Data Mining and Analysis Training (DMAT)

The data age is here. Almost every decision made today whether it be a consumer choice, or a business decision is based on data. Everything from smartphone and tablet, to your games console and fitness tracker rely on enterprise cloud computing and data collected has become an invaluable commodity for the businesses that analyse and utilize it.

The exponential growth in sensors and the data they captured will further drive the data age. In order to fully realise the smart living dream, a vast amount of network of sensors will be part of the infrastructure. By understanding the data collected and generating actionable insights from it will be the key to the success and continuous innovation in the organization.

Course Objective
Data Mining and Analysis Training Course (DMAT) is a program designed anyone interested in acquiring knowledge and skills on Advanced Analytics. Participants will gain knowledge on data mining and how analytics tools and be used to identify trends and insights.
Course Duration
32 hours / Four-Day
Pre-Requisite
NA
Examination
Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Advanced Analytics based on the syllabus covered
Certification
Participants will be awarded a Certificate of Completion upon completion of the course
Who Will Benefit from the Course
Data Mining and Analysis Training Course (DMAT) is designed for Professionals and Anyone interested in acquiring advanced knowledge on utilizing and implementing Data Analytics in their decision making process

Participants will have the opportunity to learn in a supportive and encouraging environment.

Class is limited to 20 participants as hands-on sessions and real-time demonstration is expected.
Course Outline
MODULE 1: INTRODUCTION TO DATA ANALYTICS  

  • What is Data Analytics?
  • Methods and Sources of Data Mining
  • What is Required?
    • MODULE 2: UNDERSTANDING DATA MINING  

  • Introduction to Data Mining
  • Key tasks in Data Mining
  • Use and Applications
    • MODULE 3: DATA MINING PROCESS  

  • Data Preprocessing
  • Statistical Inference
  • Multivariate Linear Regression
    • MODULE 4: CLASSIFICATION METHODS  

  • Different Classification Methods
  • Neural Networks
  • Decision Trees
  • Bagging, Random Forests, Boosting
  • Support Vector Machines and Flexible
    • MODULE 5: PRINCIPAL COMPONENTS ANALYSIS  

  • Introduction to Principal Components Analysis
  • Clustering
  • Model Assessment and Selection
    • MODULE 6: HANDS-ON EXERCISE AND DISCUSSION