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====== Visual Analytics (602AA)======

  * **Salvatore Rinzivillo** ([[rinzivillo@isti.cnr.it]])

==== Quick access links ====

  * MS Teams Channel: https://teams.microsoft.com/l/team/19%3AySCF-pb5z22vNq2oSroKWz-ejSxNTyzEG0egi-BPrHo1%40thread.tacv2/conversations?groupId=4094af8b-f2f0-4d2f-a38f-72e2a8adb25e&tenantId=c7456b31-a220-47f5-be52-473828670aa1
  * Telegram channel: https://t.me/+6PTkOzAWcWswMWI8
  * All source code of exercises are available at the URL: https://github.com/va602aa-master

===== Schedule =====
  * Wednesday, 11:00 - 13:00, Room M1
  * Friday, 11:00 - 13:00, Room M1

===== News =====
To keep updated with the last news of the course, subscribe at the Telegram channel: https://t.me/+6PTkOzAWcWswMWI8
   * :!: **March 21** Today the class will start at 11:30 (instead of the usual 11:15)   
   * **February 19** The start of the course has been postponed due to the teacher being affected by the flu. Lessons will commence on February 26th.

===== Exams =====
Students will be admitted to the exam after the registration on the website [[http://esami.unipi.it]]. 
The exam consists of a discussion of the project. It is mandatory to submit a short report (6-10 pages) within the deadline by mail to the instructor, specifying the tag "[VA]" in the subject.

Planned dates:
  * Please log on to the portal for registration to get the next dates


==== Project assignment ====

The final project should have the following requirements:
    * The application should contain several visual widgets, each providing insights on a selection of dimensions of the original data
    * It is possible to use state-of-the-art charts (bar charts, line charts, etc.) and libraries (plot.ly, vega, etc). 
    * The final evaluation will take into account the implementation of a novel, original visualization to present the data in a creative, non-trivial way, using D3.js (see examples on Vast Challenge 2008 developed in class). You can refer to visualization techniques already present in the literature, by adapting or implementing part of the solution.
    * Interactivity should be implemented, providing toolbars, selections, and filters for the data.
    * The visual widget should interact among them, realizing a set of linked displays to browse the data across multiple dimensions
    * The project should be submitted as a Git repository
    * The project report should be submitted 4 days before the discussion and should discuss at least the following points:
      * Description of data and presentation of the pattern or model to communicate
      * design choices: colors, interactions, shapes, transformations)
      * state-of-art: similar tools or interfaces for the same problem
      * Detailed description of the visualization with a description of the interaction
      * use case example for an analytical task

The student may choose one of the following project proposals. She/he can also propose an additional topic. In this case, a project proposal should be submitted for approval, containing a description of the data, a sketch of the possible visualization, and the motivation for the project.

=== Final Exam Project Assignment: VAST Challenge 2024 Datasets ===


**Objective:**  
For your final exam, you will engage with synthetic yet realistic datasets from the VAST Challenge 2024. Your task is to develop a visual analytics solution that addresses specific research questions posed in one of the mini-challenges (MiniChallenge 2 or MiniChallenge 3).

==== Overview: ====

The datasets for this project are synthetic representations of network graph structures, where entities are depicted as nodes and events or relationships among these entities as edges. Your goal is to analyze these data to uncover insights by answering the research questions provided in the selected MiniChallenge.

==== Assignment Details: ====

**Dataset Selection:**
   - Choose one of the following MiniChallenges:
     - [MiniChallenge 2](https://vast-challenge.github.io/2024/MC2.html)
     - [MiniChallenge 3](https://vast-challenge.github.io/2024/MC3.html)

**Data Understanding:**
   - Familiarize yourself with the synthetic dataset, understanding its structure, nodes (entities), and edges (relationships/events).
   - Identify key characteristics of the network, such as node types, edge types, and any metadata provided.

**Research Questions:**
   - Review the research questions posed by the selected MiniChallenge.
   - Your visual analytics solution should address all the questions posed by the challenge.

**Visual Analytics Solution:**
   - Design and implement a visual analytics system tailored to explore the data effectively in response to the chosen research question(s).
   - Utilize appropriate visualization techniques (e.g., graph layouts, node-link diagrams) to represent the network structure clearly.
   - Incorporate interactive elements to enable dynamic exploration of the dataset.
   - You can preprocess, transform, and prepare the given data in the format that is most appropriate for your visual design

**Analysis and Insights:**
   - Conduct an analysis using your visual analytics solution to address the selected research question(s).
   - Document any insights or patterns discovered during your exploration that relate directly to the research questions.





==== Next Exams ====
  * June 9th (submit your project and report by June 5th)
  * June 30th (submit your project and report by June 26th)
  * July 22th (submit your project and report by July 17th)

===== Textbooks =====
  * Visual Analytics for Data Scientists. Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel. Springer, 2020. ISBN: 978-3-030-56146-8 
  * [[http://www.vismaster.eu/news/mastering-the-information-age/|VisMaster - Mastering the information age]]
  * Design for Information. Isabel Meirelles, Rockport Publisher,2013.
  * Interactive Data Visualization for the Web, Scott Murray, O'Reilly Atlas, 2013
  * 
===== Useful Resources =====
  * Tools
    * [[http://d3js.org|D3 Javascript Library]]
    * [[https://vuejs.org/| Vue.js Framework]]
    * [[https://nodejs.org/| Node.js]]
  * Reading Material
    * [[http://www.slideshare.net/AmandaMakulec/data-visualization-resource-guide-september-2014|Data Visualization Resources (on Slideshare)]]
    * [[http://blog.hubspot.com/marketing/data-visualization-mistakes|Why Most People's Charts & Graphs Look Like Crap]]
    * [[https://infoactive.co/data-design/|Data + Design (crowdsourced book)]]
    * [[http://svgpocketguide.com/book/|Pocket Guide to Writing SVG]]
  * Inspiration
    * [[http://datavisualization.ch/]]
    * [[http://infosthetics.com/]]
    * [[http://www.informationisbeautiful.net/]]
    * [[http://visualoop.com/]]

