December 2018
Beginner
720 pages
15h 32m
English
Content preview from Python for Finance, 2nd Edition
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Part III. Financial Data Science
This part of the book is about basic techniques, approaches, and packages for financial data science. Many topics (such as visualization) and many packages (such as scikit-learn) are fundamental for data science with Python. In that sense, this part equips the quants and financial analysts with the Python tools they need to become financial data scientists.
Like in Part II, the chapters are organized according to topics such that they can each be used as a reference for the topic of interest:
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Chapter 7 discusses static and interactive visualization with
matplotlibandplotly. -
Chapter 8 is about handling financial time series data with
pandas. -
Chapter 9 focuses on getting input/output (I/O) operations right and fast.
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Chapter 10 is all about making Python code fast.
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Chapter 11 focuses on frequently required mathematical tools in finance.
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Chapter 12 looks at using Python to implement methods from stochastics.
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Chapter 13 is about statistical and machine learning approaches.
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