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Data Science From Scratch: First Principles With Python
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Data Science Libraries, Frameworks, Modules, And Toolkits Are Great For Doing Data Science, But Theyre Also A Good Way To Dive Into The Discipline Without Actually Understanding Data Science. In This Book, Youll Learn How Many Of The Most Fundamental Data Science Tools And Algorithms Work By Implementing Them From Scratch.If You Have An Aptitude For Mathematics And Some Programming Skills, Author Joel Grus Will Help You Get Comfortable With The Math And Statistics At The Core Of Data Science, And With Hacking Skills You Need To Get Started As A Data Scientist. Todays Messy Glut Of Data Holds Answers To Questions No Ones Even Thought To Ask. This Book Provides You With The Knowhow To Dig Those Answers Out. Get A Crash Course In Python Learn The Basics Of Linear Algebra, Statistics, And Probabilityand Understand How And When They'Re Used In Data Science Collect, Explore, Clean, Munge, And Manipulate Data Dive Into The Fundamentals Of Machine Learning Implement Models Such As Knearest Neighbors, Naive Bayes, Linear And Logistic Regression, Decision Trees, Neural Networks, And Clustering Explore Recommender Systems, Natural Language Processing, Network Analysis, Mapreduce, And Databases
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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 is the page count of this book? A: This book has three hundred thirty pages. It provides ample content for learning fundamental data science concepts.
- Q: What is the binding type of this book? A: This book is paperback bound. This makes it lightweight and easy to carry around.
- Q: What are the dimensions of this book? A: The book measures seven point zero one inches in length, zero point seven five inches in width, and nine point two five inches in height. These dimensions make it a compact option for readers.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It starts with fundamental concepts and gradually explains more complex data science tools.
- Q: How do I implement the algorithms in this book? A: You can implement the algorithms by following the step-by-step instructions provided in the chapters. The book encourages hands-on practice with coding examples.
- Q: What programming language is used in this book? A: The book primarily uses Python for its examples and implementations. Python is a popular language for data science.
- Q: How do I keep this book in good condition? A: To keep this book in good condition, store it in a dry place away from direct sunlight. Avoid bending or damaging the spine.
- Q: Is this book appropriate for children? A: No, this book is not specifically designed for children. It assumes a basic understanding of mathematics and programming.
- Q: What if I receive a damaged copy of the book? A: If you receive a damaged copy, you should contact customer service for a return or replacement. Most retailers have a policy in place for such issues.
- Q: Does this book cover machine learning? A: Yes, this book covers the fundamentals of machine learning. It includes implementations of various models like decision trees and neural networks.
- Q: Can I use this book for self-study? A: Yes, this book is ideal for self-study. It provides clear explanations and exercises to test your understanding.
- Q: What data science concepts are introduced in this book? A: The book introduces several concepts, including data collection, cleaning, and manipulation, along with statistical fundamentals.
- Q: Is there a warranty for this book? A: Typically, there is no warranty for books. However, return policies vary by retailer. Check with your seller for details.
- Q: Are there any prerequisites for reading this book? A: Yes, a basic understanding of mathematics and some programming skills are recommended to fully benefit from this book.
- Q: Does this book provide practical examples? A: Yes, the book provides practical examples and coding exercises to help reinforce the material discussed.