Module 1: Python for finance: 8 hours

THE LINGUA FRANCA OF DATA SCIENCE, MACHINE LEARNING, AND FINANCE

QDC Lecture 1 (Module 1): Introduction to Python by Saeed Amen

• Installing Anaconda and PyCharm – Instructions

QDC Lecture 2 (Module 1): Data Analysis in Python by Saeed Amen

• Data Analysis in Python – Forum

QDC Lecture 3 (Module 1) Analysis of financial data using Python by Saeed Amen

• Analysis of Financial Data Using Python – Forum

QDC Lecture 4 (Module 1): Financial market case studies using Python by Saeed Amen

• Financial Market Case Studies Using Python – Forum

Module 1 Test

Module 2: Databases in finance – Kdb+/q: 

AN UNRIVALED TOOL FOR BIG DATA AND HIGH-FREQUENCY DATA

QDC Lecture 5 (Module 2): Overview of kdb+/q by Paul Bilokon

• Installing kdb+/q – Slides
• What is kdb+/q? – Slides
• Variables, types, and operators – Slides (no solutions)
• Overview of kdb+/q – Self Study Forum

QDC Lecture 6 (Module 2): Foundation of the q programming language by Paul Bilokon

• Lists – Slides
• Dictionaries – Slides 
• Foundation of the q programming language – Forum

QDC Lecture 7 (Module 2): Working with tables by Paul Bilokon

• Control Flow – Slides
• Iterators  – Slides
• q-sql – Slides
• Working with tables – Forum

QDC Lecture 8 (Module 2): Kdb+/q for big data and machine learning by Paul Bilokon

• q-sql – Slides
• Big Data in kdb+/q – Slides
• Parallelisation – Slides
• Joins – Slides
• kdb+/q for big data and machine learning – Forum

QDC Lecture 9 (Module 2): Kdb+/q in practice by Paul Bilokon

• Big Data in kdb+/q – Slides
• kdb+tick – Slides
• kdb+/q in practice – Forum

Module 2 Test

Module 3: C++ fundamentals with use cases from finance

PERFORMANCE, PRODUCTION STABILITY, AND PORTABILITY

QDC Lecture 10 (Module 3): C++ introduction by Ivan Zhdankin

• QDC Tutorial 1 Curve Interpolation – Slides
• QDC lecture 1 Intro – Slides
• Sample Code – Lecture1
• Sample Code – Tutorial1
• coderpad(3)
• coderpad (1)
• C++ introduction – Forum

QDC Lecture 11 (Module 3): Introduction to OOP in C++ by Ivan Zhdankin

• Lecture Intro OOP – Slides
• Tutorial Algo IR risk hedging – Slides
• Sample Code – Lecture2
• Sample Code – Tutorial2
• Introduction to OOP in C++ – Forum

QDC Lecture 12 (Module 3): Defining your own structures in C++ by Ivan Zhdankin

• QDC Tutorial 3 – Slides
• QDC lecture 3 – Slides
• Sample Code – Lecture3
• Sample Code – Tutorial 3
• Defining your own structures in C++ – Forum

QDC Lecture 13 (Module 3): Introduction to Standard Library by Ivan Zhdankin

• QDC Tutorial 4 – Slides
• QDC lecture 4 – Slides
• Sample Code – Lecture4
• Sample Code – Tutorial 4
• Introduction to Standard Library – Forum

Module 3 Test

Module 4: Data structures and algorithms in C++

THE CORE OF COMPUTING

QDC Lecture 14 (Module 4): Git and GitHub by Ivan Zhdankin

• Git and GitHub – Webex Link
• Git and GitHub – Recording Link
• Data Structures and Algorithms – Slides
• What is Git? – Slides
• Git and GitHub – Forum

QDC Lecture 15 (Module 4): Analysis Tools, Recursion and Sorting by Ivan Zhdankin

• Analysis Tools, Recursion and Sorting – Recording Link
• Analysis tool, Recursion – Slides
• Interview Questions
• Pricing Vanilla instruments and Derivatives – Slides
• Analysis Tools, Recursion and Sorting – Forum

QDC Lecture 16 (Module 4): Arrays, Linked Lists, Stacks and Queues by Ivan Zhdankin

• Queues Sorting – Slides
• Code / Interview Questions
• Data Structures and Algorithms, Risk-Factor Simulation – Slides
• Arrays, Linked Lists, Stacks and Queues – Forum

QDC Lecture 17 (Module 4): Trees and Graphs by Ivan Zhdankin:

• Data Structures and Algorithms in C++ – Maps and Hash Tables, Trees, Graph Algorithms – Slides
• Data Structures and Algorithms – Risk-Factor Simulation, Aggregation – Slides
• Trees and Graphs – Forum

Module 4 Test

Module 5: Designing algorithmic trading applications

STATE-OF-THE-ART CONTENT

QDC Lecture 18 (Module 5): The hardware of electronic trading by Paul Bilokon

• The hardware of electronic trading – Slides
• The hardware of electronic trading – Forum

QDC Lecture 19 (Module 5): The networking of electronic trading by Paul Bilokon

• The networking of electronic trading – Slides
• The networking of electronic trading – Forum

