Build an Interactive Data Analytics Dashboard with Python

Name: Build an Interactive Data Analytics Dashboard with Python
Rating: 4.4 (151 reviews)

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

Created byTed Petrou

Last updated 10/2022

English

English [Auto],

What you'll learn

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

Course content

13 sections • 153 lectures • 11h 59m total length

Course Overview8:58
Navigate to coronavirus-project.dunderdata.com to view the live dashboard!
Downloading the Course Material2:17
Exploring the Course Material3:47
Note for Windows Users0:11
Creating the Virtual Environment (fast)5:34
Update!
Step 7 has changed since the video was created. Make sure to use the step below in the text!
Create the virtual environment quickly. Use the next video for a more detailed explanation.
cd into the project directory
python -m venv dashboard_venv
source dashboard_venv/bin/activate
pip install -U pip
pip install wheel
pip install -r requirements.txt
pip install matplotlib==3.4.3 nbconvert==5.6.1 notebook==6.1.6
pip install jupyter-dash==0.4
Note for windows users - Activate your virtual environment by running the command dashboard_venv\Scripts\activate.bat. Do not use source as shown in the video.
Creating the Virtual Environment15:00
Update!
Step 7 has changed since the video was created. Make sure to use the step below in the text!

cd into the project directory
python -m venv dashboard_venv
source dashboard_venv/bin/activate
pip install -U pip
pip install wheel
pip install -r requirements.txt
pip install matplotlib==3.4.3 nbconvert==5.6.1 notebook==6.1.6
pip install jupyter-dash==0.4
Note for windows users - Activate your virtual environment by running the command dashboard_venv\Scripts\activate.bat. Do not use source as shown in the video.
Activating and Deactivating the Virtual Environment3:05
Activate and deactivate the virtual environment to control Python mappings and libraries, using the deactivate command or the bin activate script, and optionally reactivating the conda environment for consistent dashboard.
Launching and Exploring the Dashboard7:51
Launch the Dash dashboard by activating the virtual env and running python dashboard.py, then explore world and U.S. data, interactive tables, charts, maps, and hover details, with stop via Ctrl-C.
Opening the Jupyter Notebooks4:13
Open the Jupyter notebooks by activating the virtual environment in the project directory and launching Jupyter Notebook from the course’s notebooks folder to run the dashboard code.
A Guide To Completing the Course3:17

Selecting the Correct Columns8:37
Updating the Run Function3:18
Updating Area Names8:38
Aggregate Repeating Areas4:58
Transposing the Data to Time Series3:29
Transpose the data to move dates into rows and countries into columns, creating a time series with a date time index using pandas.
Finding and Handling Bad Data16:22
Encapsulate all Steps into a Class5:46
Encapsulate all data cleaning and transformation steps into a single prepare data class, with an option to download new data or read local data, producing four prepared data frames.

Exponential Growth and Decline2:46
Explore exponential growth and decline using a × b^x, where a is the initial value, b the growth rate, and x the time unit; b>1 grows, b<1 declines toward zero.
Finding Optimal Parameters to Model Total Cases with scipy's least_squares8:17
Predicting Unseen Data2:32
Automate Model Training4:09
Automate model training by wrapping least squares optimization into a reusable function that fits a simple exponential model to any data series with given bounds and an initial guess.
Automate Daily Predictions6:15
Automate Cumulative Predictions6:38
Plotting Actual vs Prediction2:45
Automating Smoothing, Training, Predicting, and Plotting4:30
Continuous Exponential Growth Models6:19
Modeling Exponential Decline8:33
Model exponential decline in coronavirus curves, introducing increasing exponential decline with a shift parameter to keep values positive; compare with growth using Italy data and note the upcoming logistic model.

Assymptotes3:11
Explore asymptotes in logistic growth models and how they differ from exponential curves, using the coronavirus cumulative curve and 1/x, a/(b+x^2) to illustrate limits.
S-Curves5:42
Experimenting with Logistic Function Parameters5:05
Estimating Logistic Function Parameters12:33
Smooth Italy case data, fit a logistic model by least-squares to estimate L, x0, and K within defined bounds, and project future cases up to May 1 using the model.
Generalized Logistic Function13:46

The CasesModel Class16:55
Instantiating and Running CasesModel8:51
Predict Deaths using Case Fatality Ratio5:09
Predict deaths by estimating the case fatality ratio from lagged cases, using a 50 day lag and 30 day windows to compute CFR for US and world.
The DeathsModel Class8:18
The deaths model class computes a CFR per area using lag and period, then predicts future deaths by multiplying cases from past dates by that CFR, followed by smoothing.
Instantiating and Running DeathsModel3:20
Instantiate and run the deaths model with training data to generate daily and cumulative death predictions for the first 10 states, and view a 30-day forecast with a 15-day lag.
Creating Final Tables for the Dashboard8:08
Create Summary Table of Current Date2:42
Code within the Modules2:14

Intro to Plotly8:41
Discovering Trace Properties9:48
Updating the Layout8:47
Creating a Figure with Multiple Traces5:36
Automate Plotting of an Area6:09
Learn to automate plotting for a specific area by filtering a data frame, selecting a kind, and producing a bar plot of actual versus predicted values over time.
Creating Subplots6:37
Adding Annotations4:27
learn to add customized subplot titles using annotations in a plotting workflow, employing update layout to label cases and deaths and customize legend placement.
Choropleth Maps10:29
Plotly Summary4:55
Learn to build interactive data visualizations with Plotly by creating figures with go.Figure or subplots, adding traces, and updating the layout, including geo maps with polygon data.

Requirements

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

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.

Build an Interactive Data Analytics Dashboard with Python

What you'll learn

Explore related topics

Course content

Getting Started10 lectures • 54min

Getting the Data3 lectures • 28min

Data Cleaning and Transformation7 lectures • 51min

Data Smoothing3 lectures • 23min

Exponential Growth and Decline Models10 lectures • 53min

Logistic Growth Models5 lectures • 40min

Modeling New Waves6 lectures • 20min

Encapsulation into Classes8 lectures • 56min

Running all of the Code2 lectures • 11min

Visualization with Plotly9 lectures • 1hr 5min

Requirements

Description

Who this course is for: