An Interactive 5-Day Training Course

Data Science for Operational Excellence

Leveraging Data-Driven Insights to Optimize Business Performance

06 - 10 Apr 2026
Dubai
| $5950
13 - 17 Jul 2026
London
| $5950
05 - 09 Oct 2026
London
| $5950
21 - 25 Dec 2026
Dubai
| $5950

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:

Key Learning Outcomes

At the end of this Data Science for Operational Excellence training course, participants will be able to:

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.

Data Science for Operational Excellence

Who Should Attend?

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

Learning Journey Breakdown

  • 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
  • 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
  • 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
  • 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
  • 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

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