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Mastering Data Visualization with Python

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Mastering Data Visualization with Python, available at $64.99, has an average rating of 4.7, with 74 lectures, based on 247 reviews, and has 1910 subscribers.

You will learn about 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. This course is ideal for individuals who are Data Science, Six Sigma and other professionals interested in data visualization or Professionals interested in creating publication quality plots or Professionals who are not happy with the plots created in MS Excel, and see them as dull and boring It is particularly useful for Data Science, Six Sigma and other professionals interested in data visualization or Professionals interested in creating publication quality plots or Professionals who are not happy with the plots created in MS Excel, and see them as dull and boring.

Enroll now: Mastering Data Visualization with Python

Summary

Title: Mastering Data Visualization with Python

Price: $64.99

Average Rating: 4.7

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

  • 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

Target Audiences

  • 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

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 Plot

Single Discrete Variable:Bar Plot, Pie Plot

Single Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot 

Two Continuous Variable: Scatter Plot

Two Variable: One Continuous, One Discrete: Box-Whisker Plot

B. Types of graphs using Matplotlib library:

Time-series:Line Plot

Single Discrete Variable:Bar Plot, Pie Plot

Single Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot 

Two Continuous Variable: Scatter Plot

In 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 Plot

displot(Distribution Plots): Histogram, KDE, ECDF and Rug Plots

catplot (Categorical Plots): Strip Plot, Swarm Plot, Box Plot, Violin Plot, Point Plot and Bar plot

In addition to these three categories, we will cover these three special kinds of plots: Joint Plot, Pair Plot and Linear Model Plot

In the end, we will discuss the customization of plots by creating themes based on the style, context, colour palette and font.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Study Plan – Please do NOT skip this

Lecture 2: Download Section 1 Resources

Lecture 3: Python Refresher – Part 1

Lecture 4: Python Refresher – Part 2

Lecture 5: Numpy Refresher

Lecture 6: Pandas Refresher

Chapter 2: Getting Data and Using the Pandas Package to Plot

Lecture 1: Download Section 2 Resources

Lecture 2: Getting Data for Plotting – Part 1

Lecture 3: Getting Data for Plotting – Part 2

Lecture 4: Anatomy of a Figure

Lecture 5: First Plot Using Pandas

Lecture 6: Refining the First Plot

Lecture 7: Line Plot Continued

Lecture 8: Bar Plot

Lecture 9: Box Plot

Lecture 10: Histogram and KDE Plot

Lecture 11: Scatter Plot

Lecture 12: Pie Plot

Lecture 13: Summary of Commonly Used Plots

Lecture 14: Download Section 3 Resources

Chapter 3: Matplotlib Library for Plots

Lecture 1: Download Section 3 Resources

Lecture 2: Line Plot Part 1

Lecture 3: Line Plot Part 2

Lecture 4: Bar Plot

Lecture 5: Box Plot

Lecture 6: Histogram

Lecture 7: Scatter Plot

Lecture 8: Pie Plot

Lecture 9: Subplots approach – An Introduction

Lecture 10: The First Plot Using Subplots Approach

Lecture 11: Creating a Plot with Two Axes

Lecture 12: Arrow and Annotation on the Plot

Lecture 13: Bar Plot and Pie Plot

Chapter 4: Seaborn Library for Plots

Lecture 1: Download Section 4 Resources

Lecture 2: Scatter Plot and Histogram

Lecture 3: Seaborn Library for Plotting – Introduction

Lecture 4: Types of Plots in Seaborn

Lecture 5: Scatter Plot using the Seaborn Library – Part 1

Lecture 6: Scatter Plot using the Seaborn Library – Part 2

Lecture 7: Line Plot using the Seaborn Library

Lecture 8: Displot – Part 1 (Histogram, KDE, ECDF and Rug Plots)

Lecture 9: Displot – Part 2 (Histogram, KDE, ECDF and Rug Plots)

Lecture 10: Two Dimensional Displots

Lecture 11: Catplot – Introduction

Lecture 12: Strip Plot and Swarm Plot

Lecture 13: Box Plot and Violin Plot

Lecture 14: Bar Plot and Point Plot

Lecture 15: Joint Plot (Scatter + Histogram)

Lecture 16: Pair Plot (Multiple Scatter + Histogram Plots)

Lecture 17: Regression or Linear Model Plot

Lecture 18: Setting the Plot Styles

Lecture 19: Setting the Plot Context

Lecture 20: Choosing an Appropriate Color Palette

Lecture 21: Setting the Plot Themes

Chapter 5: Python for Absolute Beginners

Lecture 1: Download Section 5 Resources

Lecture 2: Installing Anaconda

Lecture 3: Jupyter Notebook

Lecture 4: Getting Started with Python

Lecture 5: Variables and Types

Lecture 6: List – Part 1

Lecture 7: List – Part 2

Lecture 8: Dictionary

Lecture 9: Tuple

Lecture 10: Set

Lecture 11: Logical Operators

Lecture 12: Numpy – Part 1

Lecture 13: Numpy – Part 2

Lecture 14: Numpy – Part 3

Lecture 15: Pandas – Series and DataFrame

Lecture 16: Pandas DataFrame

Lecture 17: Importing .csv Files as DataFrame

Lecture 18: Pandas DataFrame – Dealing with Columns

Lecture 19: Pandas DataFrame – Dealing with Rows

Chapter 6: Bonus Section

Lecture 1: BONUS LECTURE

Instructors

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  • 2 stars: 2 votes
  • 3 stars: 12 votes
  • 4 stars: 80 votes
  • 5 stars: 153 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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