Big Data Analytics

In today’s digital economy, data can be found everywhere. But how much of these data are actually useful and valuable? Predictive analytics help organizations derives values from data by utilizing different tools and techniques.

Furnished with these insights, organizations will be able to compete better and provide offerings that exceed customers’ expectations.

Course Content:

• This course focuses on fundamental understanding of big data, statistical concepts, predictive analytics and includes hands-on on building predictive models
• 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

Objectives:

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

Who Should Attend:

• This course is designed for those are interested in exploring the potential values of their organizational data and deriving insights from these data. This includes managers and executives from various departments such as Marketing, Operations, and HR.