Data Science Fundamentals in R E-Learning Training

Bestel deze unieke E-Learning cursus Data Science Fundamentals in R online, 1 jaar 24/ 7 toegang tot rijke interactieve video’s, spraak, voortgangsbewaking door rapportages en testen per hoofdstuk om de kennis direct te toetsen.

R is een van de meest gebruikte tools voor datawetenschap. Dit pad van cursussen omvat veelvoorkomende taken die nodig zijn voor datawetenschappers die R gebruiken: data-ruzie, debuggen, defensief programmeren en domeinspecifieke taalintegratie.

Cursusinhoud

Introduction

Course: 11 Minutes

• Course Introduction
• What is Data Science?
• Examples of Data Science
• Sources of Data for Learning

Important R Basics

Course: 28 Minutes

• Data Frames
• The Structure Function str
• Summary Statistics
• Import JSON Data
• Foreach Looping

Data Management

Course: 32 Minutes

• Reshaping Data
• Merging Data
• Transposing Data
• Aggregating Data
• Basic Imputation
• Linear Fitted Imputation
• Categorize Continuous Variables

Data Analysis

Course: 45 Minutes

• Modeling for Data Science
• Linear Modeling
• Analysis of Variance
• The R coef Function
• The R fitted Function
• The R residuals Function
• The Variance-Covariance Matrix
• Confidence Intervals
• Fitting Generalized Linear Models
• Plotting Linear Models
• T-Test
• The TukeyHSD Test
• The predict Function

Time Series

Course: 8 Minutes

• Time Series
  The R forecast package

Practice: Data Science Fundamentals in R

Course: 15 Minutes

• Exercise: Using R for Data Science

Introduction

Course: 5 Minutes

• Course Introduction
• Supervised and Unsupervised Learning

Clustering

Course: 34 Minutes

• Multidimensional Scaling
• Hierarchical Clustering
• Hierarchical Clustering with corclust
• K-Means Clustering
• Selecting K for kmeans Clustering
• Clustering Large Applications (Clara)
• Fuzzy C-Means Clustering

Classification and Regression

Course: 1 Hour, 11 Minutes

• Classification Trees with rpart in R
• Regression Tree with rpart
• Classification Trees with the tree Package
• Regression Trees with the tree Package
• K-Nearest Neighbor Classification
• The randomForest Package
• Combining Random Forests
• Random Forests for Proximity Classification
• Partitioning Around Medoids (PAM)
• Naive Bayes Classifier
• Linear Discriminant Analysis (LDA)
• Quadratic Discriminant Analysis (QDA)
• Mixture Discriminant Analysis (MDA)
• Support Vector Machines (SVMs)
• Loess Regression
• Partial Least Squares Regression (PLS)
• Smoothing Splines

Boosting and Bagging

Course: 11 Minutes

• AdaBoost in R
• Bagging

Advanced Visualizations

Course: 15 Minutes

• Scatterplot Matrix in R
• Overlay Density Plots
• Scatterplot 3D Visualization in R

Practice: Machine Learning Examples in R

Course: 15 Minutes

• Exercise: Statistical analysis in R