Data Visualization

LEARNING OUTCOME

– How to create a simple web page

– How to convert a collection of data points into rendered objects using modern web standards

– How to build responsive interactive charts using Python

CONTENT

While we live in the era of Data, we humans are still visual animals. Being able to build a proper visualization is your key to extracting insights from data as well as communicating it to decision makers. From data exploration, all the way to analysis reporting, your data visualization skills are indispensable for succeeding as a data scientist. 

This course will focus on using the web browser as the perfect platform both for sharing your visualization and making them interactive. The first part is dedicated to D3.js, the famous javascript library for data-driven visualizations, and in the second part, you will learn how to build high level interactive charts for your web documents using the Python bindings of Plotly and Bokeh. 

The course is self-contained although previous knowledge of scripting languages like Javascript and Python are helpful.

D3: interactive visualisations in the browser

– D3.js History and scope- WEB document and the D.O.M.- Javascript basics

– Selections

– SVG and CANVAS

– Loading and Binding Data

– Mouse events and transitions

– Layouts and scales

Plotly and Bokeh: High level libraries for data visualisation. Write Python, get HTML. Plotly:

– Plotly object model

– Figure, data and layout

– Online and offline mode

– Cufflinks: bind plotly directly to pandas dataframes

– Custom widgets

Bokeh:

– Bokeh glyphs

– Manipulating plots

– Charts, plotting, models

– Styling and interactions

– Bokeh server