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.
Learning Journey Context
This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.
Relevant for: Data Scientist, Data Analyst, Machine Learning Engineer.
💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.
Quick Facts
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.
Outcomes
- Course Overview : 2mins.
- Visualizing Relationships and Distributions in Seaborn : 54mins.
- Building Trellis Plots in Seaborn : 29mins.
- Controlling Plot Aesthetics and Style in Seaborn : 17mins.
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