The availability of data generated from sensors, mobile devices, social networks, and so on has grown continuously in recent years. Visualization is what one needs to put data to good use: it allows to analyze, explore and communicate possibly large and complex data in a meaningful way.
The first part of the course will deal with scientific visualization, which concerns the graphical illustration of scientific data (for example, biological data) to understand and glean insights into the underlying phenomena.
The second part of the course will introduce the fundamentals of information visualization.
Unlike scientific visualization, where data have an immediate physical representation, information visualization often deals with abstract data, which do not have a direct visual representation, like the network of people connections on a social network. We will learn to decide what to visualize, how to abstract and encode it using different graph types, and how to evaluate different solutions according to visual perception rules.

Fundamentals of scientific and data visualization. Visual perception. Best practices in data visualization. Visualization techniques for both scientific phenomena and abstract data.
Visualization libraries.

By the end of the course, the students will be able to
● illustrate and communicate data and results using visualization, also for complex and large datasets
● using existing libraries and software tools for visualization purposes (e.g. seaborn, D3.js).

Syllabus
1. Introduction: differences between scientific visualization, data visualization, interactive
visualization, visual analytics and infographics.
2. Scientific Visualization
a. 3D data visualization
b. Spatial data structures and Indexing
c. Flow visualization
d. Paraview tool
e. Topological Data Analysis for scientific visualization
f. TTK - Topology Toolkit
3. Data Visualization Pipeline
a. Data and attribute types
b. Data preprocessing
c. Graph and chart types
d. Encoding and decoding processes
e. Evaluation of visualizations
4. Visual Perception
a. Fundamentals
b. Gestalt laws
c. Preattentive processes
d. Color
5. Time Series
6. Animated Charts
7. Graph Drawing
a. Trees
b. Small graphs
c. Large graphs
8. Multi-dimensional Data Visualization
a. Multi-dimensional glyphs
b. Dimensionality reduction techniques
c. Ordering/sorting
d. Dataset summarization
9. Machine Learning and Data Visualization
10. Python for Data Science and Visualization
a. Intro, NumPy, Pandas
b. Python’s visualization ecosystem (Matplotlib, Plotly, ...)
11. D3.js