QDC Lecture 20 (Module 5): Low-latency programming by Paul Bilokon

• Low-latency programming – Slides
• Low-latency programming – Forum

QDC Lecture 21 (Module 5): Event-driven architecture by Paul Bilokon

• Event-driven architecture – Slides
• Event-driven architecture – Forum

QDC Lecture 22 (Module 5):The workflow of a trading platform by Paul Bilokon

• The workflow of a trading platform – Slides
• The workflow of a trading platform – Forum

Module 5 Test

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Module 6: Artificial Intelligence (AI), Generative AI (GenAI), and Large Language Models (LLMs)

In the era of rapidly evolving AI technologies, Module 6 equips quantitative developers with a practical and strategic understanding of the foundational concepts and cutting-edge tools reshaping finance today. Across four intensive lecture weeks, you will build both theoretical insight and hands-on awareness of how machine learning and AI systems from neural networks to generative models are transforming trading, risk management, and production workflows.

QDC Lecture 23 (Module 6): Neural Networks, Linear Models, and Ensemble Methods by Paul Bilokon

Kick off with the statistical and algorithmic foundations of modern machine learning. Explore linear models and ensemble techniques before progressing into neural networks — the core building blocks for deeper AI systems.

QDC Lecture 24 (Module 6): Artificial Intelligence (AI): Attention, Transformers, and GenAI by Paul Bilokon

Delve into the key innovations that power next-generation AI, including the attention mechanism and transformer architectures that underlie most large language models (LLMs). Understand how these technologies function and why they have become central to generative intelligence.

QDC Lecture 25 (Module 6): GenAI and LLMs in Trading and Risk Management by Paul Bilokon

Examine practical applications of GenAI and LLMs within quantitative finance. From signal generation and data synthesis to sentiment analysis and automated reporting, discover where and how these models can augment trading strategies and risk workflows responsibly and effectively.

QDC Lecture 26 (Module 6): Productionizing LLMs, Achieving Industrial Scale by Paul Bilokon

Finish by learning how to operationalise AI/LLM systems in real-world environments. Topics include model deployment, versioning, monitoring, scalability, and integration with existing quant systems — ensuring your AI solutions are robust and production-ready.

Module 6 is a forward-looking addition to the QDC curriculum, designed for quant developers who want to harness AI’s potential with both technical confidence and business relevance.

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Module 6 Test

Final Examination

Candidates will sit a formal examination on a computer. The exam is taken online by students globally.

Marking Classifications:

Students achieving an overall mark of 70% or higher will be awarded the Certificate with Distinction. The total mark is calculated as equally weighted marks for module tests and final exam.

• Distinction: 70-100%; US equivalent: A/A+
• Merit: 60-69%; US equivalent: B+/A
• Pass: 50-59%; US equivalent: B-/B

Module 7: QUANTITATIVE Analyst Developer Strat: THE PROFESSION: 15+ hours – Self study

Course curriculum

This is the first of a kind course that teaches what the job of a quantitative analyst, developer or strat really is. There will be nearly no math in the course, but you will learn about the exact types of the quant jobs on the sell and buy-side, in consulting and in fintech, daily routines of different types of quants and their interaction among themselves and with other stakeholders, quant reports and other deliverables, quant library organization, the quant R&D process on the sell and buy-side and, finally, the skills that are really necessary beyond the technical ones and the career prospects.

There is also a special section providing a very high-level view on the quant modelling for pricing, risk and trading strategy design.

Sections on the organizational structure, quant job types and quant deliverables will be also useful for non-quants, including MBAs and CFA candidates.

Introduction

• Welcome
• The course outline
• Who is this course for?
• Evolution of the profession
• Pricing and signal vs Risk and Return
• The ultimate motivation
• Why becoming a quant?
• (Special topic) Probability vs Statistics vs ML
• (Special topic) Why everything needs to be priced?

Financial firms and quant roles

• Financial businesses and risks
• Sell-side organisation
• Sell-side quants
• Buy-side organisation and quants
• Financial Consulting
• Fintech and financial software vendors

Daily tasks and reports

• Daily operations of a trading desk or a POD
• Products, positions, books; ageing and position effects
• Evolution of the quant and IT responsibilities
• FO Reports: Position and PNL
• FO Reports: Scenarios and Risk
• FO Reports: PNL explain and PNL predict
• Regulatory reports and limits

Quant libraries and systems

• Quant library. Use cases and deployment
• Quant library organization
• The pricing function
• End user tools
• Case Study: Numerix

Required skills

• Introduction
• Required skills. Part 1
• Required Skills. Part 2

Grades. Progression and Pay.

• Grades. Progression and pay. Introduction
• Grades. Progression and pay

Final Examination: 

Examination Preparation Week: Thursday 1st October 2026
Examination Date: Thursday 22nd October 2026

• Candidates will sit a formal examination on a computer. The exam is taken online by students globally.

Marking Classifications:

Students achieving an overall mark of 70% or higher will be awarded the Certificate with Distinction. The total mark is calculated as equally weighted marks for module tests and final exam.

• Distinction: 70-100%; US equivalent: A/A+
• Merit: 60-69%; US equivalent: B+/A
• Pass: 50-59%; US equivalent: B-/B