Data Science

Data Visualization and Summarization

Data Visualization and Interpretation

Different types of data

Data summarization and visualization methods

Tables, Graphs, Charts, Histograms

Frequency distributions

Relative frequency

Measures of central tendency and dispersion

Box Plot

Chebychev’s Inequality

Data visualization and Storytelling with Data

Basic probability concepts

Conditional probability

Bayes Theorem

Monty Hall Problem

Probability distributions

Continuous and discrete distributions

Sequential decision-making

Decision Tree

Sampling and estimation: Estimation problems, Point and interval estimates

Case Studies: 1. Central Parking Solutions Private Limited (IIMB Case);

2. A Dean’s Dilemma: To Admit or Not to Admit (IIMB Case)

Descriptive Statistics

Introduction to Advanced Data Analytics

Statistical inferences for various Business problems

Types of Variables

Measures of central tendency

Dispersion

Variable Distributions

Probability

Distributions

Normal Distribution and Properties

Histograms

Exploratory Data Analysis

Histograms

Probability Theory

Bayes Theorem

Random Variables

Cumulative Distribution Function

Continuous Distributions

 

Skewness

Anova

Central limit Theorem

Monte Carlo Method

Estimate

Kernel Density Estimate

Regression

Covariance

Correlation

Causation

Euclidean Distance

Data quality and outlier treatment

Outlier treatment with robust measurements (median)

Outlier treatment with central tendency Mean

Replacing with series means or median values

Z score Calculation

Data Normalization

Sampling and estimation

Test of Hypothesis

Null/Alternative Hypothesis formulation

Type I and Type II errors

One Sample TTEST

Paired TTEST

Independent Sample TTEST

Analysis of Variance (ANOVA),

MANOVA

Chi Square Test (Non Parametric Tests)

Kruskal-Wallis,

Mann-Whitney,

Wilcoxon, McNemar test

Data Preparation and Quality Check

Data Validation and Data Imputation

Proc Univariate techniques analysis (SAS)

Q-Q probability plots

Cumulative frequency (P P) plots

Explorer analysis (SPSS)

Steam and leaf analysis

Kolmogorov Smirnov test

Shapiro Wilks test

 

Data Transformation

Log transformation (s)

Arcsine transformation

Box- Cox transformation

Square root transformation

Inverse transformation

Predictive Analytics

Simple linear regression

Coefficient of determination

Significance tests

Residual analysis

Confidence and Prediction intervals

Multiple linear Regression

Coefficient of determination

Interpretation of regression coefficients

Categorical variables

Heteroscedasticity

Multicollinearity

Outliers

Auto regression and Transformation of variables

Regression Model Building

Logistic and Multinomial Regression

Logistic function

Estimation of probability using logistic regression

Deviance

Wald Test

Hosmer Lemshow Test

Classification table

Gini co-efficient

Forecasting

Moving average

Exponential smoothing

Casual Models

“Application of predictive analytics in retail, direct marketing, health care, financial services, insurance, supply chain, etc.”

Case Studies: Pricing of players in the Indian Premier League (IIMB Case)

Colonial Broadcasting Company (HBS Case)

Pedigree vs Grit: Predicting Mutual Fund Manager Performance (Kellogg Case)

Breaking Barriers – Micro-Mortgage Analytics (IIMB Case)

A Game of Two Halves: In-Play betting in Football (IIMB Case)

Optimization Analytics (Prescriptive Analytics)

 

Introduction to Operations Research (OR)

Linear programming (LP)

Formulating decision problems using linear programming

Interpreting the results and sensitivity analysis

Concepts of shadow price and reduced cost

Multi-period LP models

“Applications of linear programming in product mix, blending, cutting stock, transportation, transshipment, assignment, scheduling, planning and revenue management problems.”

Network models and project planning.

