Data Science with R

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Data Science with R Language

    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 AnalysisClassification 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