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Python for Finance Cookbook. Over 80 powerful recipes for effective financial data analysis - Second Edition Eryk Lewinson

(ebook) (audiobook) (audiobook) Książka w języku 1
Python for Finance Cookbook. Over 80 powerful recipes for effective financial data analysis - Second Edition Eryk Lewinson - okladka książki

Python for Finance Cookbook. Over 80 powerful recipes for effective financial data analysis - Second Edition Eryk Lewinson - okladka książki

Python for Finance Cookbook. Over 80 powerful recipes for effective financial data analysis - Second Edition Eryk Lewinson - audiobook MP3

Python for Finance Cookbook. Over 80 powerful recipes for effective financial data analysis - Second Edition Eryk Lewinson - audiobook CD

Autor:
Eryk Lewinson
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
740
Dostępne formaty:
     PDF
     ePub
Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.

You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.

Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.

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O autorze książki

Eryk Lewinson received his master's degree in Quantitative Finance from Erasmus University Rotterdam. In his professional career, he has gained experience in the practical application of data science methods while working in risk management and data science departments of two "big 4" companies, a Dutch neo-broker and most recently the Netherlands' largest online retailer.
Outside of work, he has written over a hundred articles about topics related to data science, which have been viewed more than 3 million times. In his free time, he enjoys playing video games, reading books, and traveling with his girlfriend.

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