Business Analytics Course - Business Analytics 360!

• Explain scope and direction
• Percent of Total, Running / Cumulative calculations
• Introduction to LOD (Level of Detail) Expressions
• User applications of Table calculations

• Using Parameters in
  • Calculated fieldsBins
  • Reference Lines
  • Filters/Sets
• Display Options (Dynamic Dimension/Measure Selection)
• Create What-If/ Scenario analysis

• Combining multiple visualizations into a dashboard (overview)
• Making your worksheet interactive by using actions
  • Filter
  • URL
  • Highlight
• Complete Interactive Dashboard for Sales & Services

• Options in Formatting your Visualization
• Working with Labels and Annotations
• Effective Use of Titles and Captions

• Multiple Table Joins
• Data Blending
• Difference between joining and blending data, and when we should do each
• Toggle between to Direct Connection and Extracts

• Introducing VBA
• What is Logic?
• What Is VBA?
• Introduction to Macro Recordings, IDE

• How VBA Works with Excel 
• Working In the Visual Basic Editor
• Introducing the Excel Object Model
• Using the Excel Macro Recorder
• VBA Sub and Function Procedures

• Key Components of  Programming  language
• Essential VBA Language Elements
• Keywords & Syntax
• Programming statements
• Variables & Data types
• Comments
• Operators
• Working with Range Objects

• A look at some commonly used code snippets

• Programming constructs in VBA
• Control Structures
• Looping Structures
• The With- End with Block

• Functions & Procedures in VBA – Modularizing your programs
• Worksheet & workbook functions
• Automatic Procedures and Events
• Arrays

• Objects & Memory Management in VBA
• The NEW and SET Key words
• Destroying Objects – The Nothing Keyword

• Error Handling

• Controlling accessibility of your code – Access specifiers

• Code Reusability – Adding references and components to your code

• Communicating with Your Users 
• Simple Dialog Boxes
• User Form Basics
• Using User Form Controls
• Add-ins
• Accessing Your Macros through the User Interface
• Retrieve information through Excel from Access Database using VBA

• What is analytics & Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project
• Why R for data science?

• Introduction R/R-Studio - GUI
• Concept of Packages - Useful Packages (Base & Other packages)
• Data Structure & Data Types (Vectors, Matrices, factors, Data frames,  and Lists)
• Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)
• Database Input (Connecting to database)
• Exporting Data to various  formats)
• Viewing Data (Viewing partial data and full data)
• Variable & Value Labels –  Date Values

• Data Manipulation steps
  • Creating New Variables (calculations & Binning)
  • Dummy variable creation
  • Applying transformations
  • Handling duplicates
  • Handling missings
  • Sorting and Filtering
  • Subsetting (Rows/Columns)
  • Appending (Row appending/column appending)
  • Merging/Joining (Left, right, inner, full, outer etc)
  • Data type conversions
  • Renaming
  • Formatting
  • Reshaping data
  • Sampling
• Data manipulation tools
  • Operators 
  • Functions
  • Control Structures (if, if else)
  • Loops (Conditional, iterative loops, apply functions)
  • Arrays
• R Built-in Functions (Text, Numeric, Date, utility)
  • Numerical Functions
  • Text Functions
  • Date Functions
  • Utilities Functions
  • R User Defined Functions
• R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)

• R Packages for Exploratory Data Analysis(dplyr, plyr, car, Hmisc, psych, etc)
• R Packages for Graphical Analysis (base, ggplot, etc)

• Basic Statistics - Measures of Central Tendencies and Variance
• Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
• Inferential Statistics -Sampling - Concept of Hypothesis Testing
• Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & modeling process
• Popular modeling algorithms
• Types of Business problems - Mapping of Techniques
• Different Phases of Predictive Modeling 

• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns

• Need of Data preparation
• Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction

• Introduction - Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
• Assess the overall effectiveness of the model 
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results - Business Validation - Implementation on new data

• Introduction - Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model (Binary Logistic Model)
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
• Interpretation of Results - Business Validation - Implementation on new data

Time Series Forecasting: Solving forecasting problems

• Introduction - Applications
• Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
• Classification of Techniques(Pattern based - Pattern less)
• Basic Techniques - Averages, Smoothening, etc 
• Advanced Techniques - AR Models, ARIMA, etc 
• Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc 

Machine Learning -Predictive Modeling – Basics 

•Introduction to Machine Learning & Predictive Modeling

•Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting

•Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

•Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)

