Data Science From Scratch: First Principles With Python

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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Frequently Asked Questions

  • Q: What is the main focus of 'Data Science from Scratch: First Principles with Python'? A: The book focuses on teaching the fundamental concepts of data science by implementing core tools and algorithms from scratch, with an emphasis on understanding the underlying mathematics and statistics.
  • Q: Who is the author of this book? A: The author of 'Data Science from Scratch' is Joel Grus.
  • Q: What programming language does the book primarily use? A: The book primarily uses Python to illustrate data science concepts and implementations.
  • Q: Is this book suitable for beginners in data science? A: Yes, the book is suitable for beginners, particularly those with some aptitude for mathematics and basic programming skills.
  • Q: How many pages does the book have? A: The book contains 330 pages.
  • Q: What kind of topics does this book cover? A: The book covers a wide range of topics including linear algebra, statistics, machine learning algorithms, data collection, cleaning, and manipulation.
  • Q: What is the publication date of this book? A: The book was published on May 26, 2015.
  • Q: What is the binding type of this book? A: The book is available in paperback binding.
  • Q: What are some machine learning models discussed in the book? A: The book discusses several machine learning models, including k-nearest neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering.
  • Q: Does the book include practical exercises or examples? A: Yes, the book includes practical exercises and examples to help readers apply the concepts they learn.