Integer Programming (IP) problems

Mixed-integer and Zero-one programming

Applications of IP in capital budgeting, location decisions, contracts

Multi-criteria decision making (MCDM) techniques

Goal Programming (GP) and analytic hierarchy process (AHP)

Applications of GP and AHP in solving problems with multiple objectives

Non-linear programming, portfolio theory

Case Studies: 1. Merton Truck Company (HBS Case),

2. Supply Chain Optimization at Madurai Aavin Milk Dairy (IIMB Case)
3. Red Brand Canners (Stanford Case)

Stochastic Models (Marketing and Retail Analytics)

Introduction to stochastic models

Markov models

Classification of states

Steady-state probability estimation

Brand switching and loyalty modeling

Market share estimation in the short and long run

Poisson process

Cumulative Poisson process

Applications of Poisson and cumulative Poisson in operations, marketing and insurance

Measuring effectiveness of retail promotions, warranty analytics

Renewal theory, Applications of renewal theory in operations and supply chain management

Markov decision process, Applications of Markov decision process in sequential decision making

Case Studies: 1. Browser Wars: Microsoft Vs Netscape (Darden Case)

2. Consumer choices between house brands and national brands in detergent purchase at Reliance Retail (IIMB Case)”
3. MNB ONE Credit card Portfolio (Darden Case).

Market Research and Operations Analytics

Principal component analysis

 

Factor analysis

Conjoint analysis

Discriminant analysis

“ARCH (autoregressive conditional heteroscedasticity)

ARCH (Generalized autoregressive conditional heteroscedasticity)”

Monte Carlo simulation

Supply chain analytics

Classification and regression trees (CART)

Chi-squared automatic interaction detector (CHAID)

Statistical process control, Value stream mapping, TRIZ

Case Studies: 1. Apollo Hospitals: Differentiation through Hospitality (IIMB Case)

2. Dean’s Dilemma: To Admit or Not to Admit (IIMB Case)
3. Dosa King – A Standardized Masala Dosa for Every Indian (IIMB Case)
4. Delivering Doors in a Window – Supply Chain Management at Hindustan Aeronautics Limited (IIMB Case)

Analytics in Finance and Insurance

“Dynamic pricing and revenue management, high dimensional data analysis, financial data analysis and prediction”

Survival analysis and its applications:

Life tables

KapMeier estimates

Proportional hazards

Predictive hazard modeling using customer history data

Analytics in finance, discounted cash flows (DCF), Profitability analysis

Asset performance:

Sharpe ratio, Calmar ratio, Value at risk (VaR), Brownian motion process, Pricing options and Black–Scholes formula, Game theory

Insurance loss models: Aggregate loss models, discrete time ruin models, Continuous time ruin models

Predictive Modeling & Diagnostics

Correlation – Pearson, Kendall

SLR Regression

MLR Regression

Residual analysis

Auto Correlation

VIF Analysis

Indexing Eigen Value interpretation

Homoscedasticity

Homogeneity

Stepwise regression

Transformation of variables

Logistic Regression Analysis

 

Discriminant and Logit Analysis

Multiple Discriminant Analysis

Stepwise Discriminant Analysis Binary

Logit Regression

Estimation of probability using logistic regression, Wald Test

Hosmer Lemshow

Advanced Analysis

Factor Analysis

Introduction to Factor Analysis – PCA

Reliability Test

KMO MSA tests

Eigen Value Interpretation

Rotation and Extraction

Varimix Models

Principle component analysis

Conformity Factor Analysis

Exploitary Factor Analysis

Cluster Analysis

Introduction to Cluster Techniques

Distance Methodologies

Hierarchical and Non-Hierarchical Procedures

K Means clustering

Wards Method

Conjoint Analysis

Statistics and terms Association with Conjoint Analysis

Assumption and limitation of conjoint analysis

Hybrid Conjoint Analysis

Time Series Forecasting

Introduction to Time Series Data

Visualizing Time Series data

Data Exploration

Intro to AR, MA, ARMA, ARIMA Models

Smoothing and annual Time series

Time series forecasting for seasonal data

Multiplicative Models

Additive Models

Case studies with ARIMA Models (Using SAS & R)

