Mastering R Programming

Curriculum:

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
• Tracking 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
• Boxplots
• 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 Visualization in R:

• Understanding on Data Visuapzation
• Graphical functions present in R
• Plot various graphs pke tableplot
• Histogram
• Boxplot
• 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:

• Anova
• 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.

Project:

• Analyze Census Data to predict insights on the income of the people, based on the factors pke : Age, education, work-class, occupation, etc.