Practical Statistics for Data Scientists: 50 Must-Know Concepts

Statistics is the backbone of modern data science, yet most resources are either too theoretical or overly simplified. Practical Statistics for Data Scientists bridges that gap by presenting 50 essential concepts in a digestible, practical format. Covering data exploration, variability, probability, hypothesis testing, regression, classification, resampling, and unsupervised learning, the book explains what each concept means, when to use it, and how it applies in real projects. With clear explanations and examples in R and Python, it serves as both a learning guide and a handy reference for professionals. Whether you’re a beginner building your statistical foundation or an experienced data scientist refreshing your knowledge, this book equips you with the statistical tools needed for data-driven decision making.