Analytics using R Programming



Analytics using R Programming: Data Analytics Using R

Analytics using R Programming: What is Data Analytics

Who uses R and how.

What is R

Why to use R

R products

Get Started with R

Introduction to R Programming

Different data types in R and when to use which one

Function in R

Various subsetting methods.

Summarizing the data using str(), class(), nrow(), ncol() and length()

Use functions like head() and tail() for inspecting data

Indulge into a class activity to summarize the data.

Data Manipulation in R

Know the various steps involved in data cleaning

Functions used for data inspection

Tacking the problem faced during data cleaning

How and when to use functions like grep, grepl, sub, gsub, regexpr, gregexpr, strsplit

How to coerce the data.

Apply family functions.

Data Import Technique in R

Import data from spreadsheets and text files into R

Install packages used for data import

Connect to RDBMS from R using ODBC and basic sql queries in R

Perform basic web scrapping.

Data Exploration in R

What is data exploration

Data exploring using Summary(), mean(), var(), sd(), unique()

Using Hmisc package and using summarize, aggregate function

Learning correlation and cor() function and visualizing the same using corrgram

Visualizing data using plot and its different flavours


Dist function

Data Visualization in R

Gain understanding on data visualization

Learn the various graphical functions present in R

Plot various graph like tableplot, histogram, boxplot etc.

Customize graphical parameters to improvise the plots.

Understand GUIs like Deducer and R commander

Introduction to spatial analysis.

Data Mining : Clustering Techniques

Introduction to data mining

Understand machine learning

Supervised and unsupervised machine learning algos

K means clustering

Data Mining : Association Rules Mining and Sentiment Analysis

Understanding associate rule mining

Understanding sentiment analysis

Linear and Logistic Regression

Understand linear regression

Understand logistic regression

Annova and Predictive Regression

Understand Annova

Understand predictive regression

Data Mining : Decision Tree and Random Forest

Understand what is Decision Tree

Algos for Decision Tree

Greedy approach : Entropy and information gain.

A perfect decision tree

Understand the concept of random forest

How random forest work

Features of random forest

Duration & Timings :

Duration – 30 Hours.

Training Type: Instructor Led Live Interactive Sessions.

Faculty: Experienced.

Weekday Session – Mon – Thu 8:00 PM to 10:00 PM (EST) – 4 Weeks. June 12, 2024.

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