•Overfitting (Bias-Variance Trade off) & Performance Metrics

•Feature engineering & dimension reduction

•Concept of optimization & cost function

•Overview of gradient descent algorithm

Machine Learning -Predictive Modeling – Basics 

•Overview of Cross validation(Bootstrapping, K-Fold validation etc)

•Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Segmentation: Solving segmentation problems

•Introduction to Segmentation & Role of ML

•Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)

•Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

•Behavioral Segmentation Techniques (K-Means Cluster Analysis)

•Cluster evaluation and profiling - Identify cluster characteristics

•Interpretation of results - Implementation on new data

Unsupervised Learning: Segmentation

•Concept of Distance and related math background

•Expectation Maximization

•Hierarchical Clustering

•Spectral Clustering (DBSCAN)

•Principle component Analysis (PCA)

Supervised Learning: Decision Trees

•Decision Trees - Introduction - Applications

•Types of Decision Tree Algorithms

•Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees

•Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness 

•Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules

•Decision Trees - Validation

•Overfitting - Best Practices to avoid

Supervised Learning: Ensemble Learning

•Concept of Ensembling

•Manual Ensembling Vs. Automated Ensembling

•Methods of Ensembling (Stacking, Mixture of Experts)

•Bagging (Logic, Practical Applications)

•Random forest (Logic, Practical Applications)

•Boosting (Logic, Practical Applications)

•Ada Boost

•Gradient Boosting Machines (GBM)

•XGBoost

Supervised Learning: Artificial Neural Networks (ANN)

•Motivation for Neural Networks and Its Applications

•Perceptron and Single Layer Neural Network, and Hand Calculations

•Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques 

•Neural Networks for Regression

•Neural Networks for Classification

•Interpretation of Outputs and Fine tune the models with hyper parameters

•Validating ANN models 

Supervised Learning: Support Vector Machines

•Motivation for Support Vector Machine & Applications

•Support Vector Regression

•Support vector classifier (Linear & Non-Linear)

Supervised Learning: Support Vector Machines

•Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)

•Interpretation of Outputs and Fine tune the models with hyper parameters

•Validating SVM models 

•

Supervised Learning: KNN

•What is KNN & Applications?

•KNN for missing treatment

•KNN For solving regression problems

•KNN for solving classification problems

•Validating KNN model

•Model fine tuning with hyper parameters

Supervised Learning: Naïve Bayes

•Concept of Conditional Probability

•Bayes Theorem and Its Applications

•Naïve Bayes for classification 

•Applications of Naïve Bayes in Classifications

Text Mining & Analytics

•Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)

Text Mining & Analytics

•Finding patterns in text: text mining, text as a graph

•Natural Language processing (NLP) 

•Text Analytics – Sentiment Analysis using R

•Text Analytics – Word cloud analysis using R

•Text Analytics -  Segmentation using K-Means/Hierarchical Clustering

•Text Analytics -  Classification (Spam/Not spam)

•Applications of Social Media Analytics

•Metrics(Measures Actions) in social media analytics

•Examples & Actionable Insights using Social Media Analytics

•Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)

•Fine tuning the models using Hyper parameters, grid search, piping etc.

Project - Consolidate Learnings:

Applying different algorithms to solve the business problems and bench mark the results

• What is analytics & Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project
• Why Python for data science?

• Overview of Python- Starting with Python
• Why Python for data science?
• Anaconda vs. python
• Introduction to installation of Python
• Introduction to Python Editors & IDE's(Jupyter,/Ipython)
• Understand Jupyter notebook & Customize Settings
• Concept of Packages - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.)
• Installing & loading Packages & Name Spaces
• Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
• List and Dictionary Comprehensions
• Variable & Value Labels –  Date & Time Values
• Basic Operations - Mathematical - string – date
• Control flow & conditional statements
• Debugging & Code profiling
• Python Built-in Functions (Text, numeric, date, utility functions)
• User defined functions – Lambda functions
• Concept of apply functions 
• Python – Objects – OOPs concepts
• How to create class and modules?
• How to call classes and modules?
• Concept of pipelines in Python

• What is NumPy?
• Overview of functions & methods in NumPy
• Data structures in NumPy
• Creating arrays and initializing
• Reading arrays from files
• Special initializing functions
• Slicing and indexing
• Reshaping arrays
• NumPy Maths
• Combining arrays
• Basic algebraic operations using NumPy arrays
• Solving linear equations
• Matrix inversions
• Calculating Eigen vectors

• What is pandas, its functions & methods
• Pandas Data Structures (Series & Data Frames)
• Creating Data Structures (Data import – reading into pandas)

