The Data Visualization Workshop: A self-paced, practical approach to transforming your complex data into compelling, captivating graphics

Döbler, Mario, Großmann, Tim

  • 出版商: Packt Publishing
  • 出版日期: 2020-07-27
  • 售價: $1,610
  • 貴賓價: 9.5$1,530
  • 語言: 英文
  • 頁數: 536
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800568843
  • ISBN-13: 9781800568846
  • 下單後立即進貨 (約3~4週)


Explore a modern approach to visualizing data with Python and transform large real-world datasets into expressive visual graphics using this beginner-friendly workshop

Key Features

  • Discover the essential tools and methods of data visualization
  • Learn to use standard Python plotting libraries such as Matplotlib and Seaborn
  • Gain insights into the visualization techniques of big companies

Book Description

Do you want to transform data into captivating images? Do you want to make it easy for your audience to process and understand the patterns, trends, and relationships hidden within your data?

The Data Visualization Workshop will guide you through the world of data visualization and help you to unlock simple secrets for transforming data into meaningful visuals with the help of exciting exercises and activities.

Starting with an introduction to data visualization, this book shows you how to first prepare raw data for visualization using NumPy and pandas operations. As you progress, you'll use plotting techniques, such as comparison and distribution, to identify relationships and similarities between datasets. You'll then work through practical exercises to simplify the process of creating visualizations using Python plotting libraries such as Matplotlib and Seaborn. If you've ever wondered how popular companies like Uber and Airbnb use geoplotlib for geographical visualizations, this book has got you covered, helping you analyze and understand the process effectively. Finally, you'll use the Bokeh library to create dynamic visualizations that can be integrated into any web page.

By the end of this workshop, you'll have learned how to present engaging mission-critical insights by creating impactful visualizations with real-world data.

What you will learn

  • Understand the importance of data visualization in data science
  • Implement NumPy and pandas operations on real-life datasets
  • Create captivating data visualizations using plotting libraries
  • Use advanced techniques to plot geospatial data on a map
  • Integrate interactive visualizations to a webpage
  • Visualize stock prices with Bokeh and analyze Airbnb data with Matplotlib

Who this book is for

The Data Visualization Workshop is for beginners who want to learn data visualization, as well as developers and data scientists who are looking to enrich their practical data science skills. Prior knowledge of data analytics, data science, and visualization is not mandatory. Knowledge of Python basics and high-school-level math will help you grasp the concepts covered in this data visualization book more quickly and effectively.


Mario Döbler is a Ph.D. student with a focus on deep learning at the University of Stuttgart. He previously interned at the Bosch Center for artificial intelligence in the Silicon Valley in the field of deep learning. He used state-of-the-art algorithms to develop cutting-edge products. In his master thesis, he dedicated himself to applying deep learning to medical data to drive medical applications.

Tim Großmann is a computer scientist with interest in diverse topics, ranging from AI and IoT to Security. He previously worked in the field of big data engineering at the Bosch Center for Artificial Intelligence in Silicon Valley. In addition to that, he worked on an Eclipse project for IoT device abstractions in Singapore. He's highly involved in several open-source projects and actively speaks at tech meetups and conferences about his projects and experiences.


  1. The Importance of Data Visualization and Data Exploration
  2. All You Need to Know about Plots
  3. A Deep Dive into Matplotlib
  4. Simplifying Visualizations Using Seaborn
  5. Plotting Geospatial Data
  6. Making Things Interactive with Bokeh
  7. Combining What We Have Learned