covid19-dashboard - A site that displays up to date COVID-19 stats, powered by fastpages.

  •        23

This project showcases how you can use fastpages to create a static dashboard that update regularly using Jupyter Notebooks. Using fastpages, data professionals can share dashboards (that are updated with new data automatically) without requiring any expertise in front end development. The content of this site shows statistics and reports regarding Covid-19.



Related Projects

covid-19-repo-data - Data archive of identifiable COVID-19 related public projects on GitHub


A comprehensive versioned dataset of the repositories and relevant related metadata about public projects hosted on GitHub related to the 2019 Novel Coronavirus and associated COVID-19 disease. For a view of the latest projects, see the covid-19 topic on GitHub. To preview and interact with the data provided, see the subsection below.

covid-chestxray-dataset - We are building an open database of COVID-19 cases with chest X-ray or CT images

  •    Jupyter

Project Summary: To build a public open dataset of chest X-ray and CT images of patients which are positive or suspected of COVID-19 or other viral and bacterial pneumonias (MERS, SARS, and ARDS.). Data will be collected from public sources as well as through indirect collection from hospitals and physicians. All images and data will be released publicly in this GitHub repo. Lung Bounding Boxes and Chest X-ray Segmentation (license: CC BY 4.0) contributed by General Blockchain, Inc.

COVID-19 - Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE


This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL). DATA SOURCES: This list includes a complete list of all sources ever used in the data set, since January 21, 2010. Some sources listed here (e.g. ECDC, US CDC, BNO News) are not currently relied upon as a source of data.

covid-19-data - Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data

  •    Python

Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data

covid19model - Code for modelling estimated deaths and cases for COVID19.

  •    Stan

Code for modelling estimated deaths and infections for COVID-19 from "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe", Flaxman, Mishra, Gandy et al, Nature, 2020, the published version of our original Report 13. If you are looking for the individual based model used in Imperial's Report 9, Ferguson, Laydon, Nedjati-Gilani et al, please look here.

covid-19-open-data - Datasets of daily time-series data related to COVID-19 for over 20,000 distinct locations around the world

  •    Python

The data is drawn from multiple sources, as listed below, and stored in separate tables as CSV files grouped by context, which can be easily merged due to the use of consistent geographic (and temporal) keys as it is done for the main table. 1 key is a unique string for the specific geographical region built from a combination of codes such as ISO 3166, NUTS, FIPS and other local equivalents. 2 Refer to the data sources for specifics about each data source and the associated terms of use. 3 Datasets without a date column contain the most recently reported information for each datapoint to date.

here-covid-19-tracker - Using HERE Technologies APIs, fork and build your own COVID-19 Tracker

  •    Javascript

Update: March 17, 2020 - The production version of this map has been updated and refined to listing countries only. We will publish the updated GitHub repo as soon as possible. This repository is the code used to power the HERE-hosted COVID-19 map. HERE is releasing this code to the community so that any developer may iterate, improve or even recommend features/functionality through Pull Requests.

covid-19-data - An ongoing repository of data on coronavirus cases and deaths in the U.S.


The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak. Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

python-business-analytics - Python solutions to solve practical business problems.

  •    Jupyter

Animated Investment Management Research at — Sponsoring open source AI, Machine learning, and Data Science initiatives. A series looking at implementing python solutions to solve practical business problems. Share your own projects on this subreddit, r/datascienceproject. Every week we will look at hand picked businenss solutions. See the following google drive for all the code and github for all the data. If you follow the LinkedIn page, you would be able to see the lastest developments.

nteract - 📘 The interactive computing suite for you! ✨

  •    Javascript

nteract is first and foremost a dynamic tool to give you flexibility when writing code, exploring data, and authoring text to share insights about the data. Edit code, write prose, and visualize.

actionsflow - The free Zapier/IFTTT alternative for developers to automate your workflows based on Github actions

  •    TypeScript

Actionsflow helps you automate workflows - it's a free IFTTT/Zapier alternative for developers. With Actionsflow you can connect your favorite apps, data, and APIs, receive notifications of actions as they occur, sync files, collect data, and more. We implemented it based on Github actions, and you use a YAML file to build your workflows. The configuration format is the same as Github actions, which makes it easy for you to get going if you've explored Github actions before. You can also use any Github actions as your job's steps. You can learn more about the core concepts of Actionsflow here.

covid3d - 🌏 An interactive 3D visualization of COVID-19.

  •    Javascript

3D visualization of latest data regarding coronavirus cases with time travel. Licensed under the GNU General Public License.

textbook - The textbook Computational and Inferential Thinking: The Foundations of Data Science

  •    Jupyter

This repository holds a Jekyll-based version of the Data 8 textbook. All textbook content is primarily stored in Jupyter notebooks in the notebooks/ folder. This can be converted to Jekyll-ready markdown and served on github pages.

hacknical - Hacknical, hacker & technical. A website for GitHub user to make a better resume.

  •    Javascript

A website for GitHub user to generate his GitHub data analysis (contributions/commits/languages/repos datas), helps to make a better resume. Attention:Most of the pages support English now😁😁😁, including github data analysis page.

Agile_Data_Code_2 - Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

  •    Jupyter

Like my work? I am Principal Consultant at Data Syndrome, a consultancy offering assistance and training with building full-stack analytics products, applications and systems. Find us on the web at There is now a video course using code from chapter 8, Realtime Predictive Analytics with Kafka, PySpark, Spark MLlib and Spark Streaming. Check it out now at

actions-gh-pages - GitHub Actions for GitHub Pages 🚀 Deploy static files and publish your site easily

  •    TypeScript

The next example step will deploy ./public directory to the remote gh-pages branch. For newbies of GitHub Actions: Note that the GITHUB_TOKEN is NOT a personal access token. A GitHub Actions runner automatically creates a GITHUB_TOKEN secret to authenticate in your workflow. So, you can start to deploy immediately without any configuration.

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

  •    Jupyter

This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.