Ementorhub

What you'll learn

Understand the role of predictive analytics for prevention, diagnosis, and effectiveness

Explain key concepts in prediction modelling: appropriate study design, adequate sample size and overfitting

Understand important issues in model development, such as missing data, non-linear relations and model selection

Know about ways to assess model quality through performance measures and validation

Skills you'll gain

Predictive Analytics

Sampling (Statistics)

Data Validation

Feature Engineering

Predictive Modeling

Sample Size Determination

R Programming

Statistical Modeling

Regression Analysis

Risk Modeling

Statistical Inference

Data Manipulation

Statistical Machine Learning

There are 5 modules in this course

Furthermore, we comprehensively discuss important modelling issues such as missing values, non-linear relations and model selection. The importance of the bias-variance tradeoff and its role in prediction is also addressed. Finally, we look at various way to evaluate a model - through performance measures, and by assessing both internal and external validity. We also discuss how to update a model to a specific setting. Throughout the course, we illustrate the concepts introduced in the lectures using R. You need not install R on your computer to follow the course: you will be able to access R and all the example datasets within the Coursera environment. We do however make references to further packages that you can use for certain type of analyses – feel free to install and use them on your computer. Furthermore, each module can also contain practice quiz questions. In these, you will pass regardless of whether you provided a right or wrong answer. You will learn the most by first thinking about the answers themselves and then checking your answers with the correct answers and explanations provided. This course is part of a Master's program Population Health Management at Leiden University (currently in development).

Prediction for prevention, diagnosis, and effectiveness

Modeling Concepts

Model development

Model validation and updating