About the AI & Data Science Summer School
Sep. 4-10, 2025 Dubai
The AI & Data Science Summer School Dubai 2025 blends real-world challenges, expert insights, and a vibrant international setting.
Over the course of one intensive week, participants explore cutting-edge tools and techniques in AI, Machine Learning, and Business Analytics—led by globally renowned faculty—while tapping into the energy and innovation of Dubai’s thriving tech ecosystem.
📅 September 4–10, 2025
🌍 No matter where you are in the world, our program is also available online.
⏺ Why Join This Program
Machine Learning
Build real AI models with Python to solve business problems.
HR Analytics
Turn HR data into smart decisions using Excel and Tableau.
Business Graphs
Reveal hidden connections with Graph Neural Networks.
No-Code AI
Create AI models without coding using the KNIME platform..
⏺ Meet Your Instructors
Prof. Meenakshi Kaushik
Professor at Department of Management and Applied Sciences in Lloyd Group of Institutions with 17+ years of extensive experience in higher education, specializing in Human Resource Management and Development. She has a strong background in industry collaboration, having worked with organizations like CIPLA and conducted training programs through ISTD. Certified member of AIMA, NHRD, and SHRM with exceptional skills in academic leadership and research mentoring.
Prof. Alireza Daneshkhah
Professor in Data Science at the Faculty of Mathematics and Data Science, Emirates Aviation University, with a PhD from the University of Warwick in causal graphical models. He has held senior academic positions at Coventry and Cranfield Universities, and leads research in AI, Bayesian modelling, and probabilistic simulation. His projects span critical domains such as climate, health, and infrastructure, funded by EPSRC, NHS, and other major UK bodies.
Prof. Dursun Delen
Regents Professor of Management Science and Information Systems and Director of the Center for Health Systems Innovation at Oklahoma State University. With over a decade of industry experience and numerous research projects funded by NASA, NSF, and the U.S. Department of Defense, he brings deep expertise in business analytics, data science, and decision support systems. Dr. Delen is a globally recognized keynote speaker and author of over 250 peer-reviewed publications and 11 books in analytics and AI.
Dr. Hossein Peyvandi
Dr. Hossein Peyvandi holds a Ph.D. in Electrical and Computer Engineering from the University of Surrey (UK) and specializes in computational intelligence, predictive analytics, and information theory. He has published 30+ peer-reviewed papers, holds a national patent in cryptography, and is the author of three books, including Computational Optimization. A recipient of the NEF Award, Dr. Peyvandi has contributed to several EU-funded R&D projects and is an active member of IEEE, AMS, and IET.
⏺ Who Should Join
- Business professionals who want to apply AI in HR, marketing, or operations.
- Data analysts and managers seeking practical AI & ML skills.
- Researchers and students with basic knowledge of Python.
- Organizations looking to empower their teams with data-driven insights
⏺ What You Will Learn – Daily Breakdown
This two-day hands-on workshop introduces essential machine learning (ML) techniques using Python, with a focus on real-world applications in business analytics, operations, and decision-making. Participants will explore the full ML pipeline, from preprocessing to model evaluation and basic deployment.
Supervised techniques (e.g., regression, decision trees, support vector machines) and unsupervised approaches (e.g., clustering, dimensionality reduction) will be practiced through case studies using scikit-learn, pandas, and visualization libraries.
No prior background in machine learning is required, although familiarity with basic Python programming is recommended. The workshop is tailored for industry professionals and graduate students aiming to enhance their analytical and data-driven decision-making capabilities.
By the end of this workshop, participants will be able to:
- Understand what machine learning is and how it is used in industry
- Build and evaluate basic ML models for classification, regression, and clustering
- Clean and prepare datasets for analysis
- Gain confidence using Python-based tools for solving real-world problems
- Leave with working templates they can adapt to their own data
- Understanding the Fundamentals
- Why People Analytics?
- Adoption of Analytics
- HR’s Contribution to Business Value
- HR Decision Making and Analytics
- HR Business Process and Analytics
- Establishing an Analytics Culture: Enable Analytical Thinking
- Role of Leader in creating analytic culture
- Understanding Data and Basic Analytic Tools: Know Your Data, A Pragmatic View of Data
- Solving Data Quality Challenges
- Data Types and Sources, Data Governance
- Creating HR Dashboards using Microsoft Excel
- Applying Pivot Tables to HR data
- Application of Tableau in HR Data Visualization
- Analytics in Various Functions and Processes: Staffing Analytics
- Analytics in Manpower Planning
- Training and Development Analytics
- Analytics in Performance Management
- Engagement Analytics, Analytics in Absenteeism
- Turnover, Case Studies on various analytics
This intensive one-day workshop introduces cutting-edge applications of Artificial
Intelligence in marketing. Participants will explore how Graph Neural Networks (GNNs),
Natural Language Processing (NLP), recommender systems, and Transformer models can be
used to predict customer behavior, personalize marketing, and enhance analytics
This one-day workshop demonstrates how to apply Graph Neural Networks (GNNs) in HR analytics. Participants will learn to build graph models of communication, collaboration, and skills across organizations. We explore how these networks help predict attrition, improve team dynamics, and support talent development.
In this course, participants will learn the fundamentals of data science, the CRISP process, and key AI/ML concepts. They will explore no-code tools like KNIME for data analysis and build machine learning models using real datasets. The course also covers best practices such as handling imbalanced data, cross-validation, ensemble methods, and model explainability.