Build an Interactive Data Analytics Dashboard with Python

person icon Teddy Petrou

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

updated on icon Updated on Nov, 2023

language icon Language - English

person icon Teddy Petrou

architecture icon Data Visualization,IT & Software

Lectures -153

Resources -1

Duration -12 hours


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Course Description

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

Who this course is for:

  • 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.


What will you learn in this course:

  • 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


What are the prerequisites for this course?

  • Students should feel comfortable with the fundamentals of Python
  • Knowledge of the pandas library in Python is helpful
Build an Interactive Data Analytics Dashboard with Python


Check out the detailed breakdown of what’s inside the course

Getting Started
10 Lectures
  • play icon Courses-Promo 03:41 03:41
  • play icon Course Overview 08:58 08:58
  • play icon Exploring the Course Material 03:47 03:47
  • play icon Note for Windows Users 00:41 00:41
  • play icon Creating the Virtual Environment (fast) 05:34 05:34
  • play icon Creating the Virtual Environment 15:00 15:00
  • play icon Activating and Deactivating the Virtual Environment 03:05 03:05
  • play icon Launching and Exploring the Dashboard 07:51 07:51
  • play icon Opening the Jupyter Notebooks 04:13 04:13
  • play icon A Guide To Completing the Course 03:17 03:17
Getting the Data
3 Lectures
Data Cleaning and Transformation
7 Lectures
Data Smoothing
3 Lectures
Exponential Growth and Decline Models
10 Lectures
Logistic Growth Models
5 Lectures
Modeling New Waves
6 Lectures
Encapsulation into Classes
8 Lectures
Running all of the Code
2 Lectures
Visualization with Plotly
9 Lectures
Intro to HTML and CSS
17 Lectures
Building the Dashboard with Dash
39 Lectures
34 Lectures

Instructor Details

Teddy Petrou

Teddy Petrou

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.

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