Title
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python,New
Sold by Ergodebooks, an authorized reseller.
Returns accepted within 30 days | support@ergodebooks.com
Shipping Information
- Free Standard Shipping — United States only
- Processing Time: 1–3 business days
- Estimated Delivery: 3–5 business days after dispatch
- Double-boxed, fully insured & discreetly packaged
- Tracking number sent via email once dispatched
- Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund
Returns accepted within 30 days of delivery.
Damaged or Defective Item
Free return shipping + replacement or full refund
Wrong Item Received
Free return shipping + replacement or full refund
Change of Mind
Return shipping at customer's expense · 25% restocking fee applies
Payment Option
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on whats important and whats not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If youre familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, youll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higherquality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that 'learn' from data Unsupervised learning methods for extracting meaning from unlabeled data.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: What topics are covered in 'Practical Statistics for Data Scientists'? A: The book covers essential statistical concepts relevant to data science, including exploratory data analysis, random sampling, experimental design, regression analysis, classification techniques, statistical machine learning methods, and unsupervised learning methods.
- Q: Who is the author of this book? A: The author of 'Practical Statistics for Data Scientists' is Peter Bruce.
- Q: What programming languages does this book focus on? A: The book provides examples and practical guidance using R and Python, making it suitable for readers familiar with either programming language.
- Q: Is this book suitable for beginners in statistics? A: While some exposure to statistics is beneficial, the book is designed to be accessible for readers who have a basic understanding of data science concepts.
- Q: How many pages does the book have? A: The book has a total of 360 pages.
- Q: What is the publication date of the second edition? A: The second edition of 'Practical Statistics for Data Scientists' was published on June 16, 2020.
- Q: What type of binding does this book have? A: The book is available in paperback binding.
- Q: Can this book help with avoiding common statistical mistakes? A: Yes, the book provides practical guidance on avoiding misuse of statistical methods, making it a useful resource for data scientists.
- Q: What category does this book fall under? A: The book is categorized under Data Mining.
- Q: Is this book a good resource for applying statistical methods in data science? A: Yes, it offers practical insights on applying statistical methods effectively within the context of data science.