 

Loss Forecasting & Portfolio Monitoring

Introduction to Survival Analysis

Using Survival Models to build Credit Baselines

Loss forecasting

Portfolio and monitoring &strategy

Introduction to Risk Management Practices by Bank

Overview of Scorecard’s

Stages of Scorecard Development

Types of Scorecards

Scorecard Development Project

Scorecard Development Project planning

Roles of Responsibilities

Data Review and Project Parameters

Project plan

Scorecard Development Preliminaries

Data Exclusion

Variable selection

Target Variable definition

Data Description

Scorecard Development

Missing Value Check

Exploratory Data Analysis

Correlation Analysis

Assigning predictive power

Sampling

Model development

Model Selection

Model finalizations

Model validation

Variable Selection using Weight of Evidence & Information Value

Scorecard Development using grouped variables

Concept of variable grouping

Computing Weight of Evidence (WOE) and Information Values

Model development with WOE Variables

Reject Inference

 

Introduction to Reject Inference

Reject inference techniques

Strategies developed with Reject Inference

Performing reject inference and remodeling on SAS

Model Validations, Finalization and Uses

Introduction to Scaling

Finalize models/Model selection based on performance stats

Model Validations

Scorecard Management reports and documentation

Data Mining

Data Mining

Data partition (Training, Validating Testing)

Data Explore

Data Testing

Data Transform

Linear Model

SVM Model

Tree Analysis

RandomForest Analysis

Model Evaluation

ROC

Lift Curve

Sensitivity

Error/ Confusion matrics

Text Mining

Vocabulary Mapping

Classify Text

Using NLTK

Feature Extraction

Market Basket Analysis

Association Rules

Support Vector Machines

Term Frequency and Weight

Term Document Matrix

UIMA

Text Analysis

Named Entity Recognition

Corpus

Business Analytics

 

Introduction and Data Analytics

Linear Regression

Logistic Regression

Decision Tree and Clustering

Time Series Modeling

Logistic Regression

Market Basket Analysis

Cross Sell Model

Market Mix Modeling

Churn Analytics

Buy till You Die Model

Customer Lifetime Value Analysis

Telecom Model to Estimate Bill

Machine Learning

Numerical and Categorical var

Supervised and Unsupervised Learning

Concepts, Inputs and Attributes

Classifier

Prediction

Bias and Variance

Trees and Classification

Decision trees

Boosting

Naive Bayes Classifiers

K-Nearest Neighbor

Logistic and Linear Regression

Ranking and Preceptor

Hierarchical and K-Means Clustering

Neural networks

Sentimental Analysis

Collaborative Filtering

Tagging

Vocabulary mapping

Linear Regression with One Variable

Model and Cost Function

Parameter Learning

Linear Regression with Multiple Variables

 

Environment Setup Instructions

Multivariate Linear Regression

Computing Parameters Analytically

Logistic Regression

Classification and Representation

Logistic Regression Model

Multiclass Classification

Regularization

Solving the Problem of over fitting

Neural Networks: Representation

Motivations

Neural Networks

Applications

Neural Networks: Learning

Cost Function and Back propagation

Back propagation in Practice

Application of Neural Networks

Advice for Applying Machine Learning

Evaluating a Learning Algorithm

Bias vs. Variance

Machine Learning System Design

Building a Spam Classifier

Handling Skewed Data

Using Large Data Sets

 

Support Vector Machines

Large Margin Classification

Kernels

SVMs in Practice

Unsupervised Learning

Clustering

Dimensionality Reduction

Motivation

Principal Component Analysis

Applying PCA

Anomaly Detection

Density Estimation

Building an Anomaly Detection System

Multivariate Gaussian distribution (Optional)

