Data Visualization with Python

Learn matplotlib, numpy, line plot, bar plot, scatter plot, histogram, data set, and distribution

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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

Description

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