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Data Science and Big Data Analytics (MR-1CP-DSBDA)

Course Description Schedule Course Outline

Detailed Course Outline

The following modules and lessons included in this course are designed to support the course objectives:

  • Introduction and Course Agenda
  • Introduction to Big Data Analytics

-Big Data Overview
-State of the Practice in Analytics
-The Data Scientist
-Big Data Analytics in Industry Verticals

  • Data Analytics Lifecycle

- Discovery
- Data Preparation
- Model Planning
- Model Building
- Communicating Results
- Operationalizing

  • Review of Basic Data Analytic Methods Using R

- Using R to Look at Data – Introduction to R
- Analyzing and Exploring the Data
- Statistics for Model Building and Evaluation

  • Advanced Analytics – Theory And Methods

- K Means Clustering
- Association Rules
- Linear Regression
- Logistic Regression
- Naïve Bayesian Classifier
- Decision Trees
- Time Series Analysis
- Text Analysis

  • Advanced Analytics - Technologies and Tools

- Analytics for Unstructured Data - MapReduce and Hadoop
- The Hadoop Ecosystem
- In-database Analytics – SQL Essentials
- Advanced SQL and MADlib for In-database Analytics

  • The Endgame, or Putting it All Together

- Operationalizing an Analytics Project
- Creating the Final Deliverables
- Data Visualization Techniques
- Final Lab Exercise on Big Data Analytics