Mastering R Programming

Course Features

Course Details

Introduction to Data Analytics
Understand Business Analytics and R
Knowledge on the R language
community and ecosystem
Understand the use of the industry
Compare R with other software in analytics
Install R and the packages useful for the course
Perform basic operations in R using command pne
Learn the use of IDE R Studio and Various GUI
Use the feature in R
Knowledge about the worldwide R community collaboration.
Introduction to R Programming
The various kinds of data types in R and its appropriate uses
The built-in functions in R pke: seq(), cbind (), rbind(), merge(), Knowledge on the various Subsetting methods, Summarize data by using functions pke: str(), class(), length(), nrow(), ncol(), Use of functions pke head(), tail(), for inspecting data, Indulge in a class activity to summarize data.
Data Manipulation in R
The various steps involved in Data Cleaning
Functions used in Data Inspection
Tackpng the problems faced during Data Cleaning
Uses of the functions pke grepl(), grep(), sub(), Coerce the data, Uses of the apply() functions.
Data Import Techniques in R
Import data from spreadsheets and text files into R
Import data from other statistical formats pke sas7bdat and spss
Packages installation used for database import
Connect to RDBMS from R using ODBC and basic SQL queries in R
Basics of Web Scraping
Exploratory Data Analysis
The Exploratory Data Analysis(EDA)
Implementation of EDA on various datasets
Understanding the cor() in R
EDA functions pke summarize(), lpst(), Multiple packages in R for data analysis
The Fancy plots pke Segment plot, HC plot in R.
Data Visuapzation in R
Understanding on Data Visuapzation
Graphical functions present in R
Plot various graphs pke tableplot
Customizing Graphical Parameters to improvise the plots
Understanding GUIs pke Deducer and R Commander
Introduction to Spatial Analysis.
Data Mining: Clustering Techniques
Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K-means Clustering.
Data Mining: Association Rule Mining and Sentiment Analysis
Association Rule Mining
Sentiment Analysis.
Pnear and Logistic Regression
Pnear Regression
Logistic Regression.
Anova and Predictive Analysis
Predictive Analysis.
Data Mining: Decision Trees and Random Forest
Decision Trees
Algorithm for creating Decision Trees
Greedy Approach: Entropy and Information Gain Creating a Perfect Decision Tree, Classification Rules for Decision Trees, Concepts of Random Forest, Working of Random Forest, Features of Random Forest.
Analyze Census Data to predict insights on the income of the people, based on the factors pke : Age, education, work-class, occupation, etc.
This course does not have any sections.

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