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Interactive Data Visualization with Python and Bokeh

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Interactive Data Visualization with Python and Bokeh, available at $89.99, has an average rating of 4.81, with 85 lectures, 1 quizzes, based on 1475 reviews, and has 11518 subscribers.

You will learn about Build advanced data visualization web apps using the Python Bokeh library. Create interactive modern web plots that represent your data impressively. Create widgets that let users interact with your plots. Learn all the available Bokeh styling features. Integrate and visualize data from Pandas DataFrames. Create dynamic graphs that plot real-time data. Plot time-series data. Integrate your data visualization apps with Flask apps. Deploy the apps in live servers. Learn how to troubleshoot Bokeh apps. This course is ideal for individuals who are Anyone involved in the data industry or Anyone who is already familiar with Python basics It is particularly useful for Anyone involved in the data industry or Anyone who is already familiar with Python basics.

Enroll now: Interactive Data Visualization with Python and Bokeh

Summary

Title: Interactive Data Visualization with Python and Bokeh

Price: $89.99

Average Rating: 4.81

Number of Lectures: 85

Number of Quizzes: 1

Number of Published Lectures: 78

Number of Published Quizzes: 1

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 79

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

  • Build advanced data visualization web apps using the Python Bokeh library.
  • Create interactive modern web plots that represent your data impressively.
  • Create widgets that let users interact with your plots.
  • Learn all the available Bokeh styling features.
  • Integrate and visualize data from Pandas DataFrames.
  • Create dynamic graphs that plot real-time data.
  • Plot time-series data.
  • Integrate your data visualization apps with Flask apps.
  • Deploy the apps in live servers.
  • Learn how to troubleshoot Bokeh apps.

Who Should Attend

  • Anyone involved in the data industry
  • Anyone who is already familiar with Python basics

Target Audiences

  • Anyone involved in the data industry
  • Anyone who is already familiar with Python basics

If you love Python and want to impress your clients or your employer with impressive data visualization on the browser, Bokeh is the way to go. This course is a complete guide to mastering Bokeh, a Python library for building advanced and data dashboards containing beautiful interactive visualizations. The course will guide you step by step from plotting simple datasets to building rich and beautiful data visualization web apps that plot data in real-time and allow web users to interact and change the behavior of your plots via the internet from their browsers. 

Whether you are a data analyst, data scientist, statistician, or any other specialist who deals with data regularly, this course is perfect for you. It will give you the skills to visualize data in a way that excites your audience and eventually sells your product or your idea much easier. All you need to have to learn Bokeh is some basic prior knowledge of Python.

The course also contains exercises to help you check your skills as you progress. You will be given access to various data samples and provided with additional examples to enforce your Bokeh skills. The course is estimated to take you around four weeks to complete assuming you devote 10-20 hours/week depending on your productivity skills.

