Python for Biostatistics- Analyzing Infectious Diseases Data

Python for Biostatistics: Analyzing Infectious Diseases Data, available at $59.99, has an average rating of 4.05, with 22 lectures, based on 14 reviews, and has 3063 subscribers.

You will learn about Learn the basic fundamentals of biostatistics and infectious disease analysis Learn how to find correlation between population and disease rate Learn how to analyze infected patient demographics Learn how to map infectious disease per county using heatmap Learn how to analyze infectious disease yearly trend Learn how to perform confidence interval analysis Learn how to forecast infectious disease rate using time series decomposition Learn how to do epidemiological modeling using SIR model Learn how to perform public health policy evaluation Learn how to calculate infectious disease transmission rate using SIR model Learn several factors that accelerate the spread of infectious disease, such as population density, herd immunity, and antigenic variation Learn how to detect potential outliers using Z score method Learn how to clean dataset by removing missing rows and duplicate values Learn how to find and download datasets from Kaggle This course is ideal for individuals who are People who are interested in learning biostatistics or People who are interested in analysing infectious disease dataset It is particularly useful for People who are interested in learning biostatistics or People who are interested in analysing infectious disease dataset.

Enroll now: Python for Biostatistics: Analyzing Infectious Diseases Data

Summary

Title: Python for Biostatistics: Analyzing Infectious Diseases Data

Price: $59.99

Average Rating: 4.05

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the basic fundamentals of biostatistics and infectious disease analysis

Learn how to find correlation between population and disease rate

Learn how to analyze infected patient demographics

Learn how to map infectious disease per county using heatmap

Learn how to analyze infectious disease yearly trend

Learn how to perform confidence interval analysis

Learn how to forecast infectious disease rate using time series decomposition

Learn how to do epidemiological modeling using SIR model

Learn how to perform public health policy evaluation

Learn how to calculate infectious disease transmission rate using SIR model

Learn several factors that accelerate the spread of infectious disease, such as population density, herd immunity, and antigenic variation

Learn how to detect potential outliers using Z score method

Learn how to clean dataset by removing missing rows and duplicate values

Learn how to find and download datasets from Kaggle

Who Should Attend

People who are interested in learning biostatistics

People who are interested in analysing infectious disease dataset

Target Audiences

People who are interested in learning biostatistics

People who are interested in analysing infectious disease dataset

Welcome to Python for Biostatistics: Analyzing Infectious Diseases Data course. This is a comprehensive project-based course where you will learn step by step on how to perform complex analysis and visualization on infectious diseases datasets. This course is a perfect combination between biostatistics and Python, equipping you with the tools and techniques to tackle real-world challenges in public health. The course will be mainly concentrating on three major aspects, the first one is data analysis where you will explore the infectious diseases data from multiple perspectives, the second one is time series forecasting where you will be guided step by step on how to forecast the spread of infectious diseases using STL model, and the third one is public health policy where you will learn how to make a data driven public health policy based on epidemiological modeling. In the introduction session, you will learn the basic fundamentals of biostatistics, such as getting to know more about challenges that we commonly face when analyzing biostatistics data and statistical models that we will use, for instance STL which stands for seasonal trend decomposition. Then, you will continue by learning how to calculate infectious disease transmission using Kermack-McKendrick equation, this is a very important concept that you need to understand before getting into the coding session. Afterward, you will also learn several factors that can potentially accelerate the spread of infectious diseases, such as population density, healthcare accessibility, and antigenic variation. Once you have learnt all necessary information about biostatistics, we will start the project. Firstly, you will be guided step by step on how to set up Google Colab IDE. Not only that, you will also learn how to find and download infectious diseases dataset from Kaggle. Once, everything is ready, we will enter the main section of the course which is the project section The project will be consisted of three main parts, the first part is to conduct exploratory data analysis, the second part is to build forecasting model to predict the spread of the diseases in the future using time series model, meanwhile the third part is to perform epidemiological modelling and use the result to develop a public health policy to slow down the spread of the infectious disease.

