Data Visualization with Python

Data Visualization with Python, available at $19.99, with 41 lectures, and has 280 subscribers.

You will learn about What is Data Visualization Plot Style Simple Plot Types of Plot Multiple Plots Line Plot Scattering in Matplotlib Labeling Plots Scatter Plots Matplotlib Glitches Colors in Scattering Plot Vs Scatter Plot Bar Plotting Multiple Bar Plot Stacked and Sub Plots Histogram Plot Data Set Data Distribution Subplot This course is ideal for individuals who are For those who is interested in data visualization It is particularly useful for For those who is interested in data visualization.

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Summary

Title: Data Visualization with Python

Price: $19.99

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

What is Data Visualization

Plot Style

Simple Plot

Types of Plot

Multiple Plots

Line Plot

Scattering in Matplotlib

Labeling Plots

Scatter Plots

Matplotlib Glitches

Colors in Scattering

Plot Vs Scatter Plot

Bar Plotting

Multiple Bar Plot

Stacked and Sub Plots

Histogram Plot

Data Set

Data Distribution

Subplot

Who Should Attend

For those who is interested in data visualization

Target Audiences

For those who is interested in data visualization

Data visualization is a crucial part of data analysis that helps communicate insights and findings effectively. Python is a popular programming language for data visualization because of its extensive libraries, making it a popular choice among data scientists, researchers, and analysts. This course on Data Visualization with Python will provide an in-depth understanding of different visualization techniques and tools available in Python.

The course will begin with an introduction to data visualization and its importance in data analysis. The course will then move on to cover the basics of Python programming, which will include data types, variables, loops, conditional statements, functions, and modules. Participants who are already familiar with Python programming can skip this section.

The course will then focus on the different libraries available for data visualization in Python. The first library that will be covered is Matplotlib, which is a widely used library for creating static visualizations in Python. Participants will learn how to create different types of plots, including line charts, bar charts, scatter plots, histograms, and heat maps. The course will also cover customization options in Matplotlib, such as controlling the font size, colors, and axis labels.

Next, the course will cover Seaborn, a library built on top of Matplotlib that provides a higher-level interface for creating statistical visualizations. Participants will learn how to create complex visualizations such as distribution plots, categorical plots, and regression plots. Seaborn provides a variety of color palettes, making it easy to customize the visualizations. The course will also cover the built-in datasets in Seaborn, which makes it easy to create sample visualizations quickly.

The course will then move on to Plotly, a library that allows the creation of interactive visualizations in Python. Participants will learn how to create a wide range of interactive charts, including line charts, scatter plots, and 3D surface plots. Plotly is a cloud-based service that allows users to share and collaborate on visualizations. The course will also cover customization options in Plotly and creating custom dashboards, making it easy to explore and visualize data.

Bokeh will also be covered in the course, which is another Python library that allows the creation of interactive visualizations. Participants will learn how to create interactive data applications and dashboards. Bokeh has a wide range of visualizations, including line charts, scatter plots and heat maps. Bokeh provides a range of customization options, including color palettes, font styles, and axes formatting. The course will also cover handling large datasets and streaming data in Bokeh.

The course will also cover geospatial visualization using Geopandas, a library that allows users to work with geospatial data in Python. Participants will learn how to create maps and visualize spatial data. The library provides a variety of plots, including choropleth maps, point maps, and line maps.

In addition to the libraries mentioned above, the course will also cover Altair, ggplot, and Plotnine. Altair is a declarative library for creating visualizations, which means that users specify the data and the chart type, and the library generates the visualization automatically. ggplot is a library that is inspired by the R ggplot2 library and allows users to create complex visualizations easily. Plotnine is a library that is based on ggplot and provides a Pythonic interface for creating visualizations.

The course will also cover best practices for data visualization, including choosing the right chart type for the data, labeling the axes, and adding titles, and legends. Participants will also learn how to design effective data visualizations by using color schemes, typography, and layout.

The course will include hands-on exercises and projects, where participants will work on real-world datasets and create visualizations using different libraries in Python. Participants will also learn how to present their findings and insights effectively using visualizations.

AD Chauhdry

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Data Visualization

Lecture 2: Plot Style

Chapter 2: Simple Plot

Lecture 1: Types of Plot

Lecture 2: Matplotlib inline

Lecture 3: Plotting a Figure

Lecture 4: Sin Plot

Lecture 5: Multiple Plots of Sin and Cos

Chapter 3: Line Plot

Lecture 1: Styling Line Plot

Lecture 2: Change Type of Line

Chapter 4: Scattering and Labelling in Matplotlib

Lecture 1: Labeling Plots

Lecture 2: Labeling Plots in Python

Lecture 3: Legends Plotting

Lecture 4: Matplotlib Gotches

Lecture 5: Matplotlib Glotches in Python

Lecture 6: Scatter Plots

Lecture 7: Scattering of Sin Plot

Chapter 5: Colors Scheme in Scattering and Line Plot

Lecture 1: Colors in Scattering

Lecture 2: Scatter and Line Plot

Lecture 3: Customization of Scatter Plot

Lecture 4: Colors Scheme of Line and Scatter Plots

Lecture 5: Example

Lecture 6: Plot Vs Scatter Plot

Chapter 6: Bar Plotting

Lecture 1: Bar Plotting

Lecture 2: List Data

Lecture 3: Labeling of Bar Plot

Lecture 4: Multiple Bar Plot

Lecture 5: Multiple Bar Plot in Python

Lecture 6: Plot by Bar Plot

Lecture 7: Adding Labels in Bar Plot

Chapter 7: Stacked and Sub Plots

Lecture 1: Multiple Line Plots

Lecture 2: Stacked Bar Plot

Lecture 3: Stacked Bar Plot in Python

Lecture 4: Sub Plots

Lecture 5: Labeling Stacked Plots

Chapter 8: Histogram in Matplotlib

Lecture 1: Histogram Plot

Lecture 2: Histogram in Matplotlib

Lecture 3: Legend Histogram

Lecture 4: Data Set

Lecture 5: Data Distribution

Lecture 6: Subplot of Histogram

Lecture 7: Color Scheme in Histogram

Instructors

AD Chauhdry
Researcher, Mathematician, and Data Scientist

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