Sparkacademy R training lets you learn R programming language with exercised that focus on Statistical analysis involving graphic representation, statistical analysis and reporting. We will teach you core concepts of R programming from data types, data structures, flow control statements to Functions and recursive functions. We will also cover R Programming for Data Science training, you will be taught core concepts like importing data in various formats for statistical computing, data manipulation, Data Wrangling, Data Aggregation, Business Analytics & Machine Learning algorithms and data visualization.

## What will you learn in this R Programming training?

- Data Science concepts of R and functions
- Creating pie charts, line charts, box plots and vectors
- Performing sorting, analyze variance and the cluster
- Machine Learning Models Linear Regression and Logistic Regression
- Database connectivity to SQLite
- Deploying R programs to Hadoop

Core Programming Principles

- Types of variables
- Logical variables and operators
- While loop
- For loop
- If statement

Fundamentals of R

- Vectors
- Vectorized operations
- Functions in R
- Packages in R

Matrices

- Build Matrix
- Matrix Operations
- Visulaization using Matplot

Data Frames

- Importing Data in to R
- Exploring Dataset
- Filtering Dataset
- Visualization with Qplot

Advanced Visualization with GGPlot2

- Movies Ratings Exercise with GGPlot2
- Factor
- Aesthetics
- Plotting with Layers
- Mapping and Settings
- Histograms and Density Charts
- Statistical Transformations
- Facets

Data Preparation

- FVT example
- gsub(), sub()
- Dealing with missing Data
- Data Filters for missing Data
- Removing records with missing Data
- Missing Data: Factual Analysis Method
- Missing Data: Deriving Values Method

List

- Handling Data-Times in R
- What is a List
- Extract components list
- adding/deleting components
- Subsetting a list

Functions

- Apply() function
- lapply()
- sapply()
- which.max() and which.min()

Machine Learning Models

We will cover simple Linear Regression, equations for line, slope, Y-intercept regression line, deploying analysis using Regression, least square criterion, analyzing results, and standard error to estimate the accuracy of the model.

- Linear Regression Model
- Analyze relationship with Regression: we will plot scatter plots, variable relationship such as Co-variance, simple Linear Regression and Best line fit.
- Deep understanding of the measure of variation, coefficient if determination, F-test with F-distribution and advanced Prediction linear regression.
- Logistic Regression Model
- Exercise Predictions using Logistic Regression, model evaluation, understanding sensitivity and specificity, confusion matrix, ROC, plot illustrating binary classifier system and ROC curve to determine sensitivity trade-offs for binary classifier.
- Random Forest Classifier Model
- Building dataframe for Random Forest Classifier
- Training Rows
- Performing Predictions
- Error evaluation
- Making Graphs from Data
- Comparing the Random Forest Model with SVM