Module 1: Introduction to Business Analytics
Introduction to terms like Business Intelligence, Business Analytics, Data,
How information hierarchy can be improved/introduced.
Understanding Business Analytics and R.
Knowledge about the R language, its community and 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 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.
Module 2: Introduction to R Programming :
The various kinds of data types in R and its appropriate uses.
The built-in functions in R like: seq(), cbind (), rbind(), merge().
Knowledge on the various subsetting methods.
Summarize data by using functions like: str(), class(), length(), nrow(),
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
Module 3: Data Manipulation in R :
The various 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.
Module 4: Data Import Techniques 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 and basic SQL queries in R.
Basics of Web Scraping.
Module 5: Exploratory Data Analysis :
Understanding the Exploratory Data Analysis(EDA).
Implementation of EDA on various datasets.
Boxplots, whiskers of Boxplots.
Understanding the cor() in R.
EDA functions like summarize(), llist()
Multiple packages in R for data analysis
Fancy plots like the Segment plot, HC plot in R.
Module 6: Data Visualization in R :
Understanding on Data Visualization.
Graphical functions present in R
R Programming Certification TOC :
Plot various graphs like tableplot, histogram, Boxplot.
Customizing Graphical Parameters to improvise plots.
Understanding GUIs like Deducer and R Commander
Introduction to Spatial Analysis.
Module 7: Data mining: clustering techniques :
Introduction to Data Mining
Concept of Machine Learning
Understanding Supervised Machine Learning algorithms.
Understanding unsupervised Machine Learning Algorithms
Module 8: Data Mining: Association rule mining and Sentiment analysis :
Association Rule Mining.
User Based Collaborative Filtering (UBCF)
Item Based Collaborative Filtering (IBCF)
Module 9: Linear and Logistic Regression :
What is Regression.
Module 10: Annova and Predictive Analysis and Data Mining :
Decision Trees and Random forest
Decision Tree, the 3 elements for classification of a Decision Tree
Pruning and Information Gain.
Bagging of Regression and Classification Trees
Concepts of Random Forest
Working of Random Forest, features of Random Forest, among others