• Importing Data from various sources (Csv, txt, excel,etc.)
• Database Input (Connecting to database)
• Viewing Data objects -  sub setting, methods
• Exporting Data to various  formats
• Understanding of data
• Important python modules: Pandas, NumPy

• Understand the data
• Sub Setting Data or Filtering Data or Slicing Data
  • Using [] brackets
  • Using indexing or referring with column names/rows
  • Using functions
• Dropping rows & columns
• Mutation of table (Adding/deleting columns) 
• Binning data (Binning numerical variables in to categorical variables using cut() and qcut() functions)
• Renaming columns or rows
• Sorting 
  • By data/values, index
  • By one column or multiple columns
  • Ascending or Descending
• Type conversions
• Setting index 
• Handling duplicates
• Handling missing values – detect, filter, replace
• Handling outliers 
• Creating dummies from categorical data (using get_dummies())
• Applying functions to all the variables in a data frame (broadcasting)
• Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)
• Important Python modules for data manipulation (Pandas, NumPy, re, math, string, datetime etc.)

• Exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Uni-variate Analysis (Distribution of data & Graphical Analysis)
• Bi - Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating different Graphs using multiple python packages- Bar/pie/line chart/histogram/stack chart/boxplot/ scatter/ density etc)
• Important Packages for Visualization (graphical analysis) – Pandas, Matplotlib, Seaborn, Bokeh etc.)

• Basic Statistics - Measures of Central Tendencies and Variance
• What is probability distribution?
• Important distributions (discrete & continuous distributions)
• Deep dive of normal distributions and properties
• Concept of sampling & types of sampling
• Concept of standard error and central limit theorem
• Inferential Statistics - Concept of Hypothesis Testing
• Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square
• Important modules for statistical methods: NumPy, SciPy, Pandas

• Introduction to Predictive Modelling
• Types of Business problems - Mapping of Techniques
• Relevance in industry and need of the hour
• Difference between jargons i.e. data science, data analysis, data analytics, data mining
• What is Machine Learning?
• What is the goal of Machine Learning?
• Applications of ML (Marketing, Risk, Operations, etc.)
• Key components of ML
• Overall process of executing the ML project  (Data Pre-processing, Sampling, Model Building, Validation)
• Common mistakes done in ML project and how to overcome
• Different terms to know for ML

• Major Classes of Learning Algorithms -Supervised vs. Unsupervised Learning vs. Semi supervised vs. Reinforcement Learning
• Important   Consideration   like   fitment   of techniques
• Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
• Concept of optimization - Gradient descent algorithm
• Concept of feature engineering
• Regularization (LASSO, LARS, Elastic net and Ridge regression)
• Types of Cross validation(Train & Test, K-Fold validation etc.)
• Cost & optimization functions

• Introduction - Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Important steps in Model building
• Need of Data preparation
• Data Audit Report  and Its importance
• Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
• Variable Reduction Techniques - Factor & PCA Analysis
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc.)
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results - Business Validation - Implementation on new data

• Introduction - Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model
• Important steps in model building
• Understanding standard model metrics (Concordance, Variable significance, Gini, KS, Misclassification, etc.)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers, etc.)
• Interpretation of Results - Business Validation 
• Implementation on new data and Tracking the model

• Over view of Decision Trees
• Types of decision trees (Regression Trees, Classification trees, Oblique Decision Trees)
• Types of decision tree algorithms (CART vs. CHAID vs. C50 etc.)
• Concept of objective segmentation
• How to use decision trees to solve regression, classification & segmentation problems)
• Rule Based Knowledge: Logic of Rules, Evaluating Rules, Rule Induction and Association Rules 
• Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; 
• Splitting criteria: Entropy; Information Gain, Gini Index, Chi Square; ANOVA)
• Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness 
• Pruning decision tree
• Cost as a consideration
• Fine Tuning model using tuning parameters
• Model validation
• Over fitting - Best Practices to avoid
• Implementation of Solution

• What is concept of Ensemble learning (Stacking, Mixture of Experts)?
• Types of ensemble models (homogeneous, heterogeneous)
• Logic, Practical Applications
• Ensemble learning techniques
• Bagging
• Random Forest
• Boosting
• AdaBoost
• Gradient Boost
• XGBoost
• Fine tuning the model using tuning parameters

