Data Science with R Training

Live Online & Classroom Enterprise Training

Data Science with R focuses on using the R programming language for data analysis and statistical modeling. It covers data manipulation, visualization, and building predictive models for insights.

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What is Data Science with R Training about?

This course introduces learners to data science using R, one of the most widely used programming languages for statistics and analytics. Participants will gain hands-on experience with R programming, data manipulation, visualization, and applying statistical and machine learning techniques. Through practical exercises with real-world datasets, learners will develop skills to analyze, interpret, and communicate data insights effectively.

What are the objectives of Data Science with R Training ?

Understand R programming basics for data science.
Manipulate and clean data using R libraries like dplyr and tidyr.
Visualize insights using ggplot2 and other visualization tools.
Apply statistical methods for data analysis.
Build and evaluate machine learning models in R.

Who is Data Science with R Training for?

Beginners aspiring to enter data science.
Students and graduates pursuing analytics careers.
Business Analysts wanting to upgrade to Python.
IT professionals transitioning into data roles.
Professionals interested in machine learning applications.
Students and beginners starting in data science.
Statisticians transitioning to programming-based analytics.
Business Analysts working with data-driven decisions.
IT professionals exploring data science roles.
Researchers needing advanced statistical analysis.

What are the prerequisites for Data Science with R Training?

Prerequisites:

Basic programming or scripting knowledge.
Understanding of statistics and probability.
Familiarity with data formats (CSV, Excel).
Problem-solving and analytical thinking skills.
Interest in statistical computing and analytics.

Learning Path:

Introduction to R and RStudio environment.
Data manipulation with dplyr and tidyr.
Data visualization using ggplot2.
Statistical analysis and hypothesis testing.
Machine learning basics with caret and other R libraries.

Related Courses:

Introduction to Statistics for Data Science
Data Visualization with R and ggplot2
Machine Learning with R
SQL for Data Science

Available Training Modes

Live Online Training

5 Days

Course Outline Expand All

Expand All

Module 1- Business Decisions and Analytics

Objectives

Business Decisions

Types of Analytics

Applications of Business Analytics

Module 2- Introduction to R Programming

Objectives

R for Data Analytics

Data Types and Variables

Operators in R

Conditional Statements in R

Loops in R

Functions in R

Module 3- Data Structures

Objectives

Working with the Data

Assigning Values to Data Structures

Manipulating Data

Module 4- Data Visualization

Objectives

Introduction to Data Visualization

Data Visualization in R

ggplot2

File Formats of Graphics Outputs

R Programming

Module 5- Statistics-I

Objectives

Hypothesis

Data Sampling

Confidence and Significance Levels

Module 6- Statistics-II

Objectives

Hypothesis Test

Hypothesis Test about Population Means

Module 7- Regression Analysis

Objectives

Regression

Types of Models

Linear Regressions

Data Preparation

Simple Linear Regression

Non-Linear Regression

Non-linear to Linear Models

Principal Component Analysis (PCA)

Module 8- Classification

Objectives

Introduction to Classification

Logistic Regression

Demo: Logistic Regression

Support Vector Machines

K-Nearest Neighbours (KNN)

K Nearest Neighbors

Naive Bayes

Decision Trees and Random Forest Classification

Random Forest

Evaluating Classifier Models

Module 9- Clustering

Objectives

Clustering and Its Applications

K-means Clustering

K-means

Hierarchical Clustering

Density-based Clustering

Module 10- Association

Objectives

Rules for Association

Apriori Algorithm

Apriori

Who is the instructor for this training?

The trainer for this Data Science with R Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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