(IBM) Data Science & Predictive Analytics for Business Intelligence is a instructor-led training programme designed to equip learners with job-relevant analytical competencies required in data-driven business environments. The course focuses on the application of statistical techniques, predictive analytics, segmentation modelling, and analytical performance evaluation to support informed, defensible business decisions.
Rather than emphasising tools or coding, the programme prioritises analytical thinking, interpretation, and business application. Learners are guided to understand how statistical models, predictive methods, and big data analytics contribute to business intelligence, risk mitigation, and strategic planning.
Through structured concepts, real-world business scenarios, and case-based discussions, the course prepares learners to engage confidently with analytics outputs, evaluate model implications, and communicate insights effectively to stakeholders across the organisation.
Learning Objectives
By the end of this program, participants will be able to:
- Develop a structured understanding of data science and predictive analytics as business intelligence capabilities
• Apply statistical thinking to analyse business data and support decision-making
• Distinguish between descriptive, diagnostic, and predictive analytics in business contexts
• Interpret predictive and segmentation models with awareness of assumptions, limitations, and risks
• Evaluate data quality, test conditions, and bias affecting analytical outcomes
• Translate analytical results into actionable business insights and risk-aware recommendations
• Communicate analytics findings clearly to business and management stakeholders
Learning Outcomes
Upon successful completion of the course, participants will demonstrate:
- Enhanced analytical literacy suitable for business intelligence and analytics roles
• Improved ability to interpret statistical and predictive outputs in organisational contexts
• Greater confidence in evaluating analytics-driven recommendations and risks
• Readiness to contribute meaningfully to data-driven decision-making initiatives
• Capability to bridge analytics outputs with business strategy, performance, and risk considerations
Course Code: TGS-2025060862
Course Duration: 3 days
Course Fee: $1800.00
| 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 | $1800.00 | $1800.00 | $1800.00 |
| SkillsFuture Funding | $900.00 | $1260.00 | $1260.00 |
| Total Nett Fee | $900.00 | $540.00 | $540.00 |
| GST (9% of Course fee) | $162.00 | $162.00 | $162.00 |
| Total Fee Payable to | $1062.00 | $702.00 | $702.00 |
Fundamentals of Statistical Techniques in Business Analytics
- Introduction to Statistical Techniques
- Why Statistics Matters in Business
- Role of Statistics in Business Decision-Making
- Types of Business Data
- Overview of Common Statistical Techniques in Business Analytics
- Descriptive, Diagnostic, and Predictive Perspectives
- Common Misconceptions and Misuse of Statistics
- Business Focus: Why Before How
Descriptive vs. Inferential Statistics
- Descriptive Statistics for Summarising Business Data
- Measures of Central Tendency
- Measures of Variability
- Visual Summarisation of Data
- Inferential Statistics for Drawing Conclusions
- Estimation and Confidence Intervals (Conceptual Level)
- Business Use Cases for Descriptive vs Inferential Analysis
- Descriptive Statistics Are Appropriate When
- Risks of Over-Generalisation and Incorrect Inference
- Business Focus: Choosing the Right Statistical Approach
Test Conditions and Data Quality Assessment
- Test Conditions and Applicability of Statistical Methods
- Data Quality Dimensions
- Impact of Poor Data Quality on Business Decisions
- Business Focus: Preventing Flawed Decisions
Interpreting Data Trends, Patterns, and Risks
- Recognising Patterns and Seasonality
- Differentiating Normal Variation from Risk Signals
- Interpreting Correlation vs Causation
- Translating Statistical Findings into Business Language
- Assessing Uncertainty and Confidence in Findings
- .Business Risk Mitigation Case Studies
- Service Quality and Performance Risk Identification
- Early Warning Indicators from Historical Data
- Recommending Risk Mitigation Actions Based on Analysis
- Communicating Findings to Management Stakeholders
- Business Focus: Applying Statistics to Risk-Aware Decision-Making
Statistical Modelling for Business Strategy
- Role of Statistical Models in Business Strategy
- Strategic Decision-Making Under Uncertainty
- Model Assumptions and Strategic Risk
- Statistical Models vs Business Reality
- Strategy Before Technique
- Regression, Time Series, and Classification Techniques
Introduction to Predictive Analytics
- What Is Predictive Analytics
- Predictive Analytics vs Traditional Reporting
- Core Components of Predictive Analytics
- Historical Data
- Statistical and Analytical Models
- Business Assumptions and Context
- Types of Predictive Questions in Business
- Uncertainty, Probability, and Risk
- Predictive Analytics in Strategic and Operational Decisions
- Business Focus: Prediction as Decision Support
- becomes a source of misplaced confidence rather than strategic advantage.
