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Short Course

Applied Computing Project

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
To be advised5Classroom LearningTGS-2024047633
To be advised5Synchronous e-LearningTGS-2024047548

Course Overview

This course is designed to provide participants with an opportunity to apply the knowledge and skills acquired throughout their academic training to a practical, real-world project. Through this course, participants will explore and solve problems relevant to their field of study. Emphasis will be placed on project planning, execution, documentation, and presentation.

Throughout the course, participants will work under the guidance of the instructors who will provide feedback as they progress through their projects. The course will end with a presentation of the final project deliverable, which may take the form of a prototype, a software application, a design, an experiment, or other relevant outputs, depending on the nature of the project.

Course Objectives

At the end of the course, participants will be able to:

  • Identify and define real-world problems
  • Acquire, process, and analyse relevant data, information, or requirements
  • Develop and implement tailored solution to address the identified problem
  • Test, validate, and document the effectiveness of the implemented solutions
  • Present and communicate project outcomes effectively to stakeholders

Who Should Attend

  • Data Scientists
  • Artificial Intelligence (AI) and Machine Learning (ML) Engineers
  • Software Engineers

Prerequisites

The minimum entry requirement is a diploma.

Contact Us

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Types of Available Fundings

  • SkillsFuture Funding
  • SkillsFuture Credit
  • Enhanced Training Support
  • SFEC

    • Instructors


    Dr Ai Xin

    Dr Ai Xin

    Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience teaching Artificial Intelligence and Data Science courses such as machine learning, deep learning and data mining. She graduated from NUS with a PhD degree in Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks. She worked at BHP Billiton Marketing Asia for eight years, gaining extensive industry experience in various functions such as risk management, supply chain management, and sales and marketing planning.

    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications. He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007. Dr Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.

    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.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$5,250.00 S$5,250.00 S$5,250.00 S$5,250.00 S$5,250.00
    Less: SSG Grant Amount S$3,675.00 S$3,675.00 S$3,675.00 S$3,675.00 -
    Nett Programme Fee S$1,575.00 S$1,575.00 S$1,575.00 S$1,575.00 S$5,250.00
    9% GST on Nett Programme Fee S$141.75 S$141.75 S$141.75 S$141.75 S$472.50
    Total Nett Programme Fee Payable, Including GST S$1,716.75 S$1,716.75 S$1,716.75 S$1,716.75 S$5,722.50
    Less Additional Funding - S$1,050.00 - S$1,050.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$1,716.75 S$666.75 S$1,716.75 S$666.75 S$5,722.50
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    3. Please note that all external funding for courses is limited in duration and subject to eligibility and availability.

    Advanced Computing for Executives

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Email: soc-ace@nus.edu.sg

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