Most books on modeling dive straight into the math and methodologies, leaving readers struggling to grasp the underlying assumptions and limitations. Written in a clear and concise style, Modeling Mindsets introduces approaches such as Bayesian inference, supervised learning, causal inference, and more.
With this book, you'll gain a deeper understanding of different modeling techniques, empowering you to choose the right one for your problem.
"It has taken me many years of fumbling around with ML and statistics to achieve a fraction of the intuition in the book. Save yourself the time!"
- Robert Martin
Table of ContentsThis book is for everyone who builds models from data: data scientists, statisticians, machine learners, and quantitative researchers. To get the most out of this book:
Don't read this book if:
Embrace the full potential of Modeling Mindsets by challenging your assumptions and being open to diverse perspectives.
This compact guide is perfect for data-driven professionals looking to enhance their understanding of modeling approaches, assumptions, and goals when working with data. By delving into this book, you'll save years of trial and error, foster intellectual growth, and improve collaboration with your colleagues. Gain valuable insights into Bayesian inference, supervised learning, causal inference, and more, and elevate your data analysis skills by choosing the right modeling approach for your problem. Make the most of your data-modeling journey with "Modeling Mindsets."by COLOMBI / DALL'ABACP / GALLI / LA ROTTA / NAPPER
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