Data Technology And Management For IT Professionals
23 Mar, 24 Mar 2023 | 9am-5.30pm | Online (Corporate Run)
8 Jun, 9 Jun 2023 | 9am-5.30pm | Online (Corporate Run)
Duration: 2 Days
This introductory course has been developed to introduce modern data management technologies to participants. Among the topics to be analysed are the definition of basic data management techniques and technologies, the background of such technologies, and the future trends of Data Technology and Management (DTM).
This course will also explain the differences between various Data Management approaches, environments, and tools, as well as the advantages and disadvantages associated with their diverse applications. Emerging trends in the field of DTM will be reviewed as well.
At the end of the course, participants will be able to:
• Understand the basic terms of Data Technology and Management (DTM), along with the characteristics and applications of different applicable DTM techniques
• Articulate the future trends of DTM
• Compare the advantages and disadvantages of the new DTM platforms and tools in different applications
• Recommend a set of tools for a given problem
Who Should Attend
IT Engineers, IT Managers, IT Executives, IT Consultants
Minimum two years of working experience in the field of IT
(Click their photos to view their short biographies)
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 the 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.
Dr Edmund Low
Dr Edmund Low
Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore.
He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.
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.
NTUC members enjoy 50% unfunded course fee support for up to S$250 each year (or up to S$500 for NTUC members aged 40 years old and above) when you sign up for courses supported under UTAP (Union Training Assistance Programme). Please visit e2i’s website to find out more.
To enquire, email firstname.lastname@example.org
To register, click Register
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