Udemy Business

Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Mastering Data Visualization with Python

Name: Mastering Data Visualization with Python
Rating: 4.8 (365 reviews)

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

Created bySandeep Kumar Quality Gurus Inc., Abhin Chhabra

Last updated 9/2021

English

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.

Course content

6 sections • 74 lectures • 9h 27m total length

Study Plan - Please do NOT skip this3:13
Download Section 1 Resources0:02
Python Refresher - Part 115:28
Python Refresher - Part 29:51
Numpy Refresher8:54
Pandas Refresher11:02

Download Section 2 Resources0:02
Getting Data for Plotting - Part 14:36
Getting Data for Plotting - Part 210:29
Anatomy of a Figure4:09
First Plot Using Pandas5:30
Refining the First Plot9:56
Line Plot Continued5:03
Bar Plot5:21
Box Plot7:54
Histogram and KDE Plot5:06
Scatter Plot3:35
Explore the relationship between two continuous variables with a scatterplot, plotting lot size (X) against price (Y) to reveal an increasing but imperfect trend influenced by multiple factors.
Pie Plot4:33
Summary of Commonly Used Plots10:33
Download Section 3 Resources0:02

Download Section 3 Resources0:02
Line Plot Part 111:54
Explore line plotting with matplotlib using the Austres dataset to visualize Australia’s population over time, compare with pandas plots, and learn figure sizing, styling, and labeling.
Line Plot Part 26:07
Refine a matplotlib line plot by adding axis labels, a legend, and multiple lines, with grid options, alpha, and line style, and explore pandas-based plotting.
Bar Plot16:15
Explore bar plots with matplotlib to visualize bedroom counts, group by bedrooms, and create a pareto chart with a cumulative sum for insights in data visualization.
Box Plot7:19
Histogram4:50
Scatter Plot2:35
Explore scatter plots in Python by plotting price against lot size with Matplotlib, and see how Seaborn adds color for air conditioner and dot size for bedrooms to visualize variables.
Pie Plot4:17
Subplots approach - An Introduction6:27
The First Plot Using Subplots Approach6:15
Creating a Plot with Two Axes8:50
Arrow and Annotation on the Plot15:43
Bar Plot and Pie Plot10:52

Download Section 4 Resources0:02
Scatter Plot and Histogram10:33
Seaborn Library for Plotting - Introduction12:28
Types of Plots in Seaborn9:18
Scatter Plot using the Seaborn Library - Part 19:27
Scatter Plot using the Seaborn Library - Part 26:50
Explore creating Seaborn relplot scatterplots to compare houses with and without driveway. Learn to use hue, column, and row for facets, and size and color to encode bedrooms and stories.
Line Plot using the Seaborn Library11:11
Displot - Part 1 (Histogram, KDE, ECDF and Rug Plots)7:04
Displot - Part 2 (Histogram, KDE, ECDF and Rug Plots)9:51
Two Dimensional Displots6:05
Explore two dimensional histograms and kde plots to visualize price and lot size distributions, using color bars and heat map style density in seaborn displot.
Catplot - Introduction3:41
Strip Plot and Swarm Plot7:19
Explore strip plots and swarm plots to visualize price distributions with seaborn, comparing by bedrooms and driveway, and learn when to adjust aspect ratio for clarity.
Box Plot and Violin Plot9:33
Bar Plot and Point Plot9:20
Joint Plot (Scatter + Histogram)8:43
Pair Plot (Multiple Scatter + Histogram Plots)9:21
Regression or Linear Model Plot6:03
Explore linear model plots in Seaborn using lmplot to reveal relationships between sepal length and petal length, including per-species lines, and extend to joint and pair plots with regression lines.
Setting the Plot Styles10:22
Setting the Plot Context3:37
Set the plot context in Seaborn to tailor size and emphasis using paper, notebook, talk, and poster. Switch context with sns.set_context to suit PowerPoint slides.
Choosing an Appropriate Color Palette10:18
Setting the Plot Themes6:43

Download Section 5 Resources0:02
Installing Anaconda6:48
Jupyter Notebook8:12
Getting Started with Python13:14
Variables and Types6:10
List - Part 115:17
List - Part 26:00
Dictionary6:54
Tuple3:27
Set3:12
Logical Operators3:22
Numpy - Part 114:16
Numpy - Part 211:55
Numpy - Part 39:05
Pandas - Series and DataFrame11:05
Pandas DataFrame7:42
Importing .csv Files as DataFrame11:37
Pandas DataFrame - Dealing with Columns9:55
Select and view pandas dataframe columns, compute a volume from length, width, and height, round to two decimals, and drop or save columns using axis and inplace.
Pandas DataFrame - Dealing with Rows23:56

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 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.

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

Mastering Data Visualization with Python

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 49min

Getting Data and Using the Pandas Package to Plot14 lectures • 1hr 17min

Matplotlib Library for Plots13 lectures • 1hr 41min

Seaborn Library for Plots21 lectures • 2hr 48min

Python for Absolute Beginners19 lectures • 2hr 52min

Bonus Section1 lecture • 1min

Requirements

Description

Who this course is for: