Course Overview
AI-Driven Analytics is an introductory, hands-on course designed to help researchers understand the wide range of analytical and AI methods shaping modern research and decision-making. The course provides a guided journey from basic data exploration and visualisation through predictive and prescriptive analytics, before introducing current AI trends such as generative models, agent-based AI, and simulation-driven approaches. Rather than focusing on mathematical detail, the course emphasises intuition, use-cases, and practical experimentation, helping participants understand which tools exist, what they are good for, and how they can be applied in their own research projects.
Taster Session
On 23 April 12-1pm we will hold an online taster session.
Join Dr Ali Hassanzadeh of the Centre for AI and Decision Sciences (AID) who will introduce some of the activities that are planned for the summer school. There will also be chance to talk to Ali and ask questions so that you can get a sense of whether this summer school is the right fit for you.
Course Lead
Dr. Ali Hassanzadeh is a Lecturer (Assistant Professor) in management science at Alliance Manchester Business School (AMBS), University of Manchester. Ali’s research has mostly centered around developing mathematical models to help high-level decision makers to improve operations in their organizations. Theory-wise, he primarily works with combinatorial optimization models, distributed optimization, algorithm design, and machine learning techniques. Ali is an academic member of the Centre for AI and Decision Sciences (AID) at AMBS.
Course Objectives
By the end of the course, participants will:
- Understand the landscape of AI-driven analytics, from descriptive and predictive methods to modern AI systems
- Gain hands-on exposure to common analytics and AI tools using simple examples
- Learn the differences between supervised, unsupervised, and reinforcement learning
- Understand emerging AI trends such as agent-based AI, simulation-driven AI, and physics-informed models
- Develop confidence to explore AI methods independently in their own research
Preparatory work or pre-requisites
No prior knowledge of AI or machine learning is required. Basic familiarity with data and research workflows is sufficient. Optional online resources and documentation will be provided upon registration.
Who should attend?
This course is suitable for:
- PhD students and Early Career Researchers
- Researchers from non-technical backgrounds
- Researchers who have heard about AI but have not yet used it
- Participants who want to explore AI methods before committing to more advanced technical courses
Course Timetable
This course will take place in-person Monday 6 July - Wednesday 8 July
1:30 - 5:00pm Monday
Foundations
- What is AI-Driven Analytics?
- From data and visualisation to descriptive and predictive analytics
- Overview of common analytics workflows
9:00 - 12:30pm Tuesday
Core Methods
- Supervised and unsupervised learning: what they do and when to use them
- Hands-on exploration with simple datasets
- How these methods appear in real research projects
1:30 - 5:00pm Tuesday
AI Trends
- Reinforcement learning and decision-focused analytics (conceptual overview)
- Simulation-based and physics-informed AI
- Understanding model limitations and failure modes
9:00 - 12:30pm Wednesday
AI Research Frontiers
- Emerging AI trends: generative AI, agentic systems, autonomous decision-making
- How researchers can experiment safely with AI tools
- Choosing the next step: tools, methods, and courses
What's included in the course?
Each full day includes a vegan buffet lunch served 12.30-1.30pm. There are morning and afternoon refreshment breaks with tea, coffee, water, and pastries/cakes.
The course includes a social programme - these are optional but free social events for everyone attending our summer school to meet attendees from other courses in a relaxed environment.
Accommodation and travel are not included in the course price. You will need to arrange any accommodation and travel separately.
Cost
- Full price: £450
- PGR/Reduced Rate: £300
As well as PGRs, reduced fees are available to those working within the voluntary, charity and community sector. We also have two bursary options available for those entitled to reduced fees. Please view more information on our main Summer School website to find out more and how to apply.
Book Your Place
Please purchase via our online store below before 15 June (payment by card only). If you any questions, or will have trouble purchasing by this date please get in touch with methods@manchester.ac.uk.
Any questions, please do not hesitate to contact us on methods@manchester.ac.uk
Credits:
Created with images by Anjali - "Group of people and communication network concept.,Social media.,Human resources." • GamePixel - "Big data and AI technology, Business intelligence and AI data science, Automating data processing, Machine learning and business intelligence, AI driven data processing, KPI performance monitoring."