Learning Python Data Visualization [Video]

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Learning Python Data Visualization [Video]

Benjamin Keller
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Learn to visualize data for real-world solutions using Python and its popular libraries for effective data analysis.
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Video Details

ISBN 139781785886102
Course Length3 hours and 9 minutes

Video Description

This video course is a beginner’s introduction to data visualization, and the techniques and libraries which can be leveraged with the Python language to achieve this. The end goal is to teach analysts and data scientists how they can visually represent complex sets of data using Python.
The video course introduces visualization concepts so viewers can analyze large and small sets of data using libraries such as Matplotlib, IPython, and so on.

This course primarily employs the IPython environment and matplotlib, with the following structure:

  • Introduce key data visualization libraries (matplotlib and so on.) and cover data importing/exporting (CSV, Excel, JSON and so on).
  • Introduce real-world data sets (to be visualized in the video).
  • Visualization types/techniques (bar chart, histogram, scatter plot, geospatial, and so on); demonstrate how to customize visualizations.
  • Introduce intermediate topics to create more advanced visualizations and using complex techniques, such as real-time data visualization.

By the end of the course, you will be able to demonstrate visualizations with interesting, real-world data sets.

The code bundle for this video course is available at- https://github.com/PacktPublishing/Learning-Python-Data-Visualization

Style and Approach

This course will push you to develop high-quality reliable reports using Python to help you derive solutions using minimal code and fewer bugs and align with business requirements. You may have previously worked on Python and learned the basic syntax, but this course focuses on visualizing data to create real-world solutions. With concise, clearly explained concepts, you’ll acquire the knowledge you really need from this course.

Table of Contents

Introduction to NumPy and Pandas
The Course Overview
Introduction to Python and Data Analysis
Installing Visualization Tools
Getting Started With NumPy
Using NumPy on Alcohol Consumption Data
Basics of the Pandas Library
Manipulating Data in Pandas
Exploring Data Visualization Platforms
Why Visualize Data?
Line Charts to Understand US Unemployment Data
Multiple Plots for US Unemployment Data
More on Multiple Plots
More Data Visualizations Methods
Investigating Fandango Scores with Bar Plots
Using Scatter Plots on Fandango Scores
Applying Histograms
Box Plots to Find Distributions and Medians
Storytelling through Data Visualization
Improving Plot Aesthetics
Using Color for Better Clarity
Using Layouts and Annotations
Conditional Plots and Geographical Data
Using the Seaborn Package
Regressions and Statistics with Seaborn
Comparisons with Conditional Plots
Introduction to Geographical Data
Geographical Data
Working with Latitudes and Longitudes
Introduction to Great Circles
Adding Extra Information to Maps

What You Will Learn

  • Why Python is used for data analysis and using important packages 
  • Data analysis using Pandas
  • Using different plots and how to apply these plots to different datasets 
  • Improving the visuals and making them look aesthetic
  • Working with the Geographical data and Seaborn Packages
  • Applying data visualization on large datasets

Authors

Table of Contents

Introduction to NumPy and Pandas
The Course Overview
Introduction to Python and Data Analysis
Installing Visualization Tools
Getting Started With NumPy
Using NumPy on Alcohol Consumption Data
Basics of the Pandas Library
Manipulating Data in Pandas
Exploring Data Visualization Platforms
Why Visualize Data?
Line Charts to Understand US Unemployment Data
Multiple Plots for US Unemployment Data
More on Multiple Plots
More Data Visualizations Methods
Investigating Fandango Scores with Bar Plots
Using Scatter Plots on Fandango Scores
Applying Histograms
Box Plots to Find Distributions and Medians
Storytelling through Data Visualization
Improving Plot Aesthetics
Using Color for Better Clarity
Using Layouts and Annotations
Conditional Plots and Geographical Data
Using the Seaborn Package
Regressions and Statistics with Seaborn
Comparisons with Conditional Plots
Introduction to Geographical Data
Geographical Data
Working with Latitudes and Longitudes
Introduction to Great Circles
Adding Extra Information to Maps

Video Details

ISBN 139781785886102
Course Length3 hours and 9 minutes
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