R Essential Training Part 2: Modeling Data

Description

In this course, you will :

• This training series provides an in-depth introduction to R, including detailed instructions for using R and RStudio as well as hands-on examples ranging from exploratory graphics to neural networks.
• demonstrates how to compute statistics, analyse data, forecast outcomes, and group and classify cases These are the fundamental techniques you'll need to generate actionable insights for your company.

Syllabus :

1. R for Data Science

• Data science with R: A case study

 

2. Exploring Data

• Computing frequencies
• Computing descriptive statistics
• Computing correlations
• Creating contingency tables
• Conducting a principal component analysis
• Conducting an item analysis
• Conducting a confirmatory factor analysis

 

3. Analyzing Data

• Comparing proportions
• Comparing one mean to a population: One-sample t-test
• Comparing paired means: Paired samples t-test
• Comparing two means: Independent samples t-test
• Comparing multiple means: One-factor analysis of variance
• Comparing means with multiple categorical predictors: Factorial analysis of variance

 

4. Predicting Outcomes

• Predicting outcomes with linear regression
• Predicting outcomes with lasso regression
• Predicting outcomes with quantile regression
• Predicting outcomes with logistic regression
• Predicting outcomes with Poisson or log-linear regression
• Assessing predictions with blocked-entry models

 

5. Clustering and Classifying Cases

• Grouping cases with hierarchical clustering
• Grouping cases with k-means clustering
• Classifying cases with k-nearest neighbors
• Classifying cases with decision tree analysis
• Creating ensemble models with random forest classification

Show more