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Course Introduction

Lecture 2: Installation

Lecture 3: Getting Help

Lecture 4: What is Bokeh

Lecture 5: Creating Your First Bokeh Plot

Lecture 6: Exercise 1: Plotting triangles and circle glyphs

Lecture 7: Exercise 1: Solution

Lecture 8: Using Bokeh with Pandas

Lecture 9: Exercise 2: Plotting Education Data

Lecture 10: Exercise 2: Solution

Lecture 11: Bug with the Show Method

Lecture 12: Using the Bokeh Documentation

Chapter 2: Customizing Bokeh Graphs

Lecture 1: Section Introduction

Lecture 2: Note

Lecture 3: Creating an Initial Plot

Lecture 4: Figure Background

Lecture 5: List of Colors

Lecture 6: Title

Lecture 7: List of Text Fonts

Lecture 8: Axes: Custom Styling

Lecture 9: Axes: Custom Geometry

Lecture 10: Axes: Categorical Data

Lecture 11: Grid

Lecture 12: Tools

Lecture 13: Glyphs

Lecture 14: Legend: Configuring

Lecture 15: Legend: Styling

Lecture 16: Popup Windows

Lecture 17: Exercise 3: Summary of Section 3

Lecture 18: Exercise 3: Solution

Chapter 3: Advanced Plotting

Lecture 1: Section Introduction

Lecture 2: ColumnDataSource

Lecture 3: Exercise 4: Plotting Elements of the Periodic Table

Lecture 4: Exercise 4: Solution

Lecture 5: Popup Windows with Custom HTML

Lecture 6: Gridplots

Lecture 7: Exercise 5: Gridplots

Lecture 8: Exercise 5: Solution

Lecture 9: Annotations: Spans and Boxes

Lecture 10: Exercise 6: Span Annotations

Lecture 11: Exercise 6: Solution

Lecture 12: Annotations: Labels and LabelSets

Lecture 13: Exercise 7: Labels in Spans

Lecture 14: Exercise 7: Solution

Chapter 4: Bokeh Server: Interactive Plotting with HTML Widgets

Lecture 1: Section Introduction

Lecture 2: Widgets in Static Bokeh Graphs

Lecture 3: Widgets in Interactive Bokeh Server Apps

Lecture 4: Select Widgets: Changing Labels Dynamically

Lecture 5: Exercise 8: Select Widgets – Drawing Spans Dynamically

Lecture 6: Exercise 8: Tips

Lecture 7: Exercise 8: Solution

Lecture 8: RadioButtonGroup Widgets: Changing Labels Dynamically

Lecture 9: Slider Widgets: Filtering Glyphs, Part 1

Lecture 10: Slider Widgets: Filtering Glyphs, Part 2

Chapter 5: Bokeh Server: Streaming Real Time Data

Lecture 1: Section Introduction

Lecture 2: Streaming Random Points and Lines

Lecture 3: Streaming Financial Data – Designing the App

Lecture 4: Streaming Financial Data – Webscraping

Lecture 5: Streaming Financial Data – Plotting

Lecture 6: Streaming Timeseries Data

Lecture 7: User Interaction Between Real-Time Plots and Widgets

Lecture 8: Example: Visualizing Spinning Planets

Chapter 6: Embedding Bokeh Plots in Websites

Lecture 1: Introduction to Flask

Lecture 2: Embedding Static Bokeh Plots in Flask

Lecture 3: Embedding Bokeh Server Plots in Flask

Lecture 4: Embedding Static Bokeh Plots in Django: Setting up a Django App

Lecture 5: Embedding Static Bokeh Plots in Django: Embedding the Plot

Chapter 7: Deploying Bokeh Data Visualization Apps in Live Servers

Lecture 1: Deployment Options

Lecture 2: Deploying Static Bokeh Plots

Lecture 3: Deploying Interactive Bokeh Server Apps Embedded in Flask- Setting up the VPS

Lecture 4: Deploying Interactive Bokeh Server Apps Embedded in Flask – Installing Software

Lecture 5: Deploying Interactive Bokeh Server Apps Embedded in Flask – Configuration Files

Lecture 6: Deploying Interactive Bokeh Server Apps Embedded in Flask – Uploading Files

Lecture 7: Deploying Interactive Bokeh Server Apps Embedded in Flask – Editing Server Files

Lecture 8: Deploying Interactive Bokeh Server Apps Embedded in Flask – Starting the Service

Lecture 9: Deploying Interactive Bokeh Server Apps Embedded in Flask – Troubleshooting

Lecture 10: Deploying Interactive Bokeh Server Apps as Standalone

Lecture 11: Bonus Lecture

Instructors

  • Ardit Sulce
    Ardit Sulce
    Python Programmer | Author at PythonHow | 10 Years on Udemy

Rating Distribution

  • 1 stars: 17 votes
  • 2 stars: 43 votes
  • 3 stars: 185 votes
  • 4 stars: 556 votes
  • 5 stars: 674 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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