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8 Data Science Resources For Investment Management Professionals

Looking to improve your investment processes and outcomes? Check out these data science resources specifically for investment management professionals.

Investment managers are increasingly interested in using data science and artificial intelligence to improve their processes and outcomes. Recently, a few clients have asked our team how they can sharpen their skills in the subject.  

The problem is that most entry-level data science material is not very useful for finance, and the material useful for finance is not entry-level by any means. Not to worry, our team has shared their top books and articles for investment professionals eager to learn about solving problems with data. Read on for a list of our top eight picks.

Algorithms to Live By- The Computer Science of Human Decisions

Authors Brian Christian and Tom Griffiths show how algorithms developed for computers also untangle very human questions by explaining, in layman’s terms, algorithmic solutions for real-world decision making. If you like problem-solving and decision theory, you’ll love this book.

Recommended by Cameron Hight, Alpha Theory CEO

Big Data: A Revolution That Will Transform How We Live, Work, and Think  

Viktor Mayer-Schönberger and Kenneth Cukier, two leading experts in data science, wrote this non-technical book that discusses what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. It’s a great place to start for those who wish to get into a data-oriented mindset, but do not have direct experience.

Recommended by Aaron Hirsch, Data Scientist at Alpha Theory

CRISP-DM – a Standard Methodology to Ensure a Good Outcome
CRISP-DM is a framework for applying data science to business problems. This article gives a solid explanation on how to approach a project before getting started. For those getting started in practicing data science, it will save them time by helping to avoid rabbit holes.

Recommended by Billy Armfield, Data Scientist at Alpha Theory

The 7 Steps of Machine Learning

This article, written by a google developer, outlines in broad strokes the steps in a typical machine learning problem. It walks through a basic example to describe the process of getting answers from data using machine learning. Readers will gain a foundational framework to think through the problem and the language to articulate each step.

Recommended by Billy Armfield, Data Scientist at Alpha Theory

Machine Learning: An Applied Mathematics Introduction by Paul Wilmott
This slim book by uber-quant Paul Wilmott gives clear and detailed explanations of the machine learning models most used in quant finance, along with pointers to further reading. While the book assumes basic calculus and linear algebra skills, it is an approachable resource for those who desire a deeper understanding of machine learning models without dense textbook reading.

Recommended by Ross Fabricant, Director of Data Science at CenterBook Partners

Statistical Methods for Machine Learning- Learn How to Transform Data into Knowledge with Python
Machine learning specialist Jason Brownlee provides a thorough hands-on introduction to statistics and hypothesis testing with step-by-step instructions through Python-based projects. The book builds a solid foundation for future discovery and assumes little prior knowledge of statistics and coding.

Recommended by Chris White, Head of Portfolio Implementation & Risk at CenterBook Partners

Machine Learning Mastery with Python- Understand Your Data, Create Accurate Models, and Work Projects End-to-End
Also, by Jason Brownlee, this step-by-step guide helps the reader master foundational techniques in machine learning, using Python with scikit-learn, pandas, tensorflow and other helpful libraries. It is written in an engaging and accessible style, without assuming much prior knowledge.

Recommended by Chris White, Asia CEO & Head of Risk & Portfolio Implementation at CenterBook Partners

An Introduction to the Bootstrap
Bradley Efron and Robert J. Tibshirani arm scientists and engineers with computational techniques to analyze and understand complicated data sets, without relying on an understanding of advanced mathematics. But be warned- this dense academic textbook is no-nonsense. Fancy charts and descriptions of tooling are few and far between.  

Recommended by Ake Kullenberg, Head of Execution Trading at CenterBook Partners

Were there any books that have been helpful to you as you begun learning about data science? We’d love to know.

Get in Touch with Alpha Theory

If you have questions about the resources mentioned above, our in-house data science team, or our leading portfolio construction platform and services for investment managers, please do not hesitate to reach out.  

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