First of all, before getting into the course, we need to ask this question to ourselves: why should we learn biostatistics, particularly infectious diseases analysis? Well, there are many reasons why, firstly, if you are interested in working in the public health or healthcare industry, having biostatistics knowledge would be very beneficial and help you to level up your career. In addition to that, you will also learn a lot of valuable skill sets that can be implemented in other projects, for example, time series decomposition can be used to forecast stock, real estate, commodity, and cryptocurrency markets. Last but not least, this course will also train you to be a better public health policy maker as you will extensively learn how to make data driven decisions and take other external factors into consideration.

Below are things that you can expect to learn from this course:

Learn the basic fundamentals of biostatistics and infectious disease analysis

Learn how to calculate infectious disease transmission rate using SIR model

Learn several factors that accelerate the spread of infectious disease, such as population density, herd immunity, and antigenic variation

Learn how to find and download datasets from Kaggle

Learn how to clean dataset by removing missing rows and duplicate values

Learn how to detect potential outliers using Z score method

Learn how to find correlation between population and disease rate

Learn how to analyze infected patient demographics

Learn how to map infectious disease per county using heatmap

Learn how to analyze infectious disease yearly trend

Learn how to perform confidence interval analysis

Learn how to forecast infectious disease rate using time series decomposition model

Learn how to do epidemiological modeling using SIR model

Learn how to perform public health policy evaluation

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the Course

Lecture 2: Table of Contents

Lecture 3: Whom This Course is Intended for?

Chapter 2: Tools, IDE, and Datasets

Lecture 1: Tools, IDE, and Datasets

Chapter 3: Introduction to Biostatistics

Lecture 1: Introduction to Biostatistics

Chapter 4: Calculating Infectious Disease Transmission with SIR Model

Lecture 1: Calculating Infectious Disease Transmission with SIR Model

Chapter 5: Factors That Accelerate the Spread of Infectious Disease

Lecture 1: Factors That Accelerate the Spread of Infectious Disease

Chapter 6: Setting Up Google Colab IDE

Lecture 1: Setting Up Google Colab IDE

Chapter 7: Finding & Downloading Infectious Disease Dataset From Kaggle

Lecture 1: Finding & Downloading Infectious Disease Dataset From Kaggle

Chapter 8: Project Preparation

Lecture 1: Uploading Infectious Disease Dataset to Google Colab

Lecture 2: Quick Overview of Infectious Disease Dataset

Chapter 9: Cleaning Infectious Disease Dataset by Removing Missing Values & Duplicates

Lecture 1: Cleaning Infectious Disease Dataset by Removing Missing Values & Duplicates

Chapter 10: Detecting Potential Outliers with Z Score

Lecture 1: Detecting Potential Outliers with Z Score

Chapter 11: Finding Correlation Between Population & Disease Rate

Lecture 1: Finding Correlation Between Population & Disease Rate

Chapter 12: Analyzing Infected Patients Demographics

Lecture 1: Analyzing Infected Patients Demographics

Chapter 13: Mapping Infectious Disease per County with Heatmap

Lecture 1: Mapping Infectious Disease per County with Heatmap

Chapter 14: Analyzing Infectious Disease Yearly Trend

Lecture 1: Analyzing Infectious Disease Yearly Trend

Chapter 15: Performing Confidence Interval Analysis

Lecture 1: Performing Confidence Interval Analysis

Chapter 16: Forecasting Infectious Disease Rate with Time Series

Lecture 1: Forecasting Infectious Disease Rate with Time Series

Chapter 17: Epidemiological Modelling with SIR Model

Lecture 1: Epidemiological Modelling with SIR Model

Chapter 18: Public Health Policy Evaluation

Lecture 1: Public Health Policy Evaluation

Chapter 19: Conclusion & Summary

Lecture 1: Conclusion & Summary

Instructors

Christ Raharja
Ex Technology Risk Consultant, and E-commerce enthusiast

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 2 votes

4 stars: 5 votes

5 stars: 6 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!