R Stats and Data Mining

RapidMiner Studio

SAS Enterprise Miner

Statistical predictive modelling in R. Lessons that avoid in-depth statistics, made as short and simple as possible, and yet cover topics of data analysis and visualisation, variable association, models such as k-NN, naive bayes, simple and multiple linear regression.

- R Introduction: Setup and Basic Data Analysis, 2016
- R Introduction: Data Analysis and Plotting, 2016
- R Introduction: Working With Google Maps, 2016
- R Introduction: Extending Google Maps with k-NN, 2016

- R Stats: Variable Association, 2016
- R Stats: Naive Bayes and k-NN, 2016
- R Stats: Data Prep and Imputation of Missing Values, 2016
- R Stats: Imputation with no Magic, 2016
- R Stats: Simple Regression Model, 2016
- R Stats: Multiple Regression - Variable Selection, 2016
- R Stats: Multiple Regression - Variable Preparation, 2016
- R Stats: Multiple Regression - Data Visualisation, 2016

Machine learning and data mining in RapidMiner Studio. These will include linear and logistic regression, neural networks, decision trees, cluster analysis, text analytics, as well as, their ensembles, evaluation and comparison.

- RapidMiner: Setup and Project Repository, 2016
- RapidMiner Stats: Basics and Loading Data, 2016
- RapidMiner Stats: Simple Data Exploration, 2016
- RapidMiner Stats: Working with Attributes, 2016
- RapidMiner Stats: Working with Aggregates, 2016
- RapidMiner Stats: Boxplots, 2016
- RapidMiner Stats: Histograms, 2016
- RapidMiner Stats: Cumulative Frequency Distribution, 2016
- RapidMiner Stats: Cumulative Relative Frequency, 2016

- RapidMiner Classification: Introduction and Business Case, 2017
- RapidMiner Classification: Model Creation and Application, 2017
- RapidMiner Classification: Training Performance, 2017
- RapidMiner Classification: Holdout Validation, 2017
- RapidMiner Classification: Cross Validation, 2017

Introduction to SAS Enterprise Miner.