Robotics Process Automation Begins With Me
8 Jun 2022 | 9am – 6pm | Online
7 Sept 2022 | 9am – 6pm | Online (Alternative Date)
Duration: 1 Day
Robotics Process Automation (RPA) is the technology that enables computer software to emulate and integrate actions typically performed by us (humans) interacting with digital systems (e.g. a computer). The software that executes these actions is termed a “robot”. Examples of tasks that RPA robots are able to automate include capturing data, running applications and communicating with other systems. By automating processes that are highly manual, repetitive and rule-based, RPA solutions can yield greater productivity, create efficiency and reduce costs. Common internal processes across industries (such as banking, retail, tech and the government) that can benefit from RPA include HR, IT services, supply chain, finance and accounting, and customer management.
The course aims to introduce robotics process automation to participants, and impart basic proficiency in RPA tools so that they are able to write their own RPA bots to automate common work processes in their organisations, upon completion of the course. UiPath is one of the leading tools for implementing RPA.
This module is part of Professional Certificate in Emerging and Disruptive Technologies.
At the end of the course, you will be able to:
- Understand what is RPA and its benefits to business processes
- Design, build and deploy an RPA robot for automation using UiPath
- Apply UiPath to develop RPA solutions for suitable processes in one’s own organisation
- Introduction to RPA and UiPath; the UiPath Studio interface
- Activities, variables and data types
- Recording and control flow
- Data manipulation
Who Should Attend
Engineers, Managers, Executives
Anyone who is interested in using UiPath to automate work processes.
(Click their photos to view their short biographies)
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 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 $250 each year (or up to $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
TGS-2020513883 (Classroom Learning)
TGS-2020503034 (Synchronous e-Learning)
Course Fee Breakdown
Singapore Citizens39 years old or younger
Singapore Citizen40 years old or older