Visualizing Statistical Data Using Seaborn
Data analysts and scientists are tasked with extracting information and insights from huge datasets. This course introduces the Seaborn Python library helping engineers communicate information via its high-level and powerful visualization tools.
What you'll learn
As deep learning approaches to machine learning rise in popularity, models are increasingly hard to understand and pick apart. Consequently, the need for sophisticated visualizations of the data going into the model is becoming more and more urgent and important. In this course, Visualizing Statistical Data Using Seaborn, you will work with Seaborn which has powerful libraries to visualize and explore your data. Seaborn works closely with the PyData stack - it is built on top of Matplotlib and integrated with NumPy, Pandas, Statsmodels, and other Python libraries for data science You will start off by visualizing univariate and bivariate distributions. You will get to build regression plots, KDE curves, and histograms to extract insights from data. Next, you will use Seaborn to visualize pairwise relationships of high dimensionality using the FacetGrid and PairGrid. Plot aesthetics, color, and style are important elements to making your visualizations memorable. Given this, you will study the color palettes available in Seaborn and see how you can manipulate specific plot elements in our graph. At the end of this course you will be very comfortable using Seaborn libraries to build powerful, interesting and vivid visualizations - an important precursor to using data in machine learning. Software required: Seaborn 0.8, Python 3.x.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Overview 2m
- Installing Seaborn and Exploring the Wine Dataset 4m
- Matplotlib and Seaborn 6m
- The Kernel Density Estimation (KDE) 5m
- Visualizing Univariate Distributions: Histograms, KDE Plots, Rugplots 6m
- Visualizing Bivariate Distributions: Jointplots, Hexbin Plots, KDE Plots 5m
- Pairwise Relationships Using Pairplot and Correlations Using Heatmap 6m
- Regression Plots Using Lmplot 5m
- Regression Plots Using Regplot 2m
- Stripplots and Swarmplots for Categorical Data 3m
- The Boxplot and the Violinplot 3m
- Statistical Estimation and Factorplots 6m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more