Singapore University of Social Sciences

Data Analytics for Decision Makers

Data Analytics for Decision Makers (ANL551)

Applications Open: 01 October 2021

Applications Close: 30 November 2021

Next Available Intake: January 2022

Course Types: Modular Graduate Course, SkillsFuture Series

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Business Administration

Schemes: Alumni Continuing Education + (ACE+), Lifelong Learning Credit (L2C), Resilience

Funding: SkillsFuture

School/Department: School of Business


Synopsis

Data Analytics for Decision Making presents Data Analytics as a key modern approach for decision making in business organisations. It examines the key aspects of Business Analytics based on Cross-Industry Process for Data Mining (CRISP-DM) framework. Students will learn to apply CRISP-DM by going through a series of projects involving data visualisation, association rule mining, clustering, predictive modelling and response modelling. By walking students through such projects, they will gain experience in turning data into important insights that may improve organisational performance.

Level: 5
Credit Units: 5
Presentation Pattern: Every January

Topics

  • Introduction to Business Analytics
  • CRISP-DM--- An Overview
  • Introduction to Data Visualisation
  • Process and challenges in a Data Visualisation project
  • Association Rule Mining
  • Process and challenges in an Association Rule Mining project
  • Data Clustering
  • Process and challenges in a Data Clustering project
  • Predictive Modelling
  • Process and challenges in a Predictive Modelling project
  • Process and challenges in a Response Modelling project

Learning Outcome

  • Design Analytics Solutions using the CRISP-DM framework
  • Appraise the suitability of analytics techniques in different contexts
  • Evaluate performance of analytics models
  • Assess the quality of data for analytics
  • Prepare data for mining and analysis
  • Construct an analytics solution using application software
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