Business Analytics

PRELUDE AND DISCLAIMER

CHAPTER 1: INTRODUCTION TO R PROGRAMMING
What is R Programming? What is R Studio?
How to download R Programming and R Studio Desktop?
How to use the interface?
     What are the different panes of the interface?
     What is a RMarkdown file? How will this file type be used?
     How To create a new RMarkdown file? How to knit to html?
     What is the working directory?
     What is a chunk? How to add a chunk?
     What is a variable? How to assign a single value to a variable object?
     Object Naming Rules and Recommendations
     Installing packages and loading libraries
     Use the package “Swirl”, from SwirlStats, to learn more about R and R Studio
Common Issues
Demo Video, Chapter 1: R Programming and R Studio | Loading a Variable
Exercise using R Studio: Getting acquainted with the tool
Chapter 1: Code to Know

CHAPTER 2: INTRODUCTION TO BUSINESS ANALYTICS
What will this text cover regarding Business Analytics?
The main ingredient: Data
Big Data
Data types and structures
R Programming Operators
Using R Studio to load and display data
     Object Naming Rules and Recommendations
     Loading a Comma delimited file (.CSV) into R Studio as a data frame object:
The data frame prefix: $ operator
Write data to a csv (saved to your working directory)
     To load a single-value variable into R Studio:
     To load a vector into R Studio:
     To load a logical indexing vector into R Studio:
     Creating a data frame from vectors
Cleaning your Environment Pane Workspace
Demo Video, Chapter 2: Loading in a Microsoft Excel file | Changing a Data Type
Chapter 2: Code to Know

CHAPTER 3: CLEANING AND MANIPULATION
Extract, Transform, load | ETL
Data Cleaning and Manipulation
Displaying and changing a value | Rows comma columns: [Row(s) , Column(s)]
Checking data types
Changing variable data types: both variable object and within a data frame object
     Changing a data type for a variable object:
     Changing a data type for a variable within a data frame object:
Displaying a column or row name
Changing a column name
Changing multiple values in a column | Find and Replace
Creating a new column in a data frame
     From a vector or logical index vector
     Based on derived values or calculation
     Removing a new column
Analyzing missing values or NA: Building blocks of code
Sorting and ordering
Creating a subset, or sample
aggregate
Demo Video, Chapter 3: Data Cleaning | Data Manipulation
Chapter 3: Code to Know

CHAPTER 4: DESCRIPTIVE ANALYTICS
Descriptive Analytic: What? When? Why? Where? Who? and How?
Common Commands in R Studio
     NROW Number of rows | NCOL, number of columns
     Head and Tail
     Unique Data (Distinct)
     Statistical Commands using R
     Table: Frequency Distribution | Extract Mode Result
     Tapply: Contigency table
Using a measure to replace a value
Plotting Basic Visualizations using Out-of-the-Box Commands
Demo Video, Chapter 4: Descriptive Analytics using R Studio
Chapter 4: Code to Know

CHAPTER 5: PREDICTIVE ANALYTICS, REGRESSION BASICS
Predictive Analytics, Regression
Linear regression
     Correlation Coefficient | cor()
     Run the Model
     Building the Regression Equation
     Solving for Y [Estimated Regression Equation]
     Confidence Intervals
     Hypothesis Testing using the Summary Results
Multiple regression problem
Demo Video, Chapter 5: Predictive Analytics, Regression using R Studio
Chapter 5: Code to Know

CHAPTER 6: PREDICTIVE ANALYTICS, CLUSTERING BASICS
Predictive Analytics, Clustering
     K-Means Clustering Model
     Example of Interpretation
Demo Video, Chapter 6: Predictive Analytics, KMeans Clustering using R Studio
Chapter 6: Code to Know

CHAPTER 7: PREDICTIVE ANALYTICS, CLASSIFICATION BASICS
Predictive Analytics, Classification
     Classification using Decision Trees
Demo Video, Chapter 7: Predictive Analytics, Classification using R Studio
Chapter 7: Code to Know

CHAPTER 8: PRESCRIPTIVE ANALYTICS, OPTIMIZATION BASICS
Prescriptive Analytics, Optimization
General Formulation Steps
Creating a matrix
Installing the lpsolve package and loading the library
Linear Programming using lpSolve
     Analyzing the Results
Demo Video, Chapter 8: Prescriptive Analytics, Optimization using R Studio
Chapter 8: Code to Know

CHAPTER 9: PRESCRIPTIVE ANALYTICS, SIMULATION BASICS
Prescriptive Analytics, Simulation
     Analyzing the Results in R Studio
     Model for inventory
     Analyzing the results
     Full Code for model
Demo Video, Chapter 9: Prescriptive Analytics, Simulation using R Studio
Chapter 9: Code to Know

APPENDIX: CODE BY CHAPTER
Chapter 1: Introduction to R Programming
Chapter 2: Introduction to Business Analytics
Chapter 3: Cleaning and Manipulation
Chapter 4: Descriptive Analytics
Chapter 5: Predictive Analytics, Regression Basics
Chapter 6: Predictive Analytics, Clustering Basics
Chapter 7: Predictive Analytics, Classification Basics
Chapter 8: Prescriptive Analytics, Optimization Basics
Chapter 9: Prescriptive Analytics, Simulation Basics

APPENDIX: REALESTATE RAW DATA