Global Learning for Operational Leaders
Classroom
26 sessions
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Online / Live
26 sessions
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Introduction
Course overview
Why Attend
Organizations today rely heavily on data to guide decisions, improve performance, and predict future outcomes. Understanding how to structure data and build reliable statistical models is a critical skill across all industries.
This course provides a strong foundation in data handling and regression modeling, enabling participants to interpret relationships between variables, build predictive models, and make evidence-based decisions using structured analytical methods.
Course Methodology
This course is designed with a practical, hands-on approach:
- Step-by-step explanation of key statistical concepts
- Practical exercises using structured datasets
- Guided model building and interpretation sessions
- Interactive discussions to reinforce learning
- Focus on real-world analytical thinking and application
Course Objectives
By the end of this course, participants will be able to:
- Understand the fundamentals of data types and structures
- Apply basic statistical techniques for data analysis
- Build and interpret simple and multiple regression models
- Identify relationships between variables in datasets
- Evaluate model performance and accuracy
- Use regression outputs to support decision-making
Target Audience
This course is suitable for:
- Data Analysts and Junior Data Scientists
- Business Analysts
- Reporting and MIS Professionals
- Engineers and Technical Staff working with data
- Finance and Operations Professionals
- Anyone responsible for data interpretation and reporting
Target Competencies
Participants will develop competencies in:
- Data interpretation and statistical reasoning
- Regression modeling (simple and multiple)
- Analytical problem-solving
- Data-driven decision-making
- Model evaluation and validation
- Structured thinking using quantitative methods
What you will achieve
Learning objectives
- Understand the fundamentals of data types and structures
- Apply basic statistical techniques for data analysis
- Build and interpret simple and multiple regression models
- Identify relationships between variables in datasets
- Evaluate model performance and accuracy
- Use regression outputs to support decision-making
Who should attend
Target audience
- This course is suitable for:
- Data Analysts and Junior Data Scientists
- Business Analysts
- Reporting and MIS Professionals
- Engineers and Technical Staff working with data
- Finance and Operations Professionals
- Anyone responsible for data interpretation and reporting
- Target Competencies
Methodology
Learning approach
- Step-by-step explanation of key statistical concepts
- Practical exercises using structured datasets
- Guided model building and interpretation sessions
- Interactive discussions to reinforce learning
- Focus on real-world analytical thinking and application
Course content
Five focused days of learning and application
Day 1
Introduction to Data and Statistical Foundations
- Understanding data types (categorical, numerical, structured)
- Data collection and preparation basics
- Descriptive statistics (mean, median, variance, etc.)
- Data visualization fundamentals
- Correlation and relationship between variables
- Introduction to analytical thinking
Day 2
Introduction to Regression Analysis
- Concept of regression modeling
- Simple linear regression
- Relationship between dependent and independent variables
- Interpreting slope and intercept
- Error terms and model fit
- Practical exercises using sample data
Day 3
Multiple Regression Analysis
- Expanding to multiple variables
- Building multiple regression models
- Understanding coefficients and variable impact
- Multicollinearity concept (intro level)
- Model interpretation techniques
- Hands-on regression modeling practice
Day 4
Model Evaluation and Performance
- Measuring model accuracy (R² and error metrics)
- Residual analysis and interpretation
- Detecting model weaknesses
- Overfitting and underfitting concepts
- Improving model reliability
- Basic validation techniques
Day 5
Practical Applications of Regression Modeling
- Applying regression to real-world datasets
- Forecasting and prediction basics
- Using regression outputs for decision-making
- Common pitfalls in data analysis
- Best practices in reporting results
- Final practical exercise and review
FAQ
Frequently asked questions
What does Foundations of Data and Models Regression Analytics (DMBI-012) cover?
This course covers Data Management and Business Intelligence through a structured five-day outline focused on practical application, discussion, and implementation planning.
When is the next available session?
The next scheduled session starts on 5 - 9 October 2026, with additional classroom dates and mirrored Online / Live options listed in the course schedules section.
Who should attend this course?
This course is suitable for:, Data Analysts and Junior Data Scientists, Business Analysts
Is bilingual course delivery available?
Yes. All course materials and presentations are provided in English, while bilingual explanation and discussion support may be available depending on the selected location and trainer availability. For example, when attending a course in Istanbul, you may request a Turkish-speaking trainer to explain the English course content in Turkish. Arabic explanation support may also be requested at this location. Other languages are available in selected cities. You can choose your preferred language support as an optional preference during registration.
How can I register for a session?
Use any Register button next to the available course dates to open the participant registration page and submit your booking request for the selected session.
Is this course available online as well as classroom-based?
Yes. The course detail page includes both classroom sessions and Online / Live sessions, with online options aligned to the same course dates for easier planning.
Where are classroom sessions delivered?
Current classroom venues include Barcelona, Paris, Munich, Kuala lumpur, London, Amsterdam, Rome.
How long is the training programme?
The programme runs for five days, with up to five training hours per day, from 9:00 AM to 2:00 PM. Each programme includes a total of 25 accredited training hours.
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