Exploratory Data Analysis with Seaborn.
Identify and interpret inherent quantitative relationships in datasets. Produce and customize various chart types with Seaborn in Python. Apply graphical techniques in exploratory data analysis (EDA).
Data Science, Machine Learning, Exploratory Data Analysis with Seaborn.
Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data.
SKILLS YOU WILL DEVELOP: Data Science, Machine Learning, Python Programming, Data Analysis, Data Visualization (DataViz)
Introduction and Importing Data, Separate Target from Features, Diagnosis Distribution Visualization, Visualizing Standardized Data with Seaborn, Violin Plots and Box Plots, Use Joint Plots for Feature Comparison, Observing Distributions and their Variance with Swarm Plots, Obtaining all Pairwise Correlations