Data Science & Analytics for Strategic Decisions Programme

• Analytical approach to resolve a business problem

• Is your organisation is data-driven

• Trends in data and obtaining related insights to enhance business performance

• Impact an organisation’s omnichannel strategies have on sales

• How to identify appropriate data/insights

• Comparison of independent data sets to obtain insights

• How to apply strategic decision-making using said techniques

• Regression to analyse the strength/impact of variables

• Predict variable impact using optimal model fit and regression effects

• Logistic regression model to test and predict expected outcomes

• Apply predictive analytics to organisational events to advance strengths and counter threats

• Correlation and causality and their significance to enhancing business performance

• Experimentation for business problems to make effective inferences

• Multivariate, A/B and Multi-Armed Bandit testing

• Effectiveness of using experimental design to make data-informed recommendations for business growth

• Recommendation Systems

  • Recommendations and Ranking

  • Collaborative Filtering

  • Personalized Recommendations

• ML and its role in driving organisational productivity

• Apply ML algorithms to achieve optimal analytical accuracy

• Programme-building facets of neural networks and deep learning

• Combine analytics with experiments to produce effective business strategies

• Decision Making Under Uncertainty

  • Bayesian Decision Making

  • Simulations to make decisions under uncertainty

• Optimal Decision Making

  • Linear Optimisation

  • Sensitivity Analysis and Shadow Price

• Driving digital transformation within the organisation

• Change management: The role of data analytics, machine learning and its applications

• Aligning organisations and teams for data-driven approaches

• Making the business case for Data Science

• Data Storytelling with Visualisation using Tableau

• Disruptive innovation

• Distilling value from analytics

• Developing a strategy roadmap, privacy implications, traps and myths

• Customer-centric analytics in retail and media

• Business process analytics

• Domain exposure

• Key challenges to data science projects and their solutions

• Delta Framework and Delta Plus Model

• Project-level risks and examples of failed data science projects

• Predict the success of big data project using DATA technique

• Drivers, expected outcomes, and technology enablers for Industry 4.0

• Components for AI success

• Challenges in the implementation of AI in systems

• Evaluate an organisation’s digital transformation journey and sustain a competitive advantage

• Overview of ChatGPT and OpenAI

• Timeline of NLP and Generative AI

• Frameworks for understanding ChatGPT and Generative AI

• Implications for work, business, and education

• Business roles to leverage ChatGPT

• Futureproofing organisations to incorporate Generative AI into workflow

• Prompt engineering for fine-tuning outputs

• Safeguards and risk mitigation measures

This data analytics course is specifically designed for both technical and non-technical professionals—no coding required. It emphasizes tool-based learning (Tableau, Excel, XLSTAT), strategic decision-making, and experimentation, making it accessible and career-relevant for diverse learners.

While it stands out among data analytics courses in Singapore, this SMU programme is designed for professionals across the globe. It offers a strategic, application-oriented curriculum with live faculty and industry expert sessions, hands-on learning with tools like Tableau and XLSTAT, a capstone project, and AI-focused masterclasses—making it ideal for global professionals seeking data-driven decision-making expertise.

Absolutely. As a practical data science course, it equips learners with skills in AI-powered storytelling, predictive modeling, risk evaluation, and future trends in data science—making it ideal for business managers aiming to lead data-driven strategies.

Yes. Participants who successfully complete the course and the capstone project will receive a verified data analytics certificate from Singapore Management University, recognized across industries and roles.

Yes, this is a fully flexible online data science course with 4–6 hours of commitment per week. Learners benefit from live weekend sessions, case studies, hands-on assignments, and interactive tools—all structured for professionals balancing full-time work.

While a typical business analytics course may focus narrowly on reporting and dashboards, this programme goes further by covering data strategy, experimentation, machine learning, and AI integration—all tied to strategic business decisions.

Yes. Whether you're in marketing, operations, finance, or HR, the programme helps you learn data analytics in a business context. It empowers you to make informed decisions and lead data-focused transformation within your organization.

Indeed. This course blends data science and analytics concepts through modules like predictive analytics, causality, data innovation, and ChatGPT applications, enabling learners to transition into analytics-driven roles with confidence.

One of the final modules focuses specifically on addressing key challenges and risks in data science projects—including ethics, data bias, implementation hurdles, and governance—preparing participants to manage projects responsibly and sustainably.