• What is concept of Instance based learning?
• What is KNN?
• KNN method for regression & classification
• KNN method for missing imputation
• Computation of Distance Matrix
• The Optimum K value
• Model Building, validation & Evaluation of Model
• Advantages & Disadvantages of KNN Models
• Applications of KNN  in collaborative filtering, digit recognition
• KNN in collaborative filtering, digit recognition

• Fundamentals of Probability; Conditional and Marginal Probability;  Bayes Theorem and Its Applications
• Probabilities - The Prior and Posterior Probabilities
• Bayesian Belief nets, MAP, Naïve Rule and Naïve Bayes
• Naïve Bayes for classification - Data Processing - Discretization of Features
• Applications of Naïve Bayes in Text Mining, Spam Engines and Classifications
• Model Building,  Validation and Evaluation of model
• Pros/Cons of Naïve Bayes Models

• What is Support vector machines?
• Understanding SVM 
• Concepts of Linearly separable vs. non separable data
• Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
• Train/Test/Tune the Model using SVM
• Applications and Interpretation

• Introduction to Segmentation
• Types of Segmentation (Subjective Vs. Objective, Heuristic Vs. Statistical)
• Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
• Concept of Distance and related math background
• Segmentation Techniques 

• K-Means/K-Medians Clustering
• Density Based clustering (DBSCAN)
• Identifying number of segments (Pseudo F-value, Silhouette score, elbow method etc.)
• Cluster evaluation and profiling
• Identifying the characteristics of segmentation
• Interpretation of results - Implementation on new data
• Overview of other unsupervised learning techniques (Factor analysis, Hidden Markov models, Gaussian mixture models etc.)

• What is forecasting?
• Applications of forecasting
• Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
• Types of Seasonality (Hourly, daily, weekly, monthly, quarterly etc.)
• Classification of Techniques(Pattern based - Pattern less)
• Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
• Classification of Time Series Techniques (Univariate & Multivariate)

Stationary Time Series Methods

• Moving Averages
• Weighted moving averages
• Exponential Smoothening
• Comparison between MA & ES

Trend Based Time Series

• Linear Regression
• Double exponential smoothening (Holt’s Method)
• Comparison between Regression & DS

Seasonal Time Series

• Decomposition - CMA Method

Advanced Techniques

• Box Jenkins Methodology
• AR, MA, ARMA Models
• ARIMA/SARIMA
• ARIMAX, SARIMAX

• Understanding Forecasting Accuracy
• Goodness Metrics: MSE, MAPE, RMSE, MAD

• Motivation for Neural Networks and Its Applications
• Understand Neural Networks
• Structure of Networks
• Perceptron and Single Layer Neural Network, and Hand Calculations
• Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques 
• The ANN Model
• Types of Activate functions
• Train/Test/Tune the ANN Model

• Unstructured vs. Semi-structured Data
• Text Mining - characteristics, trends
• Domain presentation - discussion of various areas and their applications
• Programming languages designated for working on Text Mining analysis
• Data Scientist - a profession comprising mainly of working with Text Mining
• Social Media – Characteristics of Social Media
• Applications of Social Media Analytics
• Examples & Actionable Insights using Social Media Analytics 

• Text processing using string functions & methods   
• Understanding regular expressions 
• Identifying patterns in the text using regular expressions  

• Getting Started with NLTK
• Introduction to NLP & NLTK
• Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)

• Fundamentals of information retrieval
• Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
• Cleaning and normalization of data
• Sentence Tokenize and Word Tokenize,  Removing insignificant words,(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatisation /chunking
• Creating Term-Document matrix
• Finding associations
• Removing rare terms (Sparse terms)
• Measurement of similarity between documents and terms
• Visualization of term significance in the form of word clouds
• Tagging text with parts of speech
• Word Sense Disambiguation

• Sentiment analysis
• vocabulary approach, based on Bayesian probability methods
• Name entity recognition (NER)
• Methods of data visualization
• word length counts plot
• word frequency plots
• word clouds
• correlation plots
• letter frequency plot
• Heat map
• Grouping texts using different methods

• Data-centric methods
• K-means
• Classification Models (spam detection, topic modelling)
• K Nearest Neighbours
• SVM (Linear Support Vector Classifier)
• Naive Bayes
• Decision tree 
• Semantic similarity between texts
• Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)

• Sentiment Analysis (Classification, weighted score etc) 
• Word cloud analysis (Examples) 
• Segmentation using K-Means/Hierarchical Clustering (Grouping the similar words) 
• Classification (Spam/Not spam)
• Topic Modeling (LDA, LSA, Louvain etc)
• Text Summarization