Mastering Data Visualization with Python
Learn how to visualize data using pandas, matplotlib, and seaborn libraries in this course. Suitable plots for different types of data are covered, including time-series, single discrete and continuous variables, and two continuous variables. The course also explores subplots and customization options for creating publication-quality plots. Ideal for data science, Six Sigma, and professionals seeking to create visually appealing and meaningful plots beyond MS Excel’s limitations.
Understand what plots are suitable for a type of data you haveVisualize data by creating various graphs using pandas, matplotlib and seaborn librariesThis course will help you draw meaningful knowledge from the data you have.Three systems of data visualization in R are covered in this course:A. Pandas B. Matplotlib C. Seaborn A. Types of graphs covered in the course using the pandas package:Time-series: Line PlotSingle Discrete Variable: Bar Plot, Pie PlotSingle Continuous Variable: Histogram, Density or KDE Plot, Box-Whisker Plot Two Continuous Variable: Scatter PlotTwo Variable: One Continuous, One Discrete: Box-Whisker PlotB. Types of graphs using Matplotlib library:Time-series: Line PlotSingle Discrete Variable: Bar Plot, Pie PlotSingle Continuous Variable: Histogram, Density or KDE Plot, Box-Whisker Plot Two Continuous Variable: Scatter PlotIn addition, we will cover subplots as well, where multiple axes can be plotted on a single figure.C. Types of graphs using Seaborn library:In this we will cover three broad categories of plots:relplot (Relational Plots): Scatter Plot and Line Plotdisplot (Distribution Plots): Histogram, KDE, ECDF and Rug Plotscatplot (Categorical Plots): Strip Plot, Swarm Plot, Box Plot, Violin Plot, Point Plot and Bar plotIn addition to these three categories, we will cover these three special kinds of plots: Joint Plot, Pair Plot and Linear Model PlotIn the end, we will discuss the customization of plots by creating themes based on the style, context, colour palette and font.Who this course is for:Data Science, Six Sigma and other professionals interested in data visualizationProfessionals interested in creating publication quality plotsProfessionals who are not happy with the plots created in MS Excel, and see them as dull and boring
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