Nex-G Innovations

Introduction to Business Analytics

• Understand Business Analytics and R
• Knowledge on the R language
• 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

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(), ncol()
• Use of functions like 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
• Tackling the problems faced during Data Cleaning
• Uses of the functions like grepl(), grep(), sub()
• Coerce the data
• Uses of the apply() functions

Data Import Techniques in R

• This module tells you about the versatility and robustness of R which can take-up data in a variety of formats
• be it from a csv file to the data 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

Exploratory Data Analysis

• Understanding the 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
• The Fancy plots like Segment plot
• HC plot in R

Data Visualization in R

• Understanding on Data Visualization
• Graphical functions present in R
• Plot various graphs like tableplot
• histogram
• boxplot
• Customizing Graphical Parameters to improvise the plots
• Understanding GUIs like 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

Linear and Logistic Regression

• Linear 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 like : Age, education, work-class, occupation, etc.