• 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

• 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, gmodes, car, vcd, Hmisc, psych, doby etc)
• R Packages for Graphical Analysis (base, ggplot, lattice,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 & modelling process
• Popular modelling algorithms
• Types of Business problems – Mapping of Techniques
• Different Phases of Predictive Modelling

DATA PREPARATION

• 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 Trade off) & 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-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

• What is 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 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.

This course Start with  introduction to Data Science and Statistics using R Language. It covers both the  aspects of Statistical concepts and the practical implementation using  R Language. If you’re new to Programming, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.

Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Data frames to manipulate data with ease.

Machine Learning and Data Science :  Data Science With R course effectively covers Data analytics, statistical predictive modelling and machine learning through various practical examples and projects

Real life examples: Every concept is explained with the help of examples, case studies and source code wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context

Introduction to Data Science With R

Data Importing / Exporting

Data Manipulation

Data Analysis – Visualization

Introduction To Statistics

Predictive Modelling

Data Exploration For Modeling

Data Preparation

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who want R training with detailed focus on Data Science and Machine Learning applications.

• Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in the Data Science Industry or want to be future Data Scientists.
• Engineers who want to use a distributed computing engine for batch or stream processing or both
• Analysts who want to leverage Spark for analyzing interesting datasets
• Data Scientists who want a single engine for analyzing and modelling data 
• MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
• Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
• Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being  data scientists
• Professionals who’ve worked mostly with tools like Excel and want to learn how to use R  for statistical analysis.