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Data Science and Big Data Analytics Overview

  • 1. Big Data Analytics

    • Big Data

    • State of the Practice in Analytics

    • Data Scientist

    • Big Data Analytics in Industry Verticals

  • 2. Data Analytics Lifecycle

    • Discovery

    • Data Preparation

    • Model Planning

    • Model Building

    • Communicating Results

    • Operationalizing

  • 3. Basic Data Analytic Methods Using R

    • Using R to Look at Data

    • Analyzing and Exploring the Data

    • Statistics for Model Building and Evaluation

  • 4. Advanced Analytics: Theory and Methods

    • K Means Clustering

    • Association Rules

    • Linear Regression

    • Logistic Regression

    • Nave Bayesian Classifier

    • Decision Trees

    • Time Series Analysis

    • Text Analysis

  • 5. Advanced Analytics: Technologies and Tools

    • Analytics for Unstructured Data

      • MapReduce and Hadoop

    • Hadoop Ecosystem

      • In-Database Analytics: SQL Essentials

      • Advanced SQL and MADlib for In-Database Analytics

  • 6. Putting it All Together

    • Operationalizing an Analytics Project

    • Creating the Final Deliverables

    • Data Visualization Techniques

    • Final Lab Exercise on Big Data Analytics


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