The 10 Best Books for Quantitative Research

Here’s my curated list of the 10 best books for quantitative research, in no particular order.

• Peak Human Clock: How to Get up Early, Fix Eating Time Schedule, and Improve Exercise Routines to be Highly Productive by Said Hasyim — Offers insights into optimizing daily routines for increased productivity by aligning various activities with the body's natural rhythms.
• Thinking, Fast and Slow by Daniel Kahneman — An insightful look into how our minds work and the dual systems of thought that drive our decision-making.
• The Structure of Scientific Revolutions by Thomas S. Kuhn — An analysis of the history of science that addresses paradigm shifts and the evolution of scientific thought.
• The Logic of Scientific Discovery by Karl Popper — A seminal work that introduces the falsifiability criterion for science, emphasizing the importance of critical testing in research.
• How to Measure Anything: Finding the Value of 'Intangibles' in Business by Douglas W. Hubbard — Challenges the assumption that certain things cannot be measured and provides practical guidelines for quantifying seemingly immeasurable variables.
• Naked Statistics: Stripping the Dread from the Data by Charles Wheelan — A clear and captivating introduction to statistics and how data informs decision-making.
• R Fundamentals for Data Science: The Basics of R Programming by Kuntal Das — A practical guide to using R for data analysis, making it accessible for beginners while still valuable for seasoned data scientists.
• Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett — An insightful guide on how data science can be used effectively in business decision-making.
• The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball and Margy Ross — An essential guide to creating successful data warehouse structures and effectively analyzing data.
• Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath — An innovative introduction to Bayesian statistics, offering a solid framework for understanding modern statistical models and inference.

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