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.
Course info
Rating
(11)
Level
Beginner
Updated
Jun 7, 2018
Duration
1h 44m
Table of contents
Description
Course info
Rating
(11)
Level
Beginner
Updated
Jun 7, 2018
Duration
1h 44m
Description

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.

About the author
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi! My name is Janani Ravi, and welcome to this course on Visualizing Statistical Data Using Seaborn. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own stuff at Loonycorn, a studio for high-quality video content. In this course, 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 and StatsModels and other Python libraries used for data science. We start this course off by visualizing univariate and bivariate distributions. We'll build regression plots, KDE curves, and histograms to extract insights from data. We'll also study how we can detect correlations in our data using heat maps. We'll then use Seaborn to visualize pairwise relationships of high dimensionality using the facet grid and the pair grid. These are examples of trellis plots which are a precursor to building ML models. And we'll explore a bike sharing dataset which allows us to make decisions about the kind of machine learning model we want to build. Plots to fix color and style are important elements to making your visualizations memorable. We'll study the color palettes available in Seaborn and see how we can manipulate specific plot elements in our graphs. At the end of this course, you'll be very comfortable using Seaborn libraries to build powerful, interesting, and vivid visualizations, an important precursor to using data in machine learning.