amitkaps / data-vis-workshop

Data Visualisation Workshop

Data Visualisation Workshop

Notes and Slides

• Introduction: Overall introductions
• Representation: Walk though different chart guidelines
• Aesthetics: Include decoding guidelines, color, chart-junk
• Interaction: Overview on animation and interation
• Model Vis: Concepts on model visualisation
• Storytelling: Storytelling and link to data visualisation
• Dashboards: Concepts on structuring an interactive dashboard
• Tools & Resources: List of tools and resources for visualisation

In-Class Notebooks

• 01-Simple
• 02-Telco-Basic
• 03-Facets
• 04-Interactive
• 05-Chart-Serving

Additional Resources:

• Visualisation Libraries:
  • pyviz.org: Overview on all python libraries got data visualisation
  • Altair: Python library for Interactive Grammar of Graphics
  • Vega-lite: JS library for Interactive Grammar of Graphics
• Chart Guidelines e.g. Visual Vocabulary
  • FT Version: ft.com/vocabulary
  • Vega-lite Version: gramener.github.io/visual-vocabulary-vega/

Session Plan

Day 1

• Workshop Introduction
• Session #1: Value of Data Visualisation
• Session #2: Tools & Abstractions for Data Visualisation
• Session #3: Theory of Data Visualisation
• Session #4: Guidelines for Better Data Visualisation
• Day One Summary
• Data-Story Group Exercise

Day 2

• Recap & Questions
• Data-Story Presentations
• Session #5: Crafting Visual Stories with Data
• Data-Story Rework
• Session #6: Interactivity
• Session #7: Explorable Vis for Business Users
• Session #8: Putting together an Interactive Application
• Overall Summary & Way Forward

Detailed session plan can be read at session.md

Setup & Installation

System Requirements:

• You need a laptop for hands-on coding during the workshop, with the following minimum specifications:
  • 2 or more CPU Cores
  • 4GB or more RAM
  • 10 GB or more free disk space
  • Running Windows 7+, 64-bit macOS, or Linux

Step 1:

• For Python data science libraries, please install the latest Anaconda Python 3 Distribution (currently Python 3.7) for your OS: https://www.anaconda.com/distribution/
• Detailed install instruction & troubleshooting is available at http://docs.anaconda.com/anaconda/install/

Step 2:

• Please download using https://github.com/amitkaps/data-vis-workshop/archive/master.zip or clone the repo from: https://github.com/amitkaps/data-vis-workshop.git

Step 3:

• Once you have Anaconda installed (Step 1) and downloaded or cloned the repo (Step 3), you can use the following conda commands to install all the required packages.

Please note:

• Do cd into the folder and then run these commands.
• If you are on Windows, then please only use the Anaconda Prompt (and not command prompt)

conda env create --file vis.yaml
conda activate vis

python install_check.py
>>> All-Set! Packages installed.

Datasets

The list of datasets are available in the data folder and you can read about them at datasets.md