Visão Geral

O Curso Developer R Advanced, ensina aos alunos habilidades mais sofisticadas de R, incluindo o uso de expressões regulares avançadas, aprendizado de máquina, modelagem de efeitos aleatórios, Inferência Bayesiana, séries temporais R avançadas e muito mais.

Objetivo

Após realizar este Curso Developer R Advanced você será capaz de:

• Use expressões regulares avançadas em R
• Aplicar técnicas avançadas de dados perdidos
• Trabalhe com séries temporais R avançadas
• Use data.table para big data
• Trabalhe com modelos lineares
• Estenda R para análise de tempo para eventos e sobrevivência
• Trabalhe com Inferência Bayesiana usando R

Materiais

Conteúdo Programatico

Advanced Regular Expressions in R

1. Using Perl-Style Regular Expressions in R

Machine Learning Approaches in R

1. Pre-processing Data
2. Feature Selection
3. Supervised Learning:
4. Classification Models
5. Regression Models
6. Unsupervised Learning:
7. Clustering

Advanced Missing Data Techniques

1. Understanding the different types of Missing Data
2. Implications for Analysis
3. The AMELIA package
4. Multiple Imputation

Advanced R Time Series

1. The ts class
2. The zoo package
3. The xts class
4. Lubridate for advanced date handling
5. Autocorrelation Plots
6. Seasonal, trend, and noise plots
7. Financial Charting with R

Using data.table for Big Data

1. Why do we need data.table?
2. Why is it
3. The i and the j arguments in data.table
4. Merging data with data.table
5. Group-by functions with data.table
6. Using data.table in functions

Generalized Linear Models

1. Logistic Regression
   
2. Poisson Regression
3. Gamma Regression

Extend R to Time to Event or Survival Analyses

1. Visualizing Hazards Across Time
2. Understanding the Log Rank Test
3. Cox Proportional Hazards Modeling
4. Understand Time Varying Covariates
5. Understand Time Dependent Covariates
6. Understanding the Hazard Ratio
7. Implement Frailty Models for Clustered Data
8. Parametric Survival Models
9. Weibull Model
10. Exponential Model
11. Predicting Failure Times

Random Effects Modeling in Linear Regression

1. Random effects introduction
2. Covariance structures
3. Interpreting random effects in models
4. Longitudinal Data
5. Clustered Data
6. Prediction in Random Effects

Extension: Random Effects Modeling in Logistic Regression

1. Random effects introduction
2. Covariance structures
3. Interpreting random effects in models
4. Marginal versus Conditional Models
5. Stratified Logistic regression
6. GEE Models in Logistic Regression

Bayesian Inference Using R

1. Linear model
2. Logistic Model
3. Random Effects / Fixed effects model