Data Science with R

Module 1: Introduction to Data Science Methodologies

• Data Types
• Introduction to Data Science Tools
• Statistics
• Approach to Business Problems
• Numerical Categorical
• R, Python, WEKA, RapidMiner

Module 2: Correlation / AssociationRegressionCategorical variables

• Introduction to Correlation Spearman Rank Correlation
• OLS Regression – Simple and Multiple Dummy variables
• Multiple regression
• Assumptions violation – MLE estimates
• Using UCI ML repository dataset or Built-in R dataset

Module 3: Data Preparation

• Data preparation & Variable identification
• Advanced regression
• Parameter Estimation / Interpretation
• Robust Regression
• Accuracy in Parameter Estimation
• Using UCI ML repository dataset or Built-in R dataset

Module 4: Logistic Regression

• Introduction to Logistic Regression
• Logit Function
• Training-Validation approach
• Lift charts
• Decile Analysis
• Using UCI ML repository dataset or Built-in R dataset

Module 5: Cluster Analysis Classification Models

• Introduction to Cluster Techniques
• Distance Methodologies
• Hierarchical and Non-Hierarchical Procedure
• K-Means clustering
• Introduction to decision trees/segmentation with Case Study
• Using UCI ML repository dataset or Built-in R dataset

Module 6: Introduction and to Forecasting Techniques/

• Introduction to Time Series
• Data and Analysis
• Decomposition of Time Series
• Trend and Seasonality detection and forecasting
• Exponential Smoothing
• Building R Dataset
• Sales forecasting Case Study

Module 7: Advanced Time Series Modeling

• Box – Jenkins Methodology
• Introduction to Auto Regression and Moving Averages, ACF, PACF
• Detecting order of ARIMA processes
• Seasonal ARIMA Models (P,D,Q)(p,d,q)
• Introduction to Multivariate Time-series Analysis
• Using built-in R datasets

Module 8: Stock market prediction

• Live example/ live project
• Using client given stock prices / taking stock price data

Module 9: Pharmaceuticals

• Case Study with the Data
• Based on open set data

Module 10: Market Research

• Case Study with the Data
• Based on open set data

Module 11: Machine Learning

• Supervised Learning Techniques
• Conceptual Overview
• Unsupervised Learning Techniques
• Association Rule Mining Segmentation

Module 12: Fraud Analytics

• Fraud Identification Process in Parts procuring
• Sample data from online
• Text Analytics

Module 13: Text Analytics

• Sample text from online

Module 14: Social Media Analytics

• Social Media Analytics
• Sample text from online