Synopsis
This book is written for both newcomers and experienced practitioners working at the intersection of data science, machine learning, and finance. It is designed to allow readers with no formal prerequisites to enter these fields with confidence, while also providing sufficient depth to be valuable to professionals. Beginning with a gentle introduction to Python, the book gradually progresses to more advanced language features and the mathematical foundations required to understand key models in quantitative finance. Throughout, the emphasis is on developing both conceptual understanding and practical skills. The material strikes a careful balance between the mathematics underpinning modern financial models and the practical considerations of data science and machine learning. Concepts are introduced and reinforced through hands-on case studies based on real financial datasets, enabling readers to gain experience working with realistic data and workflows. The contents of this book have been refined over many years of teaching to students and practitioners with diverse backgrounds at Imperial College London and the Thalesians Intensive Summer School in Artificial Intelligence, and reflects both academic rigor and real-world relevance.
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About the Authors
Dr Paul Alexander Bilokon is CEO and Founder of Thalesians Ltd. He previously served as Director and head of global credit and core e-trading quants at Deutsche Bank, with the teams that he helped set up with Jason Batt and Martin Zinkin. Having also worked at Morgan Stanley, Lehman Brothers, and Nomura, Paul pioneered electronic trading in credit with Rob Smith and William Osborn.
Paul graduated from Christ Church, University of Oxford, with a distinction and Best Overall Performance prize. He also graduated twice from Imperial College London.
Paul teaches data science and machine learning to MSc students in mathematics and finance at Imperial College London. He has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. Paul's books are being published by World Scientific, Springer, and Wiley.
Dr Antoine Jacquier is Professor of Mathematics at Imperial College London and a scientific consultant for several Financial and Tech companies. His research lies in Stochastic Analysis and Quantum Computing, with a particular focus on financial applications including volatility modelling, pricing and calibration.
Dr Ewan Mackie is a Quantitative Researcher at a tier one investment bank specializing in statistical modelling and machine learning. Ewan has over a decade of experience designing and building tooling and analytics in python. Ewan graduated from King's College London and Imperial College London, UK. and completed postdoctoral research at Instituto de Matemática Pura e Aplicada (IMPA) in Rio de Janeiro, Brazil.
Dr Aitor Muguruza is Chief Artificial Intelligence Officer at Kaiju Capital Management, Spain. Aitor's journey in finance began as an undergraduate exchange student studying maths at the University of Texas, where he became fascinated by the bridge between stochastic calculus and finance. He pursued an MSc in Mathematical Finance at Imperial College London for which he was awarded the Natixis Foundation for Quantitative Research 2017 prize for best Master's thesis (across EU and UK). Aitor has since been an active member of the research community publishing several peer-reviewed papers. He went on to earn a PhD in mathematics from Imperial College London, and his research areas include stochastic volatility modelling, machine learning, and AI in finance. He is an expert in Monte Carlo simulation methods and is skilled in C#, C++, and Python. Before joining Kaiju, Aitor served as a Quantitative Research Analyst at Natixis, where he worked in the equities division.
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An Introduction To Python For Quantitative Finance: From Scratch To Productivity (Hardcover)
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Hardcover. Condition: new. Hardcover. Written by a data scientist with decades of practical experience on some of the most challenging datasets, this book caters to both novices and professionals working in the fields of data science and machine learning. It enables novices to enter these fields with confidence and ease. There are no formal prerequisites. An introduction to Python is included. The book then proceeds to cover some of the language's most advanced features. The mathematical foundations that follow are meticulously explained. The discussion then proceeds to a practical consideration of data science and machine learning. The material is delivered through case studies developed on real datasets. The reader is invited to follow these case studies, and so develop experience in working with real data. Numerous solved exercises make this work suitable for self-study. The contents of this book have been taught by the author to students of different age groups and levels of experience at Imperial College London and at the Thalesians Intensive Summer School in Artificial Intelligence over the years. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9789811215728
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Hardback. Condition: New. This book is written for both newcomers and experienced practitioners working at the intersection of data science, machine learning, and finance. It is designed to allow readers with no formal prerequisites to enter these fields with confidence, while also providing sufficient depth to be valuable to professionals.Beginning with a gentle introduction to Python, the book gradually progresses to more advanced language features and the mathematical foundations required to understand key models in quantitative finance. Throughout, the emphasis is on developing both conceptual understanding and practical skills.The material strikes a careful balance between the mathematics underpinning modern financial models and the practical considerations of data science and machine learning. Concepts are introduced and reinforced through hands-on case studies based on real financial datasets, enabling readers to gain experience working with realistic data and workflows.The contents of this book have been refined over many years of teaching to students and practitioners with diverse backgrounds at Imperial College London and the Thalesians Intensive Summer School in Artificial Intelligence, and reflects both academic rigor and real-world relevance. Seller Inventory # LU-9789811215728
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Hardback. Condition: New. This book is written for both newcomers and experienced practitioners working at the intersection of data science, machine learning, and finance. It is designed to allow readers with no formal prerequisites to enter these fields with confidence, while also providing sufficient depth to be valuable to professionals.Beginning with a gentle introduction to Python, the book gradually progresses to more advanced language features and the mathematical foundations required to understand key models in quantitative finance. Throughout, the emphasis is on developing both conceptual understanding and practical skills.The material strikes a careful balance between the mathematics underpinning modern financial models and the practical considerations of data science and machine learning. Concepts are introduced and reinforced through hands-on case studies based on real financial datasets, enabling readers to gain experience working with realistic data and workflows.The contents of this book have been refined over many years of teaching to students and practitioners with diverse backgrounds at Imperial College London and the Thalesians Intensive Summer School in Artificial Intelligence, and reflects both academic rigor and real-world relevance. Seller Inventory # LU-9789811215728
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