Introduction To Python For Quantitative Finance: From Scratch To Productivity
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A01=Aitor Muguruza
A01=Antoine Jacquier
A01=Ewan Mackie
A01=Paul Alexander Bilokon
AI
Analysis
Analytics
Artificial Intelligence
Author_Aitor Muguruza
Author_Antoine Jacquier
Author_Ewan Mackie
Author_Paul Alexander Bilokon
Big Data
Bioinformatics
Category=UNC
Category=UYQM
Classification
Clustering
Data
Data Analysis
Data Science
Data Scientist
Data Visualisation
Dataset
Datasets
Deep Learning
Development
Dimensionality Reduction
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Forecasting
forthcoming
High-frequency Data
Image Analysis
Image Classification
Image Processing
Keras
Linear Algebra
Linear Regression
Machine Learning
Matplotlib
Matrices
ML
Modelling
Neural Nets
Neural Networks
NumPy
Optimisation Theory
PCA
Principal Components Analysis
Probability Theory
Programming
Programming Languages
Pylab
Python
Python Libraries
Regression
Science
Scientific Computation
Scientific Computing
Scikit-learn
SciPy
Software
Software Development
Statistical Analysis
Statistical Modelling
Statistics
Supervised Learning
Technology
TensorFlow
Time Series Analysis
Unsupervised Learning
Vectors
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Product details
- ISBN 9789811215728
- Publication Date: 28 Jun 2026
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
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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.
