Data science on COVID-19 / CORONA virus spread data

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Learning Goals and Content Overview

Lecture 3: Used Python Resources

Chapter 2: Business Understanding

Lecture 1: Introduction to Data Science

Lecture 2: CRISP-DM

Lecture 3: Terminology Data Science

Lecture 4: Python Project Setup

Chapter 3: Data Understanding

Lecture 1: Introduction Data Understanding

Lecture 2: Data Gathering GITHUB – Johns Hopkins GITHUB

Lecture 3: Data Gathering Web Scraping Example

Lecture 4: Data Gathering API call

Lecture 5: Data Gathering REST API call

Lecture 6: Data Gathering Wrap Up

Chapter 4: Data Preparation

Lecture 1: Initial Data Preparation

Lecture 2: Conversion of Date Objects

Lecture 3: Relational Data Structure

Chapter 5: Explorative Data Analysis – Dynamic Dashboards

Lecture 1: Introduction – Plotting with Matplotlib

Lecture 2: Dynamic Plots with Plot.ly

Lecture 3: First Dynamic COVID 19 visualization

Lecture 4: Dynamic Plots via Dash

Chapter 6: Modeling

Lecture 1: Modeling Introduction

Lecture 2: Modeling start with helper functions

Lecture 3: Exponential Slopes

Lecture 4: Machine Learning Basics Introduction

Lecture 5: Scikit-Learn Linear Regression

Lecture 6: ML Model Hypothesis

Lecture 7: Logarithmic Feature Space

Lecture 8: Piecewise Linear Regression

Lecture 9: Filtering the COVID Input and Doubling Rate calculations

Chapter 7: Evaluation – Full Walkthrough

Lecture 1: Preparing the full walkthrough – Minimum Viable Product

Lecture 2: Groupby apply on test data structure

Lecture 3: Merging the full dataset

Lecture 4: Automated Feature Transfomation

Lecture 5: Finalizing the Minimum Viable Product

Chapter 8: Deployment

Lecture 1: Prepare for Professional Software Delivery

Lecture 2: Summary Best Practices

Chapter 9: Predictive Machine Learning Modeling

Lecture 1: Forecasting / Predictions Overview

Lecture 2: Overfitting Introduction

Lecture 3: Overfitting data preparation

Lecture 4: Overfitting demo and metrics

Lecture 5: Cross validation Explained

Lecture 6: Forecasts Programming Intro

Lecture 7: Forecasts with Facebook Prophet

Lecture 8: FB Prophet Cross-Validation

Lecture 9: Controlling Results and Trivial Model

Lecture 10: Selection Bias and Variance

Chapter 10: Simulation of SIR compartmental model

Lecture 1: SIR modeling of infectious disease

Lecture 2: Simulating the SIR curves

Lecture 3: Curve fitting of SIR parameters

Lecture 4: Dynamic SIR Simulation Example

Lecture 5: Thank you

Chapter 11: Bonus: Introduction to Tableau Dashboards

Lecture 1: Introduction and visualization tool overview

Lecture 2: Tableau and the final dashboard

Lecture 3: Visualizing a world map

Lecture 4: Time Series Chart

Lecture 5: Rolling Mean Calculation

Lecture 6: Doubling Rate Calculation

Lecture 7: Adding additional statistical data

Lecture 8: Dynamic axis selection

Lecture 9: First dashboard and publishing

Chapter 12: Oct 2020: 6 Month Project Review

Lecture 1: Status Review on current Covid-19 data

Lecture 2: Automatic Git pull

Lecture 3: Debugging and error fix on some countries

Lecture 4: Visualising World Map Plot.ly

Lecture 5: Thank you: Students Dashboards Compilation

Chapter 13: Section 13: Dez 2021 Update on boosters data

Lecture 1: Dynamic World Map: Bossters per hundred

Chapter 14: Section 14: Final wrap up with ChatGPT

Lecture 1: ChatGPT wrap up