Hyperautomation

Synopsis

ANL505 Hyperautomation aims to equip students with practical knowledge and skills using workflow automation tools, process discovery, process mining, inter-business process management system, low-code, business rules engine, optical character recognition (OCR) with robotic process automation (RPA), and machine learning (ML) in data acquisition and analysis. Students will learn essential concepts, skills, and techniques in machine learning, JavaScript and Python to design and implement workflows in a scalable and reproducible manner. By the end of this course, students will be competent in using RPA and ML for data acquisition, analysis, and automated reporting.

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

Topics

Introduction to hyperautomation
Megatrends in hyperautomation and robotics
Introduction to Software-as-a-Service
Essentials of Configuration Management Database (CMDB)
Introduction to robotic process automation (RPA)
Use of optical character recognition (OCR) capabilities
Essential Python programming
Python debugging and logging best practices
Introduction to artificial intelligence and neural networks
Essential mathematics for neural networks
Implementing neural networks and machine learning
Evaluating machine learning models

Learning Outcome

Assess hyperautomation approaches based on mega trends and data configuration
Design data acquisition types and methods to populate CMDB with enterprise level tools
Formulate possible machine learning solutions for different problems
Evaluate performance of machine learning models
Create table structures, workflow, and front-end scripts to meet automation needs with enterprise grade tools
Construct scripted workflows for automation with enterprise grade tools

Part-time Programmes / CET Courses

Singapore University of Social Sciences

Hyperautomation