the exam can be done in different moments and in any order. The dates of the exams are set as per the academic calendar and will take place in Udine (typically in “Sala Riunioni” of Department of Mathematics, Computer Science and Physics). Enroll only for the last (second) part of the exam.