| Date | Venue | Fees | |
|---|---|---|---|
| 13 - 17 Jul 2026 | London - UK | $ 5,950 | |
| 20 - 24 Jul 2026 | Paris - France | $ 5,950 | |
| 05 - 09 Oct 2026 | London - UK | $ 5,950 | |
| 21 - 25 Dec 2026 | Dubai - UAE | $ 5,950 | |
| 12 - 16 Jul 2027 | London - UK | $ 5,950 | |
| 04 - 08 Oct 2027 | London - UK | $ 5,950 | |
| 20 - 24 Dec 2027 | Dubai - UAE | $ 5,950 |
Introduction
This training course equips professionals with cutting-edge data science techniques to drive operational excellence and business efficiency. In today’s fast-paced and data-driven world, organizations that harness data effectively outperform competitors, reduce costs, and enhance customer satisfaction. This training course empowers participants to turn operational data into actionable insights, optimizing processes and decision-making across all levels of the organization.
Participants will explore practical data science applications, including predictive analytics, process optimization, and performance monitoring. By integrating advanced data analysis methods with operational strategies, learners will gain a comprehensive understanding of how to improve efficiency, reduce waste, and enhance productivity. This training course is a must-attend for professionals seeking to leverage data to create measurable impact in operations.
This GLOMACS Data Science for Operational Excellence training course will highlight:
- Advanced techniques in operational data analysis
- Predictive modeling to optimize process efficiency
- Data-driven decision-making strategies for managers
- Real-world case studies of operational excellence
- Tools and frameworks to implement sustainable improvements
Objectives
At the end of this Data Science for Operational Excellence training course, participants will be able to:
- Understand the principles of operational data science
- Analyse operational performance using data techniques
- Develop predictive models for process optimization
- Apply data insights to enhance business efficiency
- Design actionable dashboards for decision support
Training Methodology
This training course employs a practical and interactive approach combining instructor-led sessions, group discussions, case studies, and hands-on exercises. Participants will actively work with real datasets, simulating operational scenarios to enhance learning and ensure immediate applicability. The methodology emphasizes engagement, collaboration, and experiential learning to reinforce concepts.
Organisational Impact
By sending employees to this training course, organizations gain measurable improvements in operational efficiency and data-driven decision-making:
- Improved process efficiency and reduced operational costs
- Enhanced quality control and performance monitoring
- Data-driven culture fostering informed decisions
- Optimized resource allocation and productivity
- Increased organizational agility and competitiveness
- Stronger alignment between operational strategy and execution
Personal Impact
Participants will enhance their professional capabilities and achieve tangible career benefits:
- Master data science tools for operational analysis
- Gain insights into predictive analytics and forecasting
- Improve problem-solving and critical thinking skills
- Strengthen decision-making through data-driven insights
- Enhance leadership in operational management
- Expand career opportunities in data-focused roles
Who should Attend?
This GLOMACS Data Science for Operational Excellence training course is suitable for professionals who wish to leverage data science to drive operational performance, efficiency, and innovation. Delegates will benefit from practical, actionable insights that can be applied immediately to their roles.
- Operations managers and team leaders seeking efficiency improvements
- Data analysts and business intelligence professionals
- Process improvement and quality assurance specialists
- Supply chain and logistics managers
- Mid to senior-level managers responsible for operational performance
Foundations of Data Science in Operations
- Overview of operational excellence principles
- Introduction to data science and its role in operations
- Key data sources and types for operational analysis
- Understanding process metrics and KPIs
- Data collection, cleaning, and preparation methods
- Basics of exploratory data analysis (EDA)
- Data visualization for operational insights
- Tools and software for operational data analysis
Descriptive Analytics and Performance Monitoring
- Techniques for summarizing operational data
- Identifying trends, patterns, and anomalies
- Key performance indicators (KPIs) and dashboards
- Root cause analysis using data
- Operational reporting and visualization frameworks
- Case studies on performance monitoring
- Integrating descriptive analytics into daily operations
- Data quality and governance considerations
Predictive Analytics for Operational Optimization
- Introduction to predictive modeling concepts
- Regression analysis for process optimization
- Forecasting demand, capacity, and resource allocation
- Predictive maintenance and risk reduction
- Scenario analysis and decision modeling
- Tools for predictive analytics implementation
- Evaluating model performance and accuracy
- Translating predictive insights into operational actions
Prescriptive Analytics and Process Improvement
- Understanding prescriptive analytics in operations
- Optimization techniques for resource allocation
- Simulation and what-if analysis
- Decision support systems for operational excellence
- Process automation using data-driven insights
- Leveraging AI and machine learning for operational improvement
- Case studies of successful prescriptive analytics implementation
- Strategies for embedding continuous improvement culture
Implementing Data Science for Sustainable Excellence
- Designing data-driven operational strategies
- Creating actionable dashboards for management
- Change management and adoption of analytics-driven processes
- Integrating cross-functional data for holistic decision-making
- Evaluating operational performance and ROI of data initiatives
- Risk management and compliance considerations
- Building a roadmap for continuous operational improvement
- Capstone project: Applying data science to a real operational scenario
- Upon successful completion of this training course, GLOMACS Certificate will be awarded to the delegates. Continuing Professional Education credits (CPE): In accordance with the standards of the National Registry of CPE Sponsors, one CPE credit is granted per 50 minutes of attendance
