Build an Interactive Data Analytics Dashboard with Python

Learn and complete all of the steps to deploy your very own data analytics dashboard on the web with Python

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Last Updated: 2022-06-16 08:06:18 English Course Type: Self Learning

Build an interactive data analytics dashboard using the Dash library in Python
Model coronavirus cases and deaths using generalized logistic functions
Smooth data using locally weighted scatterplot smoothing
Read and clean data so that it is suitable for modeling
Learn how to use Plotly, an interactive data visualization library in Python targeting the web
Learn HTML and CSS fundamentals to add and style elements of the dashboard
Setup an Ubuntu server running NGINX to host the dashboard on the web for all to see
Run nightly cron jobs to update the data and model predictions
Encapsulate all of your code into Python classes to ease automation
Learn how to complete a comprehensive, end-to-end project in Python using a vast array of skills

This course teaches you all of the skills to build interactive data analytics dashboards with Python. Specifically, you will be building a Coronavirus Forecasting Dashboard that shows historical and predicted values for deaths and cases for all countries in the world and US states from the ongoing coronavirus pandemic. The final product will be a live dashboard, automatically updated daily, hosted on a remote server for anyone, anywhere in the world to see!

You will learn and complete the entire process for building the dashboard including the following topics:

Getting, Cleaning, and Transforming the Data - You will learn how to collect the data, find and clean bad data, and transform it so that it can be used for building models capable of prediction.
Data Smoothing - You will learn several different techniques such as LOWESS to smooth the jagged raw data so that the model can better detect trends.
Exponential Growth and Decline Models - You will begin modeling coronavirus cases for each area of the world with these simple models that can capture a single exponential growth or decline phase, but not both.
Logistic Growth Models - You will learn about a separate class of "S-Curve" models capable of capturing both exponential growth and decline in the same model.
Modeling New Waves - Coronavirus cases appear in waves over different time periods. You will learn how to change your model so that it can detect any number of new waves in the future.
Encapsulation into Classes - After selecting your model, you will encapsulate all of the code together into Python classes, eventually to be used in your final production code.
Visualizations with Plotly - You will learn how to use the Plotly Python library to create interactive data visualizations targeted for the web
HTML and CSS - You are building a web application and will learn the fundamentals of HTML and CSS to help add customization with the help of Dash.
Building the Dashboard with Dash - You will learn how to build all of the components and interactivity of the dashboard with the Dash Python library.
Deployment - One of the most exciting parts of a project is deploying it on your own server for the world to see. You will learn two different deployment options - one simple and the other more complex, but with more flexibility.

What's Included?

This course comes with a massive amount of material including:

13 Jupyter Notebooks
26 Exercises with detailed solutions
200 page PDF of the entire course content
All production code for the dashboard

Technologies used

All code for developing the dashboard will be done using Python
Pandas will be used extensively for analyzing and transforming data
Statsmodels will be used for smoothing
Scipy will be used for building the models for coronavirus cases and parameter optimization
Matplotlib will be used in the notebooks for static visualizations
Plotly will be used for interactive data visualizations that appear in the dashboard
Dash will be used for building the dashboard itself
HTML/CSS will be used together with Dash to customize components of the dashboard
You'll learn how to setup your own Linux Ubuntu server to run your dashboard

Students should feel comfortable with the fundamentals of Python
Knowledge of the pandas library in Python is helpful

Intermediate Python programmers excited to complete a comprehensive project covering all of the steps to launch a dashboard on the web for all to see.