  

{{ :magistraleinformaticaeconomia:va:2025:va_lesson16_storytelling.pdf |}}
===== Class Calendar =====

All exercises and code discussed during each lesson are available as a Git repository at:
https://github.com/va602aa-master

A collection of Observable Notebooks are available at https://observablehq.com/collection/@rinziv/va602aa

Recordings of lessons on Microsoft Teams are accessible within the channel of the course.

| ^ Day ^ Topic ^ Learning material ^
^ 01| 2025/02/26 | Intro: Visual Analytics Process; |{{ :magistraleinformaticaeconomia:va:2024:va_lesson1_introcourse.pdf |Slides}} ; VisMaster Book (Chapter 2) |
^ 02| 2025/02/28 | Vision and Cognition; |{{ :magistraleinformaticaeconomia:va:2025:va_lesson2_vision_perception.pdf | Slides}} |
^ 03| 2025/03/05 | Visual Variables; | {{ :magistraleinformaticaeconomia:va:2025:va_slide_03_visual-variables-slides.pdf | Slides}} |
^ 04| 2025/03/07 | Introduction to Python Altair Library | {{ :magistraleinformaticaeconomia:va:2025:va_slide_04_vegaaltair.pdf | Slides}}; {{https://altair-viz.github.io/index.html| website}} |
^ 05| 2025/03/12 | Color Models | {{ :magistraleinformaticaeconomia:va:2025:va_lesson5_colors.pdf | Slides}} |
^ | 2025/03/14 | No lesson | --  |
^ 06| 2025/03/19 | Toolbox: HTML, CSS, JS | {{ :magistraleinformaticaeconomia:va:2025:va_lesson6_html_css_js.pdf | Slides}} |
^ 07| 2025/03/21 | NPM, GIT and Vue.js | {{ :magistraleinformaticaeconomia:va:2025:va_lesson7_nodejs_npm_git.pdf | Slides}} |
^ | 2025/03/26 | No lesson | --  |
^ | 2025/03/28 | No lesson | --  |
^ 08| 2025/04/02 | Chart Taxonomy | {{ :magistraleinformaticaeconomia:va:2025:va_lesson8_charting_taxonomy.pdf | Slides}} |
^ 09| 2025/04/04 | Intro to D3.js |  |
^ | 2025/04/09 | No lesson | --  |
^ | 2025/04/11 | No lesson | --  |
^ 10| 2025/04/16 | Scale functions | {{ :magistraleinformaticaeconomia:va:2025:va_lesson10_scales.pdf | Slides}}; {{https://observablehq.com/@d3/introduction-to-d3s-scales | Notebook}}  |
^ 11| 2025/04/23 | Question and answering | --  |
^ | 2025/04/30 | Scale functions (cont.d) | {{ :magistraleinformaticaeconomia:va:2025:va_lesson10_scales.pdf | Slides}}; {{https://observablehq.com/@d3/quantile-quantize-and-threshold-scales | Notebook}}  |
^ 12| 2025/05/07 | Hierachical Data | {{ :magistraleinformaticaeconomia:va:2025:va_lesson12_hierarchies.pdf | Slides}}  |
^ 13| 2025/05/09 | Modular Programming in D3 and Javascript| {{https://observablehq.com/d/f9a177396c890d21| Notebook}} | 
^ 14| 2025/05/14 | Geographic Data | {{ :magistraleinformaticaeconomia:va:2025:va_lesson14_geography.pdf | Slides}}  |
^ 15| 2025/05/16 | Geography in D3  | {{https://observablehq.com/@rinziv/geographic-data| Notebook}}  |
^ 16| 2025/05/21 | Visual Storytelling | {{ :magistraleinformaticaeconomia:va:2025:va_lesson16_storytelling.pdf | Slides}}  |
^ 17| 2025/05/23 | VAST 2008 - Project  | {{https://github.com/VA602AA-master/VC2018 | GitHUB Repository}}  |
^ 18| 2025/05/28 | VAST 2008 - Project (cont.d) |  |
^ 19| 2025/05/30 | VAST 2008 - Project (cont.d) |  |
===== Previous Editions =====
  * [[magistraleinformaticaeconomia:va:Course2024]]
  * [[magistraleinformaticaeconomia:va:Course2023]]
  * [[magistraleinformaticaeconomia:va:Course2022]]
  * [[magistraleinformaticaeconomia:va:Course2021]]
  * [[magistraleinformaticaeconomia:va:Course2020]]
  * [[magistraleinformaticaeconomia:va:Course2019]]
  * [[magistraleinformaticaeconomia:va:Course2018]]
  * [[magistraleinformaticaeconomia:va:Course2017]]
  * [[magistraleinformaticaeconomia:va:Course2016]]
  * [[magistraleinformaticaeconomia:va:Course2015]]