Course Overview:
This masterclass delivers a strategic, intuitive journey through the world of predictive analytics—beginning with descriptive and exploratory techniques and progressing to advanced machine learning (ML) methods such as classification, categorization, regression (linear and non-linear), time series forecasting, seasonal-trend decomposition (STL) etc. Designed for both analytics enthusiasts and business leaders, the session avoids technical coding and instead focuses on demystifying ML-powered forecasting through real-world case studies. With a strong emphasis on practical business applications, model interpretability, and the evolving landscape of data- driven strategy, participants will gain a clear understanding of how leading organizations transform data into actionable foresight—and how these predictive approaches can be effectively adapted within their own business contexts.
Course Objectives:
- To equip participants with a solid understanding of key predictive analytics concepts
- To showcase real-world business applications of predictive models across sectors such as finance, retail, healthcare, manufacturing etc.,
- To develop critical awareness of the tools, trends, and limitations in predictive analytics
Upon the completion of this course, the participant will be able to:
- Identify and differentiate between key predictive analytics techniques such as classification, regression, categorization, and time series forecasting, and understand when to apply each.
- Interpret the output of predictive models and assess their business value, accuracy, and limitations in real- world decision-making contexts.
- Recognize industry-specific use cases where predictive analytics has delivered measurable impact and draw parallels for potential adoption within their own business functions.
- Be empowered to make informed, strategic decisions about selecting tools, platforms, and data strategies for implementing predictive analytics in a scalable and explainable manner.
Course Outline:
- Introduction to Predictive Analytics: Role in the analytics value chain (descriptive → diagnostic → predictive → prescriptive)
- Overview of Key Techniques: Classification, regression (linear & advanced), categorization, time series forecasting, and STL decomposition
- Exploratory and Descriptive Analytics as a Foundation for Prediction
- Machine Learning Models in Business: Strengths, limitations, and model interpretability
- Real-World Case Studies: Cross-industry applications from retail, finance, healthcare, manufacturing, and logistics
- Tool Landscape: Overview of current no-code/low-code platforms for predictive modeling
- Ethical Considerations and Strategic Integration of Predictive Analytics in Decision-Making
- Future Trends: AutoML, predictive + prescriptive analytics, and integration with GenAI
Delivery Method:
In-Person Classes
Target Audience Level:
- Business professionals, managers, and decision-makers seeking to harness predictive analytics for strategic forecasting and improved data-driven decisions
- Early-career data analysts and functional experts looking to deepen their understanding of predictive modelling in business contexts
Trainer's Profile/Brief:
Dr. Anu Jossan is Programme Leader of Business Analytics at QFBA Northumbria university with over 18 years of experience across banking, academia, research, and executive education. She is a Certified AI Expert, Certified Data Science Professional, PMP® (Project Management Professional), and CISA® (Certified Information Systems Auditor). Her expertise spans banking risk, financial econometrics, econometric modeling, and machine learning applications for forecasting and strategic analysis. Dr. Jossan has led impactful research in applied econometrics and supervised over 300+ graduate/postgraduate projects involving Economics, Business, Analytics, and predictive modeling. She teaches graduate/postgraduate courses on econometrics, financial modeling, machine learning for forecasting, and financial/banking risk management. She regularly delivers workshops on data-driven decision-making across the GCC and Europe. Her leadership includes chairing international accreditation panels with national and international agencies and designing forward-looking curricula aligned with industry needs. Dr. Jossan has delivered international presentations and masterclasses on “Data-Driven Decision Making,” “Strategic Forecasting,” and “Quantitative Research in Finance” for institutions such as Qatar Stock Exchange, NASPA, and IAFOR and is also working as visiting professor for University American College Skopje and Woxsen University.
