Art of Effective Visualization of Multi-dimensional Data
summary
Hands-on recipes for visualizing 1–6D structured data using Python (matplotlib, pandas, seaborn, plotnine) favor faceting over 3D and combine color, size, shape with binning to keep plots interpretable. For unstructured data the pipeline converts audio to spectrogram→RGB images, uses Keras VGG16 bottleneck features plus dense layers and achieves ~90% test accuracy on an UrbanSound8K 10-class subset. Word embeddings (Bible corpus) show semantic clusters and the notebooks with code are provided.
AI-written summary · about
chapters (23)
- 0:00Intro
- 4:13GET THE SLIDES AND CODE HERE
- 4:42Data Visualization & Storytelling
- 5:05What is Data Visualization
- 5:40Why Data Visualization?
- 8:08Motivation for effective data visualization
- 9:17Visualizing the Broad Street Cholera Outbreak
- 10:32Florence Nightingale's depiction of causes of death
- 11:53Effective Visualization with Grammar of Graphics
- 12:26Major Components of the Grammar of Graphics
- 14:24Start building with data, aesthetics, scale & geoms
- 15:32Visualize data, aesthetics with statistics
- 15:49Visualize up to 6-D with aesthetics, scale & facets
- 17:53Popular Data Visualization Tools & Frameworks
- 19:06Python Data Visualization Frameworks
- 20:39Basic Descriptive Statistics
- 21:07Univariate Analysis. Visualizing 1-D
- 22:44Multivariate Analysis - Visualizing 2-D
- 35:15Visualizing Text Data - Basic EDA
- 35:57Visualizing Text Data - Language Structure
- 37:20Visualizing Text Data - Language Semantics
- 43:53Visualizing Audio Data - Can you see what you hear?
- 49:46Faster Python with Intel Distribution for Python 2018
related talks
01h 38mEffective Data Visualization
02h 23mEffective Data Visualization
34mVisualising Multi Dimensional Data
03h 09mEffective Data Visualization
01h 28mPython Visualization for Exploration of Data
02h 31mTech Tutorial: Data visualization in Python
43mVisualising Machine Learning Models
43mAdvanced Data Visualizations In Jupyter Notebooks