Dove e quando

A main challenge in data analysis is how to effectively present the results, in particular to people without knowledge of the preceding statistical analysis. A useful strategy is to produce interactive plots. Indeed, compared to static plots, interactive charts offer a great level of flexibility, in particular for data exploration (eg, data patterns in 3D). However, producing such charts is usually not trivial, requiring manual tweaking and knowledge of javascript. In this tutorial, I will introduce plotly.js through its Python API, with various examples dependently on the task at hand. Built on top of d3.js and stack.gl, plotly.js is a high-level, declarative, open-source charting library. The Python API let interactive plots be a part of the data analysis pipeline. Plotly offers easy and quick strategies to convert static plots to interactive ones, that can also be used directly in presentations. Finally, I will show an example of Dash, a Python framework for building analytics web apps. Short bio: Federico Tomasi is a PhD candidate in Computer Science at Università di Genova. He graduated cum laude in 2015 with a thesis developed during a six-month internship at the University of Sheffield, UK. He is part of the Biomedical Data Science group at DIBRIS. His research topics involve graphical models for structure inference in case of multivariate time-series data under the influence of latent factors.

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