Recommender Systems

Predicting Movie Ratings

Collaborative Filtering

Low Rank Matrix Factorization

Natural language Processing (NLP)

Language modeling

Hidden Markov models

Basic Text Processing

Minimum Edit Distance

Language Modeling

Spelling Correction

NLP Tasks and Text Similarity

 

Syntax and Parsing

Language Modeling and Word Sense Disambiguation

Part of Speech Tagging and Information Extraction

Text Summarization

Collocations and Information Retrieval

Sentiment Analysis and Semantics

Discourse, Machine Translation, and Generation

Tagging problems

Probabilistic context-free grammars

Parsing problem

Statistical approaches to machine translation

Log-linear models and their application to NLP problems

Unsupervised and semi-supervised learning in NLP

Deep Learning

Intro to Deep Learning

Simple Word Vector representations: word2vec, GloVe

Advanced word vector representations: language models, softmax, single layer networks

Neural Networks and back propagation — for named entity recognition

Project Advice, Neural Networks and Back-Prop (in full gory detail)

Practical tips: gradient checks, over fitting, regularization, activation functions, details

Introduction to Tensor flow

Recurrent neural networks — for language modeling and other tasks

GRUs and LSTMs — for machine translation

Recursive neural networks — for parsing

Recursive neural networks — for different tasks (e.g. sentiment analysis)

Convolutional neural networks — for sentence classification

The future of Deep Learning for NLP: Dynamic Memory Networks

Machine learning and artificial neural networks

Generative models

Restricted Boltzmann Machine

Contrastive Divergence algorithm

Deep Belief Network

Back propagation algorithm

Logistic regression and Soft max regression

Unsupervised pertaining in deep neural networks

 

Regularization in neural networks

Dropout

Convolutional Neural Networks

Invariance, stability

Variability models (deformation model, stochastic model).

Scattering networks

Group Formalism

Supervised Learning: classification

Properties of CNN representations: invertibility, stability, invariance

Covariance/invariance: capsules and related models.

Connections with other models: dictionary learning, LISTA.

Other tasks: localization, regression.

Embeddings (DrLim), inverse problems

Extensions to non-euclidean domains

Dynamical systems: RNNs.

Deep Unsupervised Learning

Auto encoders (standard, denoising, contractive, etc)

Variational Autoencoders

Adversarial Generative Networks

Maximum Entropy Distributions

Non-convex optimization for deep networks

Stochastic Optimization

Attention and Memory Models

Artificial Intelligence

Introduction

Overview

Agents: Perception, Decisions, and Actuation

Search and Planning

Uninformed Search (Depth-First, Breadth-First, Uniform-Cost)

Informed Search (A*, Greedy Search)

Heuristics and Optimality

Constraint Satisfaction Problems

Backtracking Search

Constraint Propagation (Arc Consistency)

 

Exploiting Graph Structure

Game Trees and Tree-Structured Computation

Minimax, Expectimax, Combinations

Evaluation Functions and Approximations

Alpha-Beta Pruning

Decision Theory

Preferences, Rationality, and Utilities

Maximum Expected Utility

Markov Decision Processes

Policies, Rewards, and Values

Value Iteration

Policy Iteration

Reinforcement Learning

TD/Q Learning

Exploration

Approximation

Geospatial Analysis

GIS Principles and Technology

Mapping Science

Principles of Spatial Analysis

Representation, Structures and Algorithms

Airborne Data Acquisition

Climate Modeling

Geo demographics and Population Geography

GIS Design

Network and Locational Analysis

Spatial Decision Support Systems

Spatio-temporal Analysis and Data Mining

Surface Water Modeling

Terrestrial Carbon: Modeling and Monitoring

Web and Mobile GIS

Big Data analysis

Hadoop

Introduction to big data

Sources of big data

Hadoop distributed file system

Statistical Analysis of Big Data

Tools: R, SPSS, SAS, SAS Enterprise Miner, WEKA, Excel

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