Participants will be equipped with industry-relevant skillset of investigating data attributes to establish correlation and discover the underlying patterns using techniques such as correlation analysis, association rules and k-means clustering. Participants will also learn how to create interactive dashboards and engaging reports using Business Intelligence tools. Participants will also acquire the skillset of developing predictive model with a process-based tool. As part of the business analytics course, participants will review a case study which covers all the aspects of the business analytics, from engineering a Data-Information Architecture to the framework of CRISP-DM and finally translating all the business requirements to build a predictive model.

Learning Outcome

At the end of this course, participants will:

  • Acquire knowledge on business analytics and its impact on enterprises with several use cases
  • Acquire concepts and techniques used in business analytics landscape which includes data mining, data warehouse, data mart and business intelligence
  • Gain a solid understanding in the statistical and analytical methods that make up the backbone of data science
  • Acquire key predictive modelling techniques and apply them to solve real life problems
  • Acquire knowledge on the end-to-end process of developing and deploying a data mining model to solve a business problem using the CRISP-DM framework

Who should Attend?

  • IT Executives/Managers
  • Risk Analyst/Management
  • Business Analyst
  • Data Analyst
  • Banking Executives/Managers
  • Software Engineers
  • System Engineers

Eligibility Criteria

2 years experience (recommended)

  • Software development
  • Business domain
  • Data/Business analysis 
This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page

Information as accurate as of 20 May 2021