Big Data Analytics

1. Understand the basics of Big Data analytics 2. Understand and apply statistical concepts into business analytics 3. Explore and prepare data for business analytics 4. Apply data mining approaches to extract useful business information from data 5. Use RapidMiner to interpret data and build predictive modeling

Module 1: Introduction to Big Data Analytics -Pre Test -The Concept of Big Data -Big Data Characteristics -Big Data Drivers -Big Data Use Cases -Types of Analytics Module 2: Data Analytics Lifecycle -Data Analytics Phases -Key Roles in Successful Analytics Project -Value of using Data Analytics Lifecycle Module 3: Statistical Concept and Exploratory Data Analysis -Types of data elements -Exploratory Data Analysis -Hypothesis Testing Module 4: Data Analytic Theory and Methods -Machine learning techniques -Supervised Learning -Unsupervised Learning Module 5: RapidMiner Basics -User Interface -Creating and Handling RapidMiner Repositories -Starting a new RapidMiner project -Operators and processes -Loading and Storing Data -Storing processes and results Module 6: Building Predictive Models using RapidMiner -K-Nearest Neighbor -Linear Regression -Decision Trees -Post Test