Machine Learning Applications in Forecasting
- Why Machine Learning Is Used in Forecasting
- Common Machine Learning Models Used in Forecasting
- Feature Engineering in ML-Based Forecasting
- When Machine Learning Forecasting Is Not Appropriate
- Business Impact of ML-Based Forecasting
- Business Focus: Forecasting With Accountability
Developing Advanced Data Analysis Methods
- Evolving from Basic to Advanced Analytical Methods
- Key Categories of Advanced Data Analysis Methods
- Time-Aware and Dynamic Analysis
- Probabilistic and Risk-Based Analysis
- Designing Advanced Analytical Methods Responsibly
- Advanced Analytics in Organizational Practice
Handling Large and Complex Datasets
- What Makes Data “Large” and “Complex”
- Strategies for Managing Large and Complex Datasets
- Governance and Responsibility at Scale
- Common Pitfalls in Large-Scale Data Analysis
- Business Focus: Complexity with Control
- Segmentation Modelling and Big Data Analytics
- Understanding Data Limitations and Bias
Managing Missing and Inconsistent Data
- Business Implications of Missing Data
- Inconsistent Data Across Enterprise Systems
- Principles for Managing Inconsistent Data
- Business Focus: Imperfect Data, Disciplined Decisions
- Segmentation and Clustering Techniques
- Data-Driven Clustering Approaches
- Business Focus: Segmentation as a Decision Tool
- Needs-Based Segmentation
- Risk-Based Segmentation
- Cross-Functional Use of Customer Segments
Big Data Tools for Trend and Risk Analysis
- What Makes Big Data Analytics Fundamentally Different
- Risk Analysis in Large-Scale Environments
- Common Big Data Analytical Use Cases
- Big Data Tools as Enablers, Not Decision-Makers
- Interpreting Trends and Risks Responsibly
- Integration with Segmentation and Predictive Analytics
- Business Focus: Seeing the Big Picture Without Losing Meaning
Facilitating Data-Driven Business Discussions
- From Analysis to Conversation
- Structuring Business-Oriented Analytics Conversations
- Managing Differing Stakeholder Perspectives
- Avoiding Common Pitfalls in Analytics Conversations
- Role of Visualization and Storytelling
- Linking Analytics to Risk-Aware Decision-Making
- Business Focus: Analytics as a Shared Language
- Evaluating Analytical Performance in Business Decision-Making
Model Evaluation and Performance Metrics
- Why Model Evaluation Matters in Business
- Purpose of Model Evaluation
- Confusion Matrix as a Foundation
- Accuracy
- Beyond Metrics: Business-Critical Evaluation Dimensions
- Business Focus: Fitness for Decision, Not Mathematical Perfection
Assessing Business Relevance of Analytical Models
- Defining Business Relevance in Analytics
- Alignment with Business Objectives
- Decision-Centric Model Design
- Operational Fit and Practical Constraints
- Interpretability and Explainability
- Cost–Benefit and Value Assessment
- Evaluating Relevance Across the Model Lifecycle
Optimising Models for Strategic Insights
- From Predictive Performance to Strategic Value
- Understanding Strategic Insight in Analytics
- Optimisation Beyond Accuracy Metrics
- Scenario Analysis and What-If Modelling
- Balancing Complexity and Strategic Clarity
- Optimisation for Decision Impact
- Integration with Strategic Planning Processes
- Governance and Strategic Accountability
Integrating Predictive Analytics with Business Strategy
- Role of Predictive Analytics in Strategic Planning
- Embedding Predictive Insights into Strategic Cycles
- Linking Predictions to Strategic Levers
- Scenario-Driven Strategy Development
- Aligning Predictive Analytics with Risk Appetite
- Predictive Analytics in Strategic Execution
- Organisational Alignment and Collaboration
- Governance and Strategic Accountability
- Business Focus: Analytics as a Strategic Capability
Business Transformation Case Studies
- Understanding Business Transformation Through Analytics
- Case Studies
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