Practical Statistics for Data Scientists - 50+ Essential Concepts Using R and Python 2nd Edition

Many data science resources cover statistical methods but often lack a deeper statistical perspective. If you're familiar with R or Python and have some background in statistics, this quick reference is designed to help you.

With this book, you'll learn:

- Why exploratory data analysis is an important first step in data science.
- How random sampling can reduce bias and improve the quality of your dataset, even with big data.
- How principles of experimental design can provide clear answers to your questions.
- How to use regression to estimate outcomes and find anomalies.
- Key classification techniques to predict which category a record belongs to.
- Statistical machine learning methods that learn from data.
- Unsupervised learning methods for making sense of unlabeled data.