This article show the features helping student learn and visualize data and model results in Orange using Breast Cancer dataset.
Dataset used here is taken from UCI Machine Learning repository, attributes are categorical and predicts weather the patient shall have recurrence or cancer or not.
For Starters, its easy to see the distribution of each column using distributions under visualization.
Easy to see scatter plot-
Decision Boundary-
Clustering, t-SNE and DBSCAN.
Complete quick and dirty workflow is as follows-

Explores a orderbook sample using Dtale and Autoviz.
So, I recently got an email from Kaggle that there is a new challenge uploaded from Optiver for Volatility prediction. Let’s try to use auto EDA tools to explore a stock.
Using Dtale-
There is describe option which shows the details of each column-

Histogram-
Quick Dashboard using Tableau
Data Source — http://data.uis.unesco.org/
A simple time-lapse on map-

A simple Rank chart of top 5 countries-