Data Science is an arena of reconstructing unstructured data to a structured model and confer it into knowledge. There are several tools and programming languages that are used in this process, and R is one such effective and required programming language for this purpose. Data Science R Training is one such training program that will help you to learn R Programming for Data Science from basic to advance level.

This training program will let you learn how to use variables, matrix, functions and other models of R programming required for Data Science. At advance level, you learn to use R programming language in collaboration of Machine learning algorithm for Data Science.

Prerequisites

This course has no specific prerequisites.

Fundamental knowledge in any high-level programming language (preferably R) is ideal but not required.

Basic knowledge of statistics would be considered as an added advantage.

Basic knowledge of computer hardware and software is ideal but not required.

What will you gain after this course

Through this course, you can stay on top of others in the talent race.

You will be recognized as a professional Data Scientist.

You will be recognized as an R Programmer, as well.

With the help of this course, you will be recognized as a Business intelligence (BI) expert.

Course Outline

Day One: Language Basics

Course Introduction

About Data Science

Data Science Definition

Process of Doing Data Science.

Introducing R Language

Variables and Types

Control Structures (Loops / Conditionals)

R Scalars, Vectors, and Matrices

Defining R Vectors

Matricies

String and Text Manipulation

Character data type

File IO

Lists

Functions

Introducing Functions

Closures

lapply/sapply functions

DataFrames

Labs for all sections

Day Two: Intermediate R Programming

DataFrames and File I/O

Reading data from files

Data Preparation

Built-in Datasets

Visualization

Graphics Package

plot() / barplot() / hist() / boxplot() / scatter plot

Heat Map

ggplot2 package (qplot(), ggplot())

Exploration With Dplyr

Labs for all sections

Day Three: Advanced Programming With R

Statistical Modeling With R

Statistical Functions

Dealing With NA

Distributions (Binomial, Poisson, Normal)

Regression

Introducing Linear Regressions

Recommendations

Text Processing (tm package / Wordclouds)

Clustering

Introduction to Clustering

KMeans

Classification

Introduction to Classification

Naive Bayes

Decision Trees

Training using caret package

Evaluating Algorithms

R and Big Data

Connecting R to databases

Big Data Ecosystem

Labs for all sections

DATA SCIENCE R TRAINING SINGAPORE.