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Big Data Analysis with R programming


Part-I : Basic Analytics Hours
Descriptive Statistics Introduction to Advanced Data Analytics 7
Statistical inferences for various Business problems
Types of Variables, measures of central tendency and dispersion
Variable Distributions and Probability Distributions
Normal Distribution and Properties
Case Studies discussion for Part-I Session 1
Test of Hypothesis Null/Alternative Hypothesis formulation 7
One Sample, two sample (Paired and Independent) T/Z Test
P Value Interpretation
Analysis of Variance (ANOVA)
Chi Square Test
Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)
Case Studies discussion for Part-I Session 2
Multivariate Regression Introduction to Correlation - Karl Pearson and Graphical Methods 7
Spearman Rank Correlation
OLS Regression - Simple and Multiple
Case Studies discussion for Part-I Session 3

Part-II : Advanced Analytics Hours
Logistic Regression Non Linear Regressions using Link functions 7
Logit Link Function
Binomial Propensity Modeling
Training-Validation approach
Case Studies discussion for Part-II Session 1
Factor Analysis Introduction to Factor Analysis – PCA
Reliability Test 4
KMO MSA tests, Eigen Value Interpretation
Factor Rotation and Extraction
Case Studies & Articles discussion for Part-II Session 2
Cluster Analysis Introduction to Cluster Techniques 4
Distance Methodologies
Hierarchical and Non-Hierarchical Procedures
K-Means clustering
Wards Method
Case Studies discussion for Part-II Session 3




Part-III : Time Series Analysis Hours
Introduction and Exponential Smoothening Introduction to Time Series Data and Analysis 7
Decomposition of Time Series
Trend and Seasonality detection and forecasting
Exponential Smoothing (Single, double and triple)
Case Studies discussion for Part-III Session 1
ARIMA Modeling Box - Jenkins Methodology 7
Introduction to Auto Regression and Moving Averages, ACF, PACF
Case Studies discussion for Part-III Session 2



Part IV : Data Mining Hours
Introduction to Excel Miner Environment Excel Miner GUI Familiarization 2
Rattle Tabs
Data Import and Variable role setting
Data Exploration and Visualization, Hypothesis Testing
Data Manipulation, Standardization, Missing value Treatment
Case Studies discussion for Part IV Session 1
Statistical Analysis & Data Mining/Machine Learning Cluster Analysis using R-Rattle 6
Association Rule Mining
Predictive Modeling using
Decision Trees
Logistic Regression
Case Studies discussion for Part IV Session 2


Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data 2
ROC, Sensitivity, Specificity, Lift charts, Error Matrix
Deploying models using Score options
Opening and Saving models using Rattle
Case Studies discussion for Part IV Session 3