ACE 2017  Keynote Addresses: Prof. Mark S. Anderson, University of California, USA Prof. Stephen Foster, University of New South Wales, Australia Prof. Tommy Chan, Queensland University of Technology, Australia Prof. Mark Burry, University of Melbourne, Australia Prof. Peter Anderson, California College of the Arts, USA.

Certified Business Analytics Specialist (CBAS)

Introduction and Overview

Business Analytics is the emerging and fastest growing technology which every organization is embracing. As per Gartner’s prediction by 2014, 30% of analytic applications will use proactive, predictive and forecasting capabilities, and the software market for business intelligence, analytics and corporate performance management grew by 13.4% in 2010 to $10.5 billion and would continue to grow.

This specialized course covers the concept of Business Analytics and its strategic importance to any organization. The course deals with basic principles, concepts, techniques and tools used in business analytics landscape which includes data mining, data warehouse, data mart, business intelligence, business analytics. Also, this course covers different types of business analytics with real life use cases including text analytics and web analytics. Participants will get good picture of all these concepts and how they all are interconnected to each other in organizational context.

Through this course participants will acquire knowledge on how to use business analytics strategically in organization and to get the most benefit out of it. Also, they will learn to select appropriate type of business analytics based on organizational needs. Participants will actively step through the industry standard process for data mining and realize why an advanced degree in statistics, mathematics or computer science is no longer needed to implement predictive analytics. Live working sessions reveal real-world obstacles and breakthroughs from which to interpret, learn and apply.

As part of the course, participant will be given a case study and it would cover all the aspects of the Business Analytics, right from making a Data/Information Architecture for the case study to the cycle of CRISP-DM and finally converting all the requirements into predictive models using Rapid Miner tool.

Duration

  • 32 Hours (4 Days)

Who Should Attend?

  • Project Managers
  • Business/IT Consultants
  • Risk Analyst
  • Business Analyst
  • Data Analyst – Statistics and Mining
  • Manager- Statistics and Mining
  • IT/IS Executives and Managers
  • Line-of-Business Executives
  • Functional Managers
  • Technology Planners
  • Data Scientist

Pre-Requisite

Participants are recommended to have preferably min. 2 years of experience in software development, business domain or data/business analysis. However, if you do not have any experiences, you can still consider taking up the course and we will advise you accordingly.

Assessment and Certification

  • Component 1: Written Examination (MCQ)
    • 40 Questions
    • 1 Hour duration
    • Closed Book
    • Score 70% to pass
  • Component 2: Project Work Component (PWC)
    • Individual work
    • 2 weeks to complete from the last day of course
    • Score 70% to pass
  • Certification
    • Upon passing the course, you will be awarded “Certified Business Analytics Specialist
    • Certification body – Global Science and Technology Forum

Funding

  • CITREP II (IDA)
    • Supports 70% funding of the nett payable course and certification / assessment fee.
    • Eligible for Singapore citizens only.
    • Valid for courses and examinations commencing on or before 31 March 2015.
    • Terms and Conditions apply. Please visit www.ida.gov.sg/citrep for full details or contact 6327 0164 for enquiry.

Course Outcome

  • An understanding of the Business Analytics and its impact on enterprises
  • Understand the data/information architecture
  • Understand the role of data in analytics
  • Understand the data mining methodology
  • Understand the various phases of CRISP-DM standard and the importance of each phase in DM projects
  • Understand the weightage, scores of DM models and learn how to interpret results

Course Outline

Introduction to Business Analytics

  • Understand the concept of Business Analytics
  • Why it is important and of great value to any organization
  • Main motivation behind the evolution of Business Analytics

Types of Analytics

  • Various type of analytics
    • Descriptive Analytics
    • Predictive Analytics
    • Prescriptive Analytics
  • Text Analytics and Web Analytics
  • Understand the use of various analytics methods
  • Understand the application usage of each one

Data/Information Architecture

  • Understand the Data/Information Architecture of any organization
  • Concept of Data Warehouse/Enterprise Data Warehouse (EDW)
  • Concept of Data Mart
  • Understand Business Intelligence
  • Understand the complete picture of how analytics will be applied in a

Data Quality in Analytics

  • Understand the importance of data in analytics
  • Data quality process
  • Various data structures
  • Understand the data uniqueness
  • Data privacy concerns
  • Data governance

Data Mining and Analytics

  • Understand the Data Mining process
  • Concept of data mining
  • Data mining objectives
  • Data mining definition
  • What data mining is and what is not data mining
  • Convergence of three key areas in data mining

Data Mining Process

  • Understand the concept of KDD
  • Understand modeling in Data Mining
  • Understand the difference between data mining models and statistical models
  • Understand the scoring in data mining models
  • Understand the data mining process

CRISP-DM Standard

  • Understand the need for standards in Data Mining
  • Main phases of CRISP-DM
  • Understand main phases and workbench streams of CRISP-DM and its importance

Data Mining Techniques

  • Understand the various data mining techniques
    • Statistical Models
    • Supervised Machine Learning Models
    • Unsupervised Machine Learning Models
  • Understand the 5 important DM techniques
  • Understand how correlation and association rule mining techniques works
  • Understand the working of regression models
  • Understand the clustering techniques
  • Understanding the Predictive Analytics techniques

Data Mining Tools

  • Explore the various tools for Data Mining in the market
  • Explore and understand the standards in Data Mining Software Tools
  • Differentiate the pros and cons of each commercial software tools
  • Look at the open source tools in the market

Case Study with Software Tool

  • Understand the RapidMiner framework
  • Explore various features of RapidMiner
  • Walkthrough various business use cases with RapidMiner