Business Analytics using SAS Training Course

  Lecture-1 Introduction to Business Analytics, R programming and R Studio
Live Lecture 
·      Introduction to Business Intelligence

·      Introduction to Business Analytics

·      Introduction to Data

·      Introduction to Information

·      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

·      Worldwide R community collaboration

·      Practical Exercise

  Lecture-2 Understanding Data
Live Lecture 
·      Importance of data in business analytics

·      Differences between data, information and knowledge

·      The various stages that an organization goes through in terms of data maturity

·      Business Analytics, Business Intelligence and Data Mining

·      Differences between Business Analytics and Business Intelligence

·      Describe the two major components within Business Analytics and Business Intelligence

·      Data Mining technique helps both Business Intelligence and Business Analytics

·      Analytical Decision-Making Process

·      Analysing Business Problems

·      Practical Exercise

  Lecture-3 Introduction to R programming and R Studio
Live Lecture 
·      Installation of rstudio

·      Implementing simple mathematical operations

·      Logic using R operators

·      Loops

·      If statements

·      Switch cases

·      Practical Exercise

  Lecture-4 Data Exploration
Live Lecture 
·      Introduction to data exploration

·      Importing and exporting data to/from external sources

·      What are data exploratory analysis and data importing?

·      Dataframes

·      Accessing individual elements

·      Vectors

·      Factors

·      Operators

·      In-built functions

·      Conditional Looping statements

·      User-defined functions

·      Data types

·      Practical Exercise

  Lecture-5 Data Manipulation
Live Lecture 
·      Need for data manipulation

·      Introduction to the dplyr package

·      Selecting one or more columns with select()

·      Filtering records on the basis of a condition with filter()

·      Adding new columns with mutate()

·      Sampling, and counting

·      Combining different functions with the pipe operator

·      Implementing SQL-like operations with sqldf

·      The various steps involved in Data Cleaning

·      Functions used in Data Inspection

·      Tackling the problems faced during Data Cleaning

·      Uses of the functions

·      Coerce the data

·      Uses of the apply() functions

·      Practical Exercise

  Lecture-6 Data Import Techniques in R
Live Lecture 
·      Import data from spreadsheets and text files into R

·      Import data from other statistical formats

·      Packages installation used for database import

·      Connect to RDBMS from R using ODBC

·      Basic SQL queries in R

·      Basics of Web Scraping

·      Practical Exercise

  Lecture-7 Exploratory Data Analysis
Live Lecture 
·      Understanding the Exploratory Data Analysis(EDA)

·      Implementation of EDA on various datasets

·      Boxplots

·      Whiskers of Boxplots

·      Understanding the cor() in R

·      EDA functions

·      Multiple packages in R for data analysis

·      The Fancy plots like the Segment plot

·      HC plot in R

·      Practical Exercise

  Lecture-8 Data Visualization
Live Lecture 
·      Introduction to visualization

·      Different types of graphs

·      The grammar of graphics

·      The ggplot2 package

·      Categorical distribution with geom_bar()

·      Numerical distribution with geom_hist()

·      Building frequency polygons with geom_freqpoly()

·      Making a scatterplot with geom_pont()

·      Multivariate analysis with geom_boxplot

·      Univariate analysis with barplot, histogram & density plot

·      Multivariate distribution

·      Creating barplots for categorical variables using geom_bar()

·      Adding themes with the theme() layer

·      Visualization with plotly

·      Frequency plots with geom_freqpoly()

·      Multivariate distribution with scatter plots and smooth lines

·      Continuous distribution vs categorical distribution with box-plots

·      Sub grouping plots

·      Co-ordinates and themes

·      Understanding plotly

·      Various plots

·      Visualization with ggvis

·      Geographic visualization with ggmap()

·      Building web applications with shinyr

·      Practical Exercise

  Lecture-9 Introduction to Statistics
Live Lecture 
·      Why do we need statistics?

