Big data Analytics

Reading Time: 2 minutes

Big Data Analytics

    Module 1

    • Introduction to Data Science
    • Analytical Terminology, Analytical Methodology
    • Introduction to SAS, R, R-studio interface
    • Data Collection, Creating Datasets
    • Reading Data From External Files (.Txt, .Xls, .Csv) — Tasks
    • Data Exploration: Proc Print, Proc Contents —Tasks
    • Data Exploration: ProcGchart, ProcGplot —- Tasks
    • Data Exploration: Statistical Terminology
    • Data Exploration: Understanding Probability
    • Data Exploration : Analyzing Categorical Data (ProcFreq) — Tasks
    • Data Exploration : Hypothesis, Types of Errors
    • Data Preparation: Arranging the data: Proc Sort, Proc Format-Tasks
    • Data Preparation: Keeping, Dropping, Renaming, Transposing
    • Data Preparation: Using SAS Functions — Tasks
    • Data Preparation: Conditional Processing, By group Processing -Tasks
    • Data Preparation: Combining Data sets — Tasks
    • Data Preparation: Do-Loops, Arrays
    • Statistics: ProcFreq
    • Statistics: ProcTtest, ProcAnova
    • Proc Npar1way

    Module 2

    • Data Mining: Introduction
    • Introduction to Regression: ProcCorr, ProcReg
    • Dimensionality Reduction Techniques: Proc Factor
    • Dimensionality Reduction Techniques: ProcPrincomp
    • Dimensionality Reduction Techniques: ProcDiscrim
    • Clustering: Introduction
    • Clustering case study — Task
    • Association Rules — Introduction
    • Association Rules — Case study: Task
    • Density Estimation: Proc KDE

    Module 3

    • ProcReg: Case study — Task
    • ProcReg: Model Diagnostics — Task
    • Introduction to Logistic Regression
    • Proc Logistic -Case study, — Task
    • Introduction to Decision Trees
    • ProcDtree, Case study — Task
    • Introduction to SVM, Naive Bayes, Case study
    • Introduction to Neural nets,
    • Neural Nets – the Case study
    • Introduction to KNN, Case study — Task
    • Introduction to Bagging and Boosting
    • Ensemble methods Case study
    • Reinforcement Learning

    Module 4

    • Introduction to Time series
    • Proc Arima — Case study — Task
    • Introduction to Text Analytics
    • Sentiment Analysis in R — Case study
    • Introduction to Optimization
    • Optimization — Case study