The Intelligent IoT Systems and Data Analytics course is a professionally structured, applied training programme designed to equip learners with the capabilities required to design, analyse, and operationalise data-driven IoT systems across enterprise and real-world contexts.
The course addresses both the foundational principles of IoT system architectures and the practical application of data analytics on IoT-generated data, enabling organisations to move from basic device connectivity to insight-driven monitoring, optimisation, and decision support. Learners develop the ability to collect, process, analyse, and interpret large volumes of sensor and telemetry data to support operational efficiency and performance improvement.
The programme emphasises IoT system understanding, data acquisition, preprocessing, exploratory analytics, pattern analysis, anomaly identification, and analytics-driven insight generation, ensuring that intelligent IoT solutions are scalable, reliable, interpretable, and operationally useful.
Learning Objectives
By the end of this course, participants will be able to:
- Understand intelligent IoT system architectures and data flows
- Collect and preprocess IoT sensor data for analytics
- Perform exploratory and descriptive analytics on IoT datasets
- Analyze time-series and streaming-style data
- Identify patterns, trends, and anomalies in sensor data
- Translate IoT analytics results into actionable insights
Learning Outcomes
Participants will demonstrate the ability to:
- Explain how data analytics enhances IoT systems
- Work with real-world IoT datasets
- Apply statistical and exploratory analytics techniques
- Interpret patterns and trends in sensor data
- Communicate analytical insights clearly and effectively
- Conceptualize analytics-driven intelligent IoT solutions
Course Code: TGS-2026061575
Course Duration: 3 days
Course Fee: $1950.00
| TYPE |
Singapore Citizens and Singapore Permanent Residents |
Employer-sponsored and self-sponsored Singapore Citizens aged 40 years old and above |
SME-sponsored Local employees (i.e Singapore Citizens and Singapore Permanent Residents) |
|
SkillsFuture Funding (Baseline) |
SkillsFuture Mid-career Enhanced Subsidy |
SkillsFuture Enhanced Training Support For SMEs |
|
| Course Fee | $1950.00 | $1950.00 | $1950.00 |
| SkillsFuture Funding | $975.00 | $1365.00 | $1365.00 |
| Total Nett Fee | $975.00 | $585.00 | $585.00 |
| GST (9% of Course fee) | $175.50 | $175.50 | $175.50 |
| Total Fee Payable to | $1150.50 | $760.50 | $760.50 |
Foundations of Intelligent IoT Systems
- Understanding the Evolution of IoT: From Connectivity to Intelligence
- Traditional IoT vs. Intelligent IoT Systems
- Role of Data Analytics in IoT Ecosystems
- Overview of Real-World Intelligent IoT Applications
- Business and Operational Motivation for Intelligent IoT
IoT System Architecture
- Understanding IoT System Architecture
- End-to-End IoT Architecture Overview
- Device Layer: Sensors and Actuators
- Gateway and Edge Layer Concepts
- Cloud and Data Platform Layer
- Application and Analytics Layer
- Architectural Considerations for IoT Data Analytics
- Centralised vs. Distributed Intelligence
- Common Architectural Pitfalls
Sensors, Devices, and Data Generation
- Role of Sensors and Devices in IoT Systems
- Types of Sensors in IoT Systems
- IoT Devices and Embedded Systems
- Sensor Data Characteristics and Limitations
- Sampling Rates and Data Frequency
- Resolution and Accuracy of Sensor Measurements
- Event-Driven vs. Continuous Data Generation
- Impact of Data Generation on Analytics
- Reliability and Failure Modes of Sensors
- Data Quality Issues Originating at the Sensor Layer
IoT Data Types and Structures
- Understanding IoT Data Types and Structures
- Structured vs. Semi-Structured IoT Data
- Time-Series Data Fundamentals
- Characteristics of IoT Time-Series Data
- Streaming vs. Batch IoT Data
- Event Data vs. Continuous Measurements
- Metadata in IoT Systems
- Contextual Information and Data Enrichment
- Data Consistency and Schema Evolution
- Data Structuring for Analytics Readiness
- 5 IoT Data Challenges
- Nature of Data Challenges in IoT Systems
- Volume, Velocity, and Variety of IoT Data
- Noise and Data Quality Issues
- Missing Values and Data Gaps
- Data Drift and Changing Behaviour
- Data Reliability and Trustworthiness
- Scalability and Storage Considerations
- Real-Time vs. Historical Analytics Constraints
- Impact of Data Challenges on Analytics Outcomes
- Addressing IoT Data Challenges (Conceptual Overview)
Hands-on Lab: IoT Data Exploration
- Data Preprocessing & Exploratory Analytics for IoT
- IoT Data Preprocessing Techniques
- Importance of Data Preprocessing in IoT Analytics
- Data Cleaning for Sensor Data
- Handling Missing and Corrupt Readings
- Noise in IoT Sensor Data
- Noise Filtering and Smoothing Techniques
