Advanced Certificate in Applied Automation in the Digital Economy Module 4: Data Analytics for Automation Decision Making (Synchronous E-Learning)

Training Provider: SINGAPORE MANAGEMENT UNIVERSITY
Course Reference: TGS-2024051840
S$600
Original: S$2,000
Save S$1,400

About This Course

This module is designed to equip participants with the knowledge and skills required to leverage data analytics in making informed decisions within an automated environment. Key topics include an introduction to data analytics fundamentals, data visualisation techniques for deriving meaningful insights, the application of machine learning algorithms for automated decision-making, predictive modeling and forecasting methodologies using advanced statistical tools, and ethical considerations surrounding data-driven decision-making processes. At the end of the module, participants will gain essential competencies to utilise data analytics effectively within automated systems. This will empower them to make informed decisions, thereby fostering efficiency and innovation in digital economies.

What You'll Learn

At the end of the 2-day module, participants will be able to:
• Understand the fundamental principles of data analytics and its role in automation decision making within the context of digital economy
• Develop proficiency in using relevant data analytics tools and techniques to analyse large datasets for identifying patterns, trends, and insights that can inform automation decision making
• Demonstrate the ability to apply statistical methods and machine learning algorithms to derive actionable conclusions from data for optimising automated processes in a digital economy setting
• Evaluate the ethical considerations and potential biases associated with utilising data analytics for automation decision making, and propose strategies to mitigate these challenges
• Design and implement a comprehensive data-driven approach to automate decision-making processes within a digital economy framework, considering factors such as scalability, reliability, and adaptability

Entry Requirements

Nil

Course Details

Duration 15 hours
Language English
Training Commitment Not specified
Total Enrolled New course
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Note: To apply for this course, visit the SkillsFuture website or contact the training provider directly.

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