Singapore University of Social Sciences

Hyperautomation

Hyperautomation (ANL505)

Applications Open: 01 April 2021

Applications Close: 31 May 2021

Next Available Intake: July 2021

Course Types: Modular Graduate Course

Language: English

Duration: 6 months

Fees: $2200 View More Details on Fees

Area of Interest: Business Administration

Schemes: Lifelong Learning Credit (L2C)

Funding: To be confirmed

School/Department: School of Business


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
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