Data Science
Module 1: Introduction to Data Science with R
 What is Data Science, significance of Data Science in today’s digitallydriven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.

Handson Exercise –Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
Module 2: Data Exploration
 Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, inbuilt functions, conditional, looping statements and userdefined functions, matrix, list and array.

Handson Exercise –Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using userdefined functions in R.
Module 3: Data Manipulation
 Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.

Handson Exercise – Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
Module 4: Data Visualization
 Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatterplot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Barplot, histogram and Density Plot, multivariate distribution, Barplots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequencyplots with geom_freqpoly(), multivariate distribution with scatterplots and smooth lines, continuous vs categorical with boxplots, subgrouping the plots, working with coordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.

Handson Exercise –Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
Module 5: Introduction to Statistics
 Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, ChiSquare testing, ANOVA, normal distribution, binary distribution.

Handson Exercise –– Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
Module 6: Machine Learning
 Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding pvalue, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multivariate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding pvalue, understanding the summary results with Null Hypothesis, pvalue & Fstatistic, building linear models with multiple independent variables.

Handson Exercise –Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.
Module 7: Logistic Regression
 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 simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, reallife applications of Logistic Regression.

Handson Exercise –Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
Module 8: Decision Trees & Random Forest
 What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, prepruning, postpruning, costcomplexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.

Handson Exercise –Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
Module 9: Unsupervised learning
 What is Clustering & it’s Use Cases, what is Kmeans Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, kmeans clustering algorithm, Theoretical aspects of kmeans, and kmeans process flow, Kmeans in R, implementing Kmeans on the dataset and finding the right no. of clusters using Screeplot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.

Handson Exercise –Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, Kmeans clustering for visualizing and interpreting results for the customer churn data.
Module 10: Association Rule Mining & Recommendation Engine
 Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, userbased collaborative filtering & ItemBased Collaborative Filtering, implementing Recommendation Engine in R, userBased and itemBased, Recommendation Usecases.

Handson Exercise –Deploying association analysis as a rulebased machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
Module 11: Introduction to Artificial Intelligence (self paced)
 Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
Module 12: Time Series Analysis (self paced)
 What is Time Series, techniques and 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 (Twitter sentiment analysis), text analysis.

Handson Exercise –Analyzing time series data, sequence of measurements that follow a nonrandom order to identify the nature of phenomenon and to forecast the future values in the series.
Module 13: Support Vector Machine – (SVM) (self paced)
 Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
Module 14: Naïve Bayes (self paced)
 What is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikitlearn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
Module 15: Text Mining (self paced)
 Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term FrequencyInverse Document Frequency (TFIDF), After TFIDF.
Module 16: Case Study The Market Basket Analysis (MBA) case study
 This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
Logistic Regression Case Study
 In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.
Multiple Regression Case Study
 You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.
Receiver Operating Characteristic (ROC) case study
 You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.