Introduction to terms Business Intelligence Business Analytics Data Information

How information hierarchy can be improved/introduced

Understanding Business Analytics and R

Knowledge about the R language its community and its 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 the 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

Various kinds of data types in R and their appropriate uses

Built-in functions in R like seq() cbind () rbind() merge()

Knowledge of 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

dplyr package to perform SQL join in R

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

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

Basic SQL queries in R

Basics of Web Scraping

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

Fancy plots like the Segment plot and HC plot in R

Data Visualization

Graphical functions present in R

Plot various graphs like tableplot histogram and Boxplot

Customizing Graphical Parameters to improvise plots

Understanding GUIs like Deducer and R Commander

Introduction to Spatial Analysis

Introduction to Data Mining

Understanding Machine Learning

Supervised and Unsupervised Machine Learning Algorithms

K-means Clustering

Association Rule Mining

User Based Collaborative Filtering (UBCF)

Item Based Collaborative Filtering (IBCF)

Linear Regression

Logistic Regression

Analysis of Variance (Anova) Technique

Sentiment Analysis: fetch extract and mine live data from Twitter

Decision Tree

Entropy

Gini Index

Pruning and Information Gain

Algorithm for creating Decision Trees

Bagging of Regression and Classification Trees

Random Forest

Working on Random Forest

Features of Random Forest among others

Analyze census data to predict insights on the income of the people based on the factors like age education work class and occupation using Decision Trees

Logistic Regression and Random Forest

Analyze the Sentiment of Twitter data where the data to be analyzed is streamed live from Twitter and sentiment analysis is performed on the same