·      Categories of statistics

·      Statistical terminology

·      Types of data

·      Measures of central tendency

·      Measures of spread

·      Correlation and covariance

·      Standardization and normalization

·      Probability and the types

·      Hypothesis testing

·      Chi-square testing

·      ANOVA

·      Normal distribution

·      Binary distribution

·      Practical Exercise

  Lecture-10 Machine Learning
Live Lecture 
·      Introduction to Machine Learning

·      Practical Exercise

  Lecture-11 Linear Regression
Live Lecture 
·      Introduction to linear regression

·      Predictive modeling

·      Simple linear regression vs multiple linear regression

·      Concepts

·      Formulas

·      Assumptions

·      Residuals in Linear Regression

·      Building a simple linear model

·      Predicting results

·      Finding the p-value

·      Practical Exercise

  Lecture-12 Logistic Regression
Live Lecture 
·      Introduction to logistic regression

·      Logistic regression concepts

·      Linear vs logistic regression

·      Math behind logistic regression

·      Detailed formulas

·      logit function and odds

·      Bivariate logistic regression

·      Poisson regression

·      Building a simple binomial model

·      Predicting the result

·      Making a confusion matrix for evaluating the accuracy

·      True positive rate

·      False positive rate

·      Threshold evaluation with ROCR

·      Finding out the right threshold by building the ROC plot

·      Cross validation

·      Multivariate logistic regression

·      Building logistic models with multiple independent variables

·      Real-life applications of logistic regression

·      An introduction to logistic regression

·      Comparing linear regression with logistics regression

·      Bivariate logistic regression with multivariate logistic regression

·      Understanding the fit of the model

·      Using qqnorm() and qqline()

·      Understanding the summary results with null hypothesis & F-statistic

·      Practical Exercise

  Lecture-13 Decision Trees and Random Forest
Live Lecture 
·      What is classification?

·      Different classification techniques

·      Introduction to decision trees

·      Algorithm for decision tree induction

·      Building a decision tree in R

·      Confusion matrix & regression trees vs classification trees

·      Introduction to bagging

·      Random forest and implementing it in R

·      Computing probabilities

·      Impurity function

·      Entropy

·      Gini index

·      Information gain for the right split of node

·      Overfitting

·      Pruning

·      Re-pruning

·      Post-pruning

·      Cost-complexity pruning

·      Pruning a decision tree and predicting values

·      Finding out the right number of trees

·      Evaluating performance metrics

·      Practical Exercise

  Lecture-14 Unsupervised Learning
Live Lecture 
·      What is Clustering?

·      Its use cases

·      What is k-means clustering?

·      What is canopy clustering?

·      What is hierarchical clustering?

·      Introduction to unsupervised learning

·      Feature extraction

·      Clustering algorithms

·      The k-means clustering algorithm

·      Theoretical aspects of k-means

·      K-means process flow

·      K-means in R

·      Implementing k-means

·      Finding out the right number of clusters using a screen plot

·      Dendograms

·      Understanding hierarchical clustering

·      Implementing it in R

·      Explanation of Principal Component Analysis (PCA)

·      Implementing PCA in R

·      Practical Exercise

  Lecture-15 Association Rule Mining & Recommendation Engines
Live Lecture 
·      Introduction to association rule mining and MBA

·      Measures of association rule mining

·      Introduction to recommendation engines

·      User-based collaborative filtering

·      Item-based collaborative filtering

·      Implementing a recommendation engine in R

·      Recommendation engine use cases

·      Practical Exercise

  Lecture-16 Time Series Analysis
Live Lecture 
·      What is a time series?

·      The techniques

·      Applications

·      Components of time series

·      Moving average

·      Smoothing techniques

·      Exponential smoothing

·      Univariate time series models

·      Multivariate time series analysis

·      ARIMA model

·      Time series in R

·      Sentiment analysis in R

·      Text analysis

·      Practical Exercise

  Lecture-17 Support Vector Machine (SVM)
Live Lecture 
·      Introduction to Support Vector Machine (SVM)

·      Data classification using SVM

·      SVM algorithms using separable and inseparable cases

·      Linear SVM for identifying margin hyperplane

·      Practical Exercise

  Lecture-18 Naïve Bayes
Live Lecture 
·      What is Naive Bayes?

·      What is the Bayes theorem?

·      What is Naïve Bayes Classifier?

·      Classification Workflow

·      How Naive Bayes classifier works

·      Classifier building in Scikit-Learn

·      Building a probabilistic classification model using Naïve Bayes

·      The zero probability problem

·      Practical Exercise

  Lecture-19 Text Mining
Live Lecture 
·      Introduction to the concepts of text mining

·      Text mining use cases

·      Understanding and manipulating the text with ‘tm’ and ‘stringr’

·      Text mining algorithms and the quantification of the text

·      TF-IDF and after TF-IDF

·      Practical Exercise

  Case Studies
Case Studies