Mastering Data Visualization With Python

Visualize data using pandas, matplotlib and seaborn libraries for data analysis and data science

What you'll learn
Understand which plots are suitable for different types of data, ensuring you select the most effective visualization method for your analysis.
Visualize data by creating various graphs using the pandas, matplotlib, and seaborn libraries, enhancing your ability to communicate data insights.
Master data visualization techniques to draw meaningful knowledge from your data, making informed decisions based on clear visual representations.
Learn to create time-series line plots, bar plots, pie plots, histograms, density or KDE plots, and box-whisker plots using the pandas package.
Explore matplotlib library to create time-series line plots, bar plots, pie plots, histograms, density or KDE plots, box-whisker plots, and scatter plots.
Master the seaborn library to create relational plots (scatter and line plots), distribution plots, and categorical plots (strip, swarm, box, violin, point etc)
Customize your plots by creating themes based on style, context, color palette, and font to enhance the visual appeal and clarity of your visualizations.
Enhance your resume with advanced data visualization skills using Python, making you a competitive candidate in data science and analytics fields.

Requirements
Some basic knowledge of Python is expected. However this course does include a quick overview of Python knowledge required for this course.

Description
This 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 visualization,Professionals interested in creating publication quality plots,Professionals who are not happy with the plots created in MS Excel, and see them as dull and boring