Section 1 : Getting Started 10 Lectures 00:42:48
Lecture 1 :
Course Overview Preview
Lecture 2 :
Downloading the Course Material
Lecture 3 :
Exploring the Course Material
Lecture 4 :
Note for Windows Users
Lecture 5 :
Creating the Virtual Environment (fast)
Lecture 6 :
Creating the Virtual Environment
Lecture 7 :
Activating and Deactivating the Virtual Environment
Lecture 8 :
Launching and Exploring the Dashboard
Lecture 9 :
Opening the Jupyter Notebooks
Lecture 10 :
A Guide To Completing the Course
Section 2 : Getting The Data 3 Lectures 00:28:05
Lecture 1 :
Downloading the Data
Lecture 2 :
Downloading all of the Data
Lecture 3 :
Saving the Data Locally
Section 3 : Data Cleaning and Transformation 7 Lectures 00:31:18
Lecture 1 :
Selecting the Correct Columns
Lecture 2 :
Updating the Run Function
Lecture 3 :
Updating Area Names
Lecture 4 :
Aggregate Repeating Areas
Lecture 5 :
Transposing the Data to Time Series
Lecture 6 :
Finding and Handling Bad Data
Lecture 7 :
Encapsulate all Steps into a Class
Section 4 : Data Smoothing 3 Lectures 00:23:12
Lecture 1 :
Moving Average Smoothing
Lecture 2 :
LOWESS Smoothing
Lecture 3 :
Build a Function to Smooth the Data
Section 5 : Exponential Growth and Decline Models 10 Lectures 00:52:45
Lecture 1 :
Exponential Growth and Decline
Lecture 2 :
Finding Optimal Parameters to Model Total Cases with scipy's least_squares
Lecture 3 :
Predicting Unseen Data
Lecture 4 :
Automate Model Training
Lecture 5 :
Automate Daily Predictions
Lecture 6 :
Automate Cumulative Predictions
Lecture 7 :
Plotting Actual vs Prediction
Lecture 8 :
Automating Smoothing, Training, Predicting, and Plotting
Lecture 9 :
Continuous Exponential Growth Models
Lecture 10 :
Modeling Exponential Decline
Section 6 : Logistic Growth Models 5 Lectures 00:34:35
Lecture 1 :
Assymptotes
Lecture 2 :
S-Curves
Lecture 3 :
Experimenting with Logistic Function Parameters
Lecture 4 :
Estimating Logistic Function Parameters
Lecture 5 :
Generalized Logistic Function
Section 7 : Modeling New Waves 6 Lectures 00:16:56
Lecture 1 :
Limit the Data
Lecture 2 :
Vertically Shifting the Generalized Logistic Function
Lecture 3 :
Upper Bounds of L
Lecture 4 :
Automating Parameter Bounds
Lecture 5 :
Finding New Waves
Lecture 6 :
The Final Model
Section 8 : Encapsulating into Classes 8 Lectures 00:38:43
Lecture 1 :
The CasesModel Class
Lecture 2 :
Instantiating and Running CasesModel
Lecture 3 :
Predict Deaths using Case Fatality Ratio
Lecture 4 :
The DeathsModel Class
Lecture 5 :
Instantiating and Running DeathsModel
Lecture 6 :
Creating Final Tables for the Dashboard
Lecture 7 :
Create Summary Table of Current Date
Lecture 8 :
Code within the Modules
Section 9 : Running all of the Code 2 Lectures 00:10:52
Lecture 1 :
Explaining update.py
Lecture 2 :
Executing update.py
Section 10 : Visualization with Plotly 9 Lectures 01:05:29
Lecture 1 :
Intro to Plotly
Lecture 2 :
Discovering Trace Properties
Lecture 3 :
Updating the Layout
Lecture 4 :
Creating a Figure with Multiple Traces
Lecture 5 :
Automate Plotting of an Area
Lecture 6 :
Creating Subplots
Lecture 7 :
Adding Annotations
Lecture 8 :
Choropleth Maps
Lecture 9 :
Plotly Summary
Section 11 : Intro to HTML and CSS 16 Lectures 01:14:04
Lecture 1 :
HTML Elements
Lecture 2 :
Writing HTML in the Notebook
Lecture 3 :
The Anchor Element
Lecture 4 :
The Image Element
Lecture 5 :
Unordered and Ordered Lists and List Items
Lecture 6 :
Logical Division of Content Element
Lecture 7 :
HTML Syntax
Lecture 8 :
Block vs Inline Elements
Lecture 9 :
Styling with CSS
Lecture 10 :
CSS Distance Units
Lecture 11 :
Element Margins
Lecture 12 :
Changing Display from Block to Inline
Lecture 13 :