- Data Normalization and Scaling
- Preprocessing for Multisensor IoT Data
- Effect of Preprocessing on Analytics Outcomes
- Balancing Automation and Human Judgment
Time-Series Analytics Fundamentals
- Understanding Time-Series Data in IoT Contexts
- Temporal Dependencies in IoT Data
- Trends in Time-Series Data
- Seasonality in IoT Time-Series Data
- Cyclic Patterns vs. Seasonality
- Windowing Techniques in Time-Series Analysis
- Aggregation Techniques for IoT Time-Series Data
- Feature Creation from Time-Series Data
- Importance of Time Alignment and Synchronisation
- Time-Series Granularity and Resolution
- Common Challenges in IoT Time-Series Analytics
Exploratory Data Analysis (EDA) for IoT
- Purpose of Exploratory Data Analysis in IoT Analytics
- Distribution Analysis of IoT Data
- Outlier Detection During EDA
- Temporal Exploration of Sensor Data
- Correlation Analysis Between Sensors
- Multisensor and Multivariate Exploration
- Visual Analytics for IoT Time-Series Data
- Identifying Normal Operational Behaviour
- EDA for Data Quality Assessment
- Role of EDA in the Analytics Lifecycle
Pattern Discovery in IoT Data
- Identifying Normal Operational Patterns
- Temporal Patterns in IoT Data
- Detecting Deviations and Irregular Behaviour
- Multivariate Pattern Analysis
- Relationships and Correlated Behaviour
- Context-Aware Pattern Interpretation
- Pattern Stability and Change Over Time
- Visual Techniques for Pattern Discovery
- Common Pitfalls in Pattern Discovery
Introduction to Anomaly Detection in IoT
- Purpose of Anomaly Detection in IoT Systems
- Understanding Normal vs. Abnormal Behaviour
- Types of Anomalies in IoT Data
- Statistical vs. Data-Driven Anomaly Detection (Conceptual)
- Threshold-Based Detection Concepts
- Challenges of Anomaly Detection in IoT
- False Positives and Operational Impact
- Importance of Context in IoT Anomaly Detection
- Anomaly Detection as a Progressive Capability
- Role of Human-in-the-Loop in IoT Anomaly Detection
- Preparing for Advanced Detection Techniques
Intelligent Analytics & Decision-Making in IoT
- From Descriptive to Predictive Analytics
- Analytics as a Maturity Continuum in IoT Systems
- Descriptive Analytics in IoT Systems
- Limitations of Descriptive Analytics
- Diagnostic Analytics: Understanding Why Things Happen
- Diagnostic Analytics in IoT
- Predictive Analytics in IoT Systems
- Prescriptive Analytics: Conceptual Understanding
- Practical Limits of Prescriptive Analytics in IoT
- Analytics Maturity and Organisational Readiness
Predictive Analytics for IoT Use Cases
- Role of Predictive Analytics in Intelligent IoT
- Predictive Maintenance: Core Concept
- Failure Pattern Analysis
- Condition-Based Monitoring
- Risk and Early-Warning Indicators
- Predictive Analytics Inputs in IoT Systems
- Handling Uncertainty in Predictions
- Balancing Sensitivity and Reliability
- Integration of Predictive Insights into Operations
Integrating Analytics into IoT Systems
- Purpose of Analytics Integration in IoT Systems
- Analytics-Driven Decision Loops
- Closing the Loop: From Insight to Action
- Real-Time vs. Batch Decision-Making
- Edge Analytics vs. Cloud Analytics
- Analytics Placement and Architectural Decisions
- Alerting Mechanisms in IoT Systems
- Dashboards and Reporting for IoT Analytics
- Integrating Analytics with Operational Workflows
- Human-in-the-Loop Decision Integration
- Measuring Effectiveness of Analytics Integration
- Common Pitfalls in Analytics Integration
Performance Monitoring & KPI Design
- Role of Performance Monitoring in Intelligent IoT Systems
- Understanding KPIs in IoT Contexts
- Defining Meaningful IoT KPIs
- Operational vs. Business Metrics
- Monitoring System Health and Reliability
- Monitoring Analytics Performance
- KPI Thresholds and Alerting
- Using KPIs for Continuous Improvement
- Avoiding Common KPI Design Pitfalls
- Aligning KPIs with Analytics Maturity
- Stakeholder-Specific KPI Views
- Governance and Accountability in KPI Usage
- Key Takeaways for Learners
Industry Use Cases of Intelligent IoT Analytics
- Role of Industry Use Cases in IoT Analytics
- Smart Manufacturing and Industry 4.0
- Analytics in Smart Manufacturing Environments
- Smart Cities and Infrastructure Monitoring
- Decision-Making in Smart City Analytics
- Energy and Utilities Analytics
- Risk Management in Energy IoT Systems
- Healthcare and Wearable IoT Analytics
- Decision Support in Healthcare IoT
- Cross-Industry Common Patterns
- Tailoring Analytics to Industry Context
- Measuring Value Across Use Cases
Capstone Lab: End-to-End IoT Data Analytics
- Problem definition and use-case selection
- Data preprocessing and EDA
- Pattern and trend identification
- Insight generation and recommendations
- Presentation and interpretation of results
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