• 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)
• Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and
scipy.stats 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 Chisquare
• Important modules for statistical methods: Numpy, Scipy, Pandas

• 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
• Variable Reduction Techniques - Factor & PCA Analysis

• Introduction to Segmentation
• 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

• 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

• 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

• 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 Tradeoff) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Overview of gradient descent algorithm
• 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 )

• What is the segmentation & Role of ML in Segmentation?
• Concept of Distance and related math background
• K-Means Clustering
• Expectation Maximization
• Hierarchical Clustering
• Spectral Clustering (DBSCAN)
• Principle component analysis (PCA)

• 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

• 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

• 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

• Motivation for Support Vector Machine & Applications
• Support Vector Regression
• Support vector classifier (Linear & Non-Linear)
• Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization, and Soft Constraints)
• Interpretation of Outputs and Fine tune the models with hyper parameters
• Validating SVM models
 

• 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

• Concept of Conditional Probability
• Bayes Theorem and Its Applications
• Naïve Bayes for classification
• Applications of Naïve Bayes in Classifications

• 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)
• Finding patterns in text: text mining, text as a graph
• Natural Language processing (NLP)
• Text Analytics – Sentiment Analysis using Python
• Text Analytics – Word cloud analysis using Python
• 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 python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
• Fine tuning the models using Hyper parameters, grid search, piping etc.

•    While loop
•    If loop
•    For loop
•    Arithmetic operations

•   Correlation
•   Linear Regression
•   Non-Linear Regression
•   Predictive time series forecasting
•   K means clustering
•   P-value
•   Find outlier
•   Neural Network
•   Error Measure

•   Overture of R Shiny
•    What is Hadoop
•    Integration of Hadoop in R
•    Data Mining using R
•    Clinical research preface in R
•    API in R (Twitter and Facebook)
•    Word Cloud in R