Description

Master essential statistical concepts for data science with this comprehensive guide featuring over 50 key techniques using R and Python. Written by experienced practitioners Peter Bruce, Andrew Bruce, and Peter Gedeck, this 360-page O’Reilly resource bridges the gap between statistical theory and practical data science applications. Learn exploratory data analysis, experimental design, regression techniques, classification methods, and machine learning algorithms through hands-on examples. Covers both supervised and unsupervised learning methods, anomaly detection, and statistical sampling techniques that improve data quality even with big datasets. Perfect for data scientists, analysts, and statisticians who need to apply statistical methods in real-world scenarios. Features code examples in both R and Python, making it accessible regardless of your preferred programming language. This updated 2nd edition includes modern approaches to statistical machine learning and reflects current best practices in the rapidly evolving field of data science.

• 50+ Essential Concepts: Comprehensive coverage of key statistical methods for data science practice

• Dual Language Support: Code examples and techniques demonstrated in both R and Python

• Expert Authors: Written by seasoned practitioners from Amazon, Statistics.com, and industry leaders

• Practical Focus: Real-world applications of statistical theory with hands-on examples and case studies

• Complete Methodology: EDA, experimental design, regression, classification, and machine learning

• Updated 2nd Edition: Modern approaches reflecting current data science best practices and tools

• O’Reilly Quality: 360 pages of authoritative content from trusted technical publisher

• Learning Path: Structured progression from basic concepts to advanced statistical machine learning