Название: Introduction to Python - Data Science, Quantitative Finance (2.0), Second Edition
Автор: Lilan Li
Издательство: Independently published
Год: 2021
Язык: английский
Формат: pdf, epub
Размер: 10.2 MB

It is a book for both beginners and experienced professionals who either have a relevant educational background or are interested in learning Python under the data science or quantitative finance background.

No prior experience in Python is required. It is a practical book complete with working code that guides the reader through the basics of Python. Topics are introduced gradually, each building on the last. The examples are either run in a command line or an editor. Jupyter notebook examples are presented in later part of the book where mathematical models and data analysis of time series are introduced.

This book is neither a Python reference nor a short cut to a job interview in the quantitative finance arena. It is an iceberg view to some of the practical usage of Python in the financial market and mathematical modelling, from beginner to intermedia level.

BY THE END OF THIS BOOK, YOU WILL BE ABLE TO:
- gain a general understanding of Python
- write basic Python code
- write Python function to perform efficient data analysis and simple financial model analysis for those who have prior knowledge of programming ( you could skip the first chapter)

WHO IS THIS BOOK FOR:
This book is directed at both industry practitioners and students interested in learning Python for financial modelling or/and data science purpose. It helps to advance your career either within the finance modelling arena or the Data science field! You will also find this book useful if you want to extend the your existing programming language knowledge in another application field.

TABLE OF CONTENT:
1 Introduction
1.1 Basic of programming language
1.2 What is Python?
1.3 Python Development Environment
1.4 Basic data types in Python
1.5 Looping and Condition
1.6 Python functions
1.7 Further examples
2 Python Packages
2.1 Mathematics - NumPy
2.2 Data Analysis - Pandas
2.3 Visual Plot – Matplotlib
3 Advanced Python Examples
3.1 Mathematical modelling
3.2 Visual graphic examples
3.3 Model Parameter Analysis
3.4 Object Oriented Programming (OOP)