The Data Analytics with R Programming Certification Training course equips learners with the knowledge and tools to perform powerful data analysis using R, one of the most popular programming languages for statistical computing and graphics. This course is ideal for aspiring data analysts, statisticians, data scientists, and professionals in analytics. With hands-on projects, practical exercises, and in-depth instruction, learners gain expertise in data manipulation, visualization, and statistical modeling using R.

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Instructor led live online Classes

Data Analytics with R Programming Certification Training

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the Data Analytics with R Programming Course?

Master In-Demand Skills: Learn how to handle, analyze, and visualize data using R.
Boost Your Career: Certification is valued in analytics, business intelligence, and data science roles.
Hands-On Learning: Work on real-life datasets and industry projects.
Foundational and Advanced Coverage: From R basics to statistical modeling and machine learning.
Recognized Credential: Showcase your analytics proficiency using R.

Course Description

This course builds your understanding of descriptive and inferential statistics, probability theory, hypothesis testing, and data distributions used in analytics.

1. Aspiring Data Analysts and Scientists 2. Business Intelligence Professionals 3. Researchers and Academic Professionals 4. Anyone preparing for data-focused certifications or roles

1. Strengthens analytical reasoning through concepts, formulas, and case studies. 2. Prepares you for data science, machine learning, and analytics programs. 3. Includes quizzes, assignments, and instructor support.

What you'll learn

Introduction to R Programming: Data types, variables, loops, and functions.

Data Wrangling with dplyr and tidyr.

Data Visualization with ggplot2.

Statistical Analysis: Regression, hypothesis testing, and ANOVA.

Exploratory Data Analysis (EDA).

Machine Learning with R: Decision trees, clustering, and classification.

R Markdown and Reporting.

Requirements

Basic understanding of mathematics and statistics.
No prior programming experience required.

Curriculum Designed by Experts

Introduction to terms Business Intelligence, Business Analytics, Data, Information
How information hierarchy can be improved/introduced
Understanding Business Analytics and R
Knowledge about the R language, its community, and its ecosystem
Understand the use of 'R' in the industry
Compare R with other software in analytics
Install R and the packages useful for the course
Perform basic operations in R using the command line
Learn the use of IDE R Studio and Various GUI
Use the ‘R help’ feature in R
Knowledge about the worldwide R community collaboration

Install R and related packages
R operations using command line

Various kinds of data types in R and their appropriate uses
Built-in functions in R like seq(), cbind (), rbind(), merge()
Knowledge of the various subsetting methods
Summarize data by using functions like: str(), class(), length(), nrow(), ncol()
Use of functions like head(), tail() for inspecting data
Indulge in a class activity to summarize data
dplyr package to perform SQL join in R

Data Types in R
R Functions: seq(), cbind (), rbind(), merge(). str(), class(), length(), nrow(), ncol() head(), tail()
SQL joins in R

Steps involved in Data Cleaning
Functions used in Data Inspection
Tackling the problems faced during Data Cleaning
Uses of the functions like grepl(), grep(), sub(), Coerce the data, uses of the apply() functions

Data Cleaning in R
Data Inspection in R
Data Coercion in R

Import data from spreadsheets and text files into R
Import data from other statistical formats like sas7bdat and spss, packages
Installation used for database import
Connect to RDBMS from R using ODBC
Basic SQL queries in R
Basics of Web Scraping

Database import
Connect to RDBMS using R
SQL queries in R
Web Scraping in R

Exploratory Data Analysis(EDA)
Implementation of EDA on various datasets
Boxplots
Understanding the cor() in R
EDA functions like summarize(), llist()
Multiple packages in R for data analysis
Fancy plots like the Segment plot, and HC plot in R

EDA using R
cor() in R
Plotting fancy graphs using R

Data Visualization
Graphical functions present in R
Plot various graphs like tableplot, histogram, and Boxplot
Customizing Graphical Parameters to improvise plots
Understanding GUIs like Deducer and R Commander
Introduction to Spatial Analysis

Graphical Functions in R
Customizing Plots using R
Spatial Analysis

Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K-means Clustering

Data Mining
Machine Learning Algorithms: Supervised Unsupervised

Association Rule Mining
User Based Collaborative Filtering (UBCF)
Item Based Collaborative Filtering (IBCF)

Association Rule Mining
Recommender Engines: User Based Collaborative Filtering (UBCF) Item Based Collaborative Filtering (IBCF)

Linear Regression
Logistic Regression

Linear Regression algorithm
Logistic Regression algorithm

Analysis of Variance (Anova) Technique
Sentiment Analysis: fetch, extract and mine live data from Twitter

Anova
Sentiment Analysis

Decision Tree
Entropy
Gini Index
Pruning and Information Gain
Algorithm for creating Decision Trees
Bagging of Regression and Classification Trees
Random Forest
Working on Random Forest
Features of Random Forest, among others

Decision Tree algorithm
Working of Random Forest

Analyze census data to predict insights on the income of the people based on the factors like age, education, work class, and occupation using Decision Trees
Logistic Regression and Random Forest
Analyze the Sentiment of Twitter data, where the data to be analyzed is streamed live from Twitter, and sentiment analysis is performed on the same

Decision Trees
Logistic Regression
Random Forest
Sentiment Analysis

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Data Analytics with R Programming Certification Training

Read Review

Instructor led live online Classes

Data Analytics with R Programming Certification Training