Selecting Elements by class and id
Lecture 14 :
Container-Item Terminology
Lecture 15 :
The Flexbox Layout
Lecture 16 :
The Grid Layout
Section 12 : Building the Dashboard with Dash 39 Lectures 00:00:00
Lecture 1 :
Parts of a Dash Application
Lecture 2 :
A Minimal Dashboard
Lecture 3 :
HTML Elements in Dash
Lecture 4 :
Adding CSS
Lecture 5 :
Creating a Data Table
Lecture 6 :
Creating Tabs
Lecture 7 :
Recreating all Plotly Figures
Lecture 8 :
Adding Plotly Figures to the Dashboard
Lecture 9 :
Adding Maps to the Dashboard
Lecture 10 :
Adding Radio Buttons Above the Map
Lecture 11 :
Interactivity Using Callbacks
Lecture 12 :
Single Callback for Updating both World and USA Data
Lecture 13 :
Callback to Change the Map
Lecture 14 :
Dashboard Complete
Lecture 15 :
Launching the Dashboard while Notebook Running
Lecture 16 :
Opening dashboard.py
Lecture 17 :
Comments in dashboard.py
Lecture 18 :
Imports and Instantiation
Lecture 19 :
Global Variables and Reading in the Data
Lecture 20 :
Functions to Create Tables and Graphs
Lecture 21 :
Creating the Navigation Bar
Lecture 22 :
Creating the Dashboard Headers
Lecture 23 :
Creating the Tabs and Tables
Lecture 24 :
Creating the Graph Components
Lecture 25 :
Creating the Map and Radio Buttons
Lecture 26 :
Creating the Cards
Lecture 27 :
Creating the Containers
Lecture 28 :
Adding the Callbacks
Lecture 29 :
Dashboard Complete
Lecture 30 :
Opening the CSS File
Lecture 31 :
CSS Variables
Lecture 32 :
Top Info and Container CSS
Lecture 33 :
Grid Container CSS
Lecture 34 :
Grid Area CSS
Lecture 35 :
Radio Button CSS
Lecture 36 :
Tab CSS
Lecture 37 :
Side Card CSS
Lecture 38 :
CSS for Smaller Screens
Lecture 39 :
Dash Summary
Section 13 : Deployment 34 Lectures 01:35:20
Lecture 1 :
What is a Web Server
Lecture 2 :
Getting Started with Python Anywhere
Lecture 3 :
The Python Anywhere Dashboard
Lecture 4 :
Create New Web App
Lecture 5 :
Upload the Files
Lecture 6 :
Change the Working Directory
Lecture 7 :
Modify WSGI File
Lecture 8 :
Open Console
Lecture 9 :
Install dash_bootstrap_components
Lecture 10 :
Install statsmodels
Lecture 11 :
Verify Dashboard is Up and Running
Lecture 12 :
Create Daily Task to Run update.py
Lecture 13 :
Log Files
Lecture 14 :
Python Anywhere Deployment Complete
Lecture 15 :
Deploying on Ubuntu with Vultr
Lecture 16 :
Launch Ubuntu Server
Lecture 17 :
Upgrade Instance to use IPv4
Lecture 18 :
Install PuTTY (Windows)
Lecture 19 :
SSH into Server (Windows)
Lecture 20 :
SSH into Server (Mac and Linux)
Lecture 21 :
Updating and Upgrading with apt
Lecture 22 :
Install ZSH
Lecture 23 :
Install pip and venv
Lecture 24 :
Install NginX
Lecture 25 :
Create Project Directory
Lecture 26 :
Transfer project files to server (Mac and Linux)
Lecture 27 :
Create Virtual Environment
Lecture 28 :
Install Requirements
Lecture 29 :
Configuring Gunicorn with systemd
Lecture 30 :
Configure NginX to Communicate with Gunicorn
Lecture 31 :
Accessing Log Files with journalctl
Lecture 32 :
Automatic Daily Updates with a Cron Job
Lecture 33 :
Deployment Complete
Lecture 34 :
Transfer project files to server (Windows)

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Theodore Petrou,

2906 Course Views

4 Courses

I am the author of Pandas Cookbook and Master Data Analysis with Python, highly rated texts on performing real-world data analysis with Pandas. I am the founder of Dunder Data, a company that teaches the fundamentals of data science and machine learning. I enjoy discovering best practices on how to use and teach data analysis with Python. I also enjoy creating open-source Python libraries and am author of Dexplot, Bar Chart Race, DataFrame Image, and Jupyter to Medium.