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Applied Data Science COVID-19 data analysis

Udemy · Frank Kienle · 1 HN points · 0 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention Udemy's "Applied Data Science COVID-19 data analysis" from Frank Kienle.
Course Description

The goal of this lecture is to transport the best practices of data science from the industry while developing a COVID-19 analysis prototype

The student should learn the process of modeling (Python) and a methodology to approach a business problem.

The final result will be a dynamic dashboard - which can be updated by one click - of COVID-19 data with filtered and calculated data sets like the current Doubling Rate of confirmed cases

Techniques used are REST Services, Python Pandas, scikit-learn, Facebook Prophet, Plotly, Dash, and SIR virus spread simulations + bonus section Tableau for visual analytics

For this, we will follow an industry-standard  CRISP process by focusing on the iterative nature of agile development

  • Business understanding (what is our goal)

  • Data Understanding (where do we get data and cleaning of data)

  • Data Preparation (data transformation and visualization)

  • Modeling (Statistics, Machine Learning, and SIR Simulations on COVID Data)

  • Deployment (how to deliver results, dynamic dashboards in python and Tableau)


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Provider Info
This course is offered by Frank Kienle on the Udemy platform.
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Hacker News Stories and Comments

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Nov 15, 2020 · 1 points, 2 comments · submitted by kienlef
kienlef
Forgot to post the Github link with the ressources: https://github.com/kienlef/Lecture_Covid_19_data_analysis
kienlef
The lecture COVID-19 data analytics was given at the Technical University Kaiserslautern, Germany, Summer 2020. The goal is to transport the best practices of data science from the industry while developing a COVID-19 analysis prototype.

The student should learn the process of modeling (Python) and a methodology to approach a business problem.

The final result will be a dynamic dashboard - which can be updated by one click - of COVID-19 data with filtered and calculated data sets like the current Doubling Rate of confirmed cases

Techniques used are REST Services, Python Pandas, scikit-learn, Facebook Prophet, Plotly, Dash, and SIR virus spread simulations + bonus section Tableau for visual analytics

The lecture was recorded quite early during the pandemic in March 2020 when the lecturer was in quarantine and had to prepare for the new lecture season purely in remote style. Any feedback for improvement is welcome. best regards Frank

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