An Interview with Mark Lewinson First, why should aspiring, or existing, data analysts choose Python? Python is a powerful and flexible language that is well-suited for data analysis. Its ease of use, large community, and powerful libraries make it a great choice and it is definitely a valuable skill for data analysts to have. What’s new in this edition? There are over 30 new recipes in the book, spread throughout the chapters. For example, there is an entirely new chapter on time series forecasting, and a lot of advanced content has been added to the chapters on real-life ML projects. In the deep learning chapter, we have shifted the focus from building neural networks from scratch in PyTorch to using libraries that offer state-of-the-art architectures out of the box. Lastly, we have put more emphasis on obtaining and preparing reliable data for analysis. What makes this book necessary? What gap does it fill? While there are many books available on financial data analysis, it can be difficult to find ones that demonstrate how to solve specific tasks or problems using Python. In this book, I have attempted to showcase the vast landscape of Python libraries that can be utilized in analyses. By using those libraries, you do not have to code everything from scratch. Instead, you can quickly solve a certain task and focus on interpreting the results and making decisions based on the outcome of your analysis. What will readers be able to do after reading this book? After reading the book, readers will know how to approach various data analysis or data science tasks set in the financial context. For data science, readers will become equipped to handle tasks such as time series forecasting and building machine/deep learning models. On the quantitative finance side, they will learn modeling volatility, asset allocation, factor modeling, and algorithmic trading. Finally, do you have any advice for readers picking up your book? Sure, my advice would be to not only read the book but also spend time playing around with the code and experimenting with it. I’m a strong believer in learning by doing and I think Jupyter Notebooks offer an excellent playground to further explore the topics covered in the book. Unleash the power of Python for financial data analysis More Content Nearly 300 pages of new and expanded content Master ML Build machine learning/deep learning projects from scratch to make informed financial decisions Visualizations Visualize your data in new ways with interactive dashboards Financial Econometrics Estimate popular econometrics models such as CAPM, GARCH, and more using a problem-solution approach Modern Libraries Use modern Python libraries, such as pandas, NumPy, and scikit-learn to analyze your financial data
✔ Author(s): Eryk Lewinson
✔ Title: Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition
✔ Rating : 4.5 out of 5 base on (33 reviews)
✔ ISBN-10: 1803243198
✔ Language: English
✔ Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
✔ Device compatibles: Android, iOS, PC and Amazon Kindle
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