Date: 30 May 2023 | 9am-5.30pm | Classroom Learning
Duration: 1 Day
In this course, participants will explore optimization models and software tools to provide them with a set of key skills in the area of prescriptive analytics. Prescriptive analytics is an extension of what happened (descriptive), and what will happen (predictive) to an optimal course of action to follow. By using optimization models and available tools, participants can determine the near-term outcomes and make the best decisions for their organizations.
As they evaluate business objectives, key metrics such as profitability improvement, cost reductions, and revenue can be optimized with prescriptive analytics. During this program, they will analyse business cases including scheduling planning, supply chain, transportation and investment portfolios, and learn to model business problems as linear programmes. They will build knowledge of linear programming and use the tools of business analytics to create value in any organisation.
Participants will learn how to carry out prescriptive analytics using open source software such as Excel add-in/Open Solver or R for optimization.
This module is part of Professional Certificate in Business Analytics.
This course will enable participants to:
- Understand how to perform prescriptive analytics with optimisation techniques
- Understand the fundamental key concepts of linear, integer programming and decision analysis
- Understand how to model business problems for decision optimisation, interpret sensitivity analysis and shadow prices
- Acquire basic proficiency in popular data analytics tools such as Excel add-in and/or R
- Apply hands-on techniques to solve optimisation models, linear and integer programming and decision analysis
- Decide which prescriptive analytics approach to use for different situations
Who Should Attend
Business Analyst, Data Analyst, Data Engineer, Analytics Consultant, Business Intelligence Manager and Program Manager. Professional who are keen to explore work opportunities in analytics.
At least a polytechnic diploma.
(Click their photos to view their short biographies)
Ms Samantha Sow
Ms Samantha Sow
Ms. Samantha Sow is currently a Senior Lecturer in the department of Information Systems and Analytics at the National University of Singapore (NUS). She has over 8+ years of experience in Business Analytics and Data Science and she conducts training for both government agencies and corporate clients. Before joining NUS, she lectures at Temasek Polytechnic, teaching Business Analytics and Data Science to professionals, managers and executives (PMEs). Prior to being an academia, Samantha worked in the research and development at Infineon Technologies. Her research and project areas include Business Analytics, Data Mining, Predictive and Prescriptive Analytics.
She has a passion for engaging and inspiring participants to enhance their workplace analytics capabilities and increase business intelligence quotient within their organisations. Her interests lie in the applications of data analytics, predictive modelling and optimization techniques to derive actionable insights for commercial effectiveness. She is familiar with typical analytics tools such as Python, R, and SAS, SPSS and Tableau. She also has working knowledge in the area of Analytics, Data Science and Machine Learning.
Samantha completed her Master of Education from University of Sheffield and graduated from the National University of Singapore with a Bachelor’s in Engineering, First Class Honours. She has also completed THEC (Teaching in Higher Education) and ACTA (Advanced Certificate in Training and Assessment). She is a member of the adult associate educator (AEN) by Institute for Adult Learning (IAL), Singapore.
Her certifications include:
Microsoft Certified Azure Data Scientist Associate. Microsoft Certified Data Analyst Associate. SAS Certified Predictive Modeller in SAS Enterprise Miner. Tableau Certified Desktop Specialist.
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email firstname.lastname@example.org
To register, click Register
Course Code: TGS-2022015667 (Classroom Learning) / TGS-2022015676 (Synchronous e-learning)
For members of public and NUS Alumnus (without R&G Voucher), please follow the steps below:
Select Short Course / Modular Course -> Apply for Myself -> Browse Academic Modules / Short Courses-> Module/Course Category -> Short Courses -> Browse Courses-> Advanced Computing for Exe (Faculty/Department / Unit)
Please download the user guide for NUS Online Application Portal after you click ‘Apply for Myself’ if you need assistance.
Course Fee Breakdown
Singapore Citizens39 years old or younger
Singapore Citizen40 years old or older