• Introduction to Business Analytics
• Types of Analytics
• Case study on Walmart, Signet Bank
• Data Science and its importance

• Population and sample
• Descriptive and Inferential Statistics
• Statistical data analysis
• Variables
• Sample and Population Distributions
• Interquartile range
• Central Tendency
• Normal Distribution
• Skewness.
• Boxplot
• Five Number Summary
• Standard deviation
• Standard Error
• Emperical Formula
• Central limit theorem
• Estimation
• Confidence interval
• Hypothesis testing
• P-value
• Scatterplot and correlation coefficient
• Standard Error
• Scales of Measurements and Data Types
• Data Summarization
• Visual Summarization
• Numerical Summarization
• Outliers and Summary

Objectives:

This module introduces you to some of the important keywords in R like Business

• Intelligence, Business
• Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems.
• This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
• Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other Software in analytics, install R and its packages.

Topics:

• Business Analytics, Data, Information
• Understanding Business Analytics and R
• Compare R with other software in analytics
• Install R

• Starting and quitting R

Recording your work

• Basic features of R.
• Calculating with R
• Named storage
• Functions
• R is case-sensitive
• Listing the objects in the workspace
• Vectors
• Extracting elements from vectors
• Vector arithmetic
• Simple patterned vectors
• Missing values and other special values
• Character vectors Factors
• More on extracting elements from vectors
• Matrices and arrays
• Data frames
• Dates and times

NOTE:-

• Assignments with Datasets

Import and Export data in R

• Importing data in to R
• CSV File
• Excel File
• Import data from text table

Topics

• Variables in R
• Scalars
• Vectors
• R Matrices
• List
• R – Data Frames
• Using c, Cbind, Rbind, attach and detach functions in R
• R – Factors
• R – CSV Files
• R – Excel File

NOTE-:

• Assignments
• Business Scenerio/Group Discussion.

R Nuts and Bolts-:

• Entering Input. – Evaluation- R Objects- Numbers- Attributes- Creating Vectors- Mixing Objects-Explicit Coercion- Summary- Names- Data Frames.

• • The dplyr Package
  • Installing the dplyr package
  • select()
  • filter()
  • arrange()
  • rename()
  • mutate()
  • group_by()
  • %>%

NOTE-:

• Assignments
• Business Scenerio/Group Discussion.

Module 2.5 – Data Manipulation in R Objectives:

• In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
• Thus using and exploring the popular functions required to clean data in R.

Topics

• Data sorting
• Find and remove duplicates record
• Cleaning data
• Merging data

Statistical Plotting-:

• Bar charts and dot charts
• Pie charts
• Histograms
• Box plots
• Scatterplots
• QQ plots

NOTE:-

• Assignments with Datasets

Objectives:

• • Control Structure Programming with R
  • The for() loop
  • The if() statement
  • The while() loop
  • The repeat loop, and the break and next statements
  • Apply
  • Sapply
  • Lapply

NOTE:

• • Assignments with Datasets

Factors

• Using Factors
• Manipulating Factors
• Numeric Factors
• Creating Factors from Continuous Variables
• Convert the variables in factors or in others.

Reshaping

• Data Modifying
• Data Frame Variables
• Recoding Variables
• The recode Function
• Reshaping Data Frames
• The reshape Package

NOTE:-

• Assignments with Datasets

Explore many algorithms and models:

• Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
• Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine!

• Machine Learning Languages, Types, and Examples
• Machine Learning vs Statistical Modelling
• Supervised vs Unsupervised Learning
• Supervised Learning Classification
• Unsupervised Learning

• K-Nearest Neighbors
• Decision Trees
• Random Forests
• Reliability of Random Forests
• Advantages and Disadvantages of Decision Trees

• Regression Algorithms
• Model Evaluation
• Model Evaluation: OverfittingandUnderfitting
• Understanding Different Evaluation Models

• K-Means Clustering plus Advantages and Disadvantages
• Hierarchical Clustering plus Advantages and Disadvantages
• Measuring the Distances Between Clusters – Single Linkage Clustering
• Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
• Density-Based Clustering

• Dimensionality Reduction: Feature Extraction and Selection
• Collaborative Filtering and Its Challenges

• An Overview of Classification.
• Why Not Linear Regression?
• Logistic Regression
• The Logistic Model
• Estimating the Regression Coefficients
• Making Predictions
• Logistic Regression for 2 Response Classes
• Lab: Logistic Regression.
• The Stock Market Data
• Logistic Regression

NOTE

• Assignments with Different Datasets
• Business Scenerio/Group Discussion

• The Basics of Decision Trees
• Regression Trees
• Classification Trees
• Trees Versus Linear Models
• Advantages and Disadvantages of Trees
• Bagging, Random Forests, Boosting
• Bagging
• Random Forests
• Lab: Decision Trees
• Fitting Classification Trees
• Fitting Regression Trees

NOTE-:

• Assignments with Different Datasets.
• Business Scenerio/Group Discussion.

• Time series
• Estimating and Eliminating the Deterministic Components if they are present in the Model.
• Estimating and Eliminating Seasonality if it is present in the Model
• Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
• Identify ‘order’ of the ARMA model
• Forecast’ or Predict for Future Values
• Practise on R

• Understand when the Support Vector family of methods are an appropriate method of analysis.
• Understand what a hyperplane is and how they are used with the Support Vector methods.
• Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support
• Vector Machines.
• Know how each of the algorithms determines the best separating hyperplane.
• Distinguish between hard and soft margins and when each is to be used.
• Know how to extend the method for nonlinear cases.

• Understand what principal components are and when principal component analysis is appropriate.
• Describe eigenvalues and eigenvectors and how they are used to calculate principal components.
• Understand loading and loading vectors.
• Know how to decide how many principal components to use in the analysis.
• Be able to use principal component analysis for regression.

• Data structures
• Vector
• Matrix
• Array
• Data frame
• List
• Factors

• DPLYR & apply Function
• Import Data File
• DPLYP – Selection
• DPLYP – Filter
• DPLYP – Arrange
• DPLYP – Mutate
• DPLYP – Summarize

• Data visualization in R
• Bar chart, Dot plot
• Scatter plot, Pie chart
• Histogram and Box plot
• Heat Maps
• World Cloud

• Introduction to statistics
• Type of Data
• Distance Measures (Similarity, dissimilarity, correlation)
• Euclidean space.
• Manhattan
• Minkowski
• Cosine similarity
• Mahalanobis distance
• Pearson’s correlation coefficient
• Probability Distributions

• Hypothesis Testing
• Introduction
• Hypothesis Testing – T Test, Anova

• Hypothesis Testing about population
• Chi Square Test
• F distribution and F ratio

• Regression
• Linear Regression Models
• Non Linear Regression Models

• Classification
• Decision Tree
• Logistic Regression
• Bayesian
• Support Vector Machines

• Clustering
• K-means Clustering and Case Study
• DBSCAN Clustering and Case study
• Hierarchical Clustering

• Association
• Apriori Algorithm
• Candidate Generation
• Visualization on Associated Rules
• Summary
• Conclusion