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Description

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.
Who this course is for:
Anyone involved in the data industry
Anyone who is already familiar with Python basics

What you'll 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.

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Getting Started
13
1.1-Course Introduction
1.2-Installation
1.3-Getting Help
1.4-What is Bokeh
1.5-Bokeh and Bokeh Server
1.6-Creating Your First Bokeh Plot
1.7-Exercise 1: Plotting triangles and circle glyphs
1.8-Exercise 1: Solution
1.9-Using Bokeh with Pandas
1.10-Exercise 2: Plotting Education Data
1.11-Exercise 2: Solution
1.12-Bug with the Show Method
1.13-Using the Bokeh Documentation
2-Customizing Bokeh Graphs
18
2.1-Section Introduction
2.2-Note
2.3-Creating an Initial Plot
2.4-Figure Background
2.5-List of Colors
2.6-Title
2.7-List of Text Fonts
2.8-Axes: Custom Styling
2.9-Axes: Custom Geometry
2.10-Axes: Categorical Data
2.11-Grid
2.12-Tools
2.13-Glyphs
2.14-Legend: Configuring
2.15-Legend: Styling
2.16-Popup Windows
2.17-Exercise 3: Summary of Section 3
2.18-Exercise 3: Solution
3-Advanced Plotting
14
3.1-Section Introduction
3.2-ColumnDataSource
3.3-Exercise 4: Plotting Elements of the Periodic Table
3.4-Exercise 4: Solution
3.5-Popup Windows with Custom HTML
3.6-Gridplots
3.7-Exercise 5: Gridplots
3.8-Exercise 5: Solution
3.9-Annotations: Spans and Boxes
3.10-Exercise 6: Span Annotations
3.11-Exercise 6: Solution
3.12-Annotations: Labels and LabelSets
3.13-Exercise 7: Labels in Spans
3.14-Exercise 7: Solution
4-Bokeh Server: Interactive Plotting with HTML Widgets
10
4.1-Section Introduction
4.2-Widgets in Static Bokeh Graphs
4.3-Widgets in Interactive Bokeh Server Apps
4.4-Select Widgets: Changing Labels Dynamically
4.5-Exercise 8: Select Widgets - Drawing Spans Dynamically
4.6-Exercise 8: Tips
4.7-Exercise 8: Solution
4.8-RadioButtonGroup Widgets: Changing Labels Dynamically
4.9-Slider Widgets: Filtering Glyphs, Part 1
4.10-Slider Widgets: Filtering Glyphs, Part 2
5-Bokeh Server: Streaming Real Time Data
8
5.1-Section Introduction
5.2-Streaming Random Points and Lines
5.3-Streaming Financial Data - Designing the App
5.4-Streaming Financial Data - Webscraping
5.5-Streaming Financial Data - Plotting
5.6-Streaming Timeseries Data
5.7-User Interaction Between Real-Time Plots and Widgets
5.8-Example: Visualizing Spinning Planets
6-Embedding Bokeh Plots in Websites
5
6.1-Introduction to Flask
6.2-Embedding Static Bokeh Plots in Flask
6.3-Embedding Bokeh Server Plots in Flask
6.4-Embedding Static Bokeh Plots in Django: Setting up a Django App
6.5-Embedding Static Bokeh Plots in Django: Embedding the Plot
7-Deploying Bokeh Data Visualization Apps in Live Servers
11
7.1-Deployment Options
7.2-Deploying Static Bokeh Plots
7.3-Deploying Interactive Bokeh Server Apps Embedded in Flask- Setting up the VPS
7.4-Deploying Interactive Bokeh Server Apps Embedded in Flask - Installing Software
7.5-Deploying Interactive Bokeh Server Apps Embedded in Flask - Configuration Files
7.6-Deploying Interactive Bokeh Server Apps Embedded in Flask - Uploading Files
7.7-Deploying Interactive Bokeh Server Apps Embedded in Flask - Editing Server Files
7.8-Deploying Interactive Bokeh Server Apps Embedded in Flask - Starting the Service
7.9-Deploying Interactive Bokeh Server Apps Embedded in Flask - Troubleshooting
7.10-Deploying Interactive Bokeh Server Apps as Standalone
7.11-Bonus Lecture