Data science from scratch : first principles with Python / Joel Grus.

Includes index.

IT Carlow ebook

Includes bibliographical references and index.

Cover; Copyright; Table of Contents; Preface to the Second Edition; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Preface to the First Edition; Data Science; From Scratch; Chapter 1. Introduction; The Ascendance of Data; What Is Data Science?; Motivating Hypothetical: DataSciencester; Finding Key Connectors; Data Scientists You May Know; Salaries and Experience; Paid Accounts; Topics of Interest; Onward; Chapter 2. A Crash Course in Python; The Zen of Python; Getting Python; Virtual Environments; Whitespace Formatting

ModulesFunctions; Strings; Exceptions; Lists; Tuples; Dictionaries; defaultdict; Counters; Sets; Control Flow; Truthiness; Sorting; List Comprehensions; Automated Testing and assert; Object-Oriented Programming; Iterables and Generators; Randomness; Regular Expressions; Functional Programming; zip and Argument Unpacking; args and kwargs; Type Annotations; How to Write Type Annotations; Welcome to DataSciencester!; For Further Exploration; Chapter 3. Visualizing Data; matplotlib; Bar Charts; Line Charts; Scatterplots; For Further Exploration; Chapter 4. Linear Algebra; Vectors; Matrices

For Further ExplorationChapter 5. Statistics; Describing a Single Set of Data; Central Tendencies; Dispersion; Correlation; Simpson's Paradox; Some Other Correlational Caveats; Correlation and Causation; For Further Exploration; Chapter 6. Probability; Dependence and Independence; Conditional Probability; Bayes's Theorem; Random Variables; Continuous Distributions; The Normal Distribution; The Central Limit Theorem; For Further Exploration; Chapter 7. Hypothesis and Inference; Statistical Hypothesis Testing; Example: Flipping a Coin; p-Values; Confidence Intervals; p-Hacking

Example: Running an A/B TestBayesian Inference; For Further Exploration; Chapter 8. Gradient Descent; The Idea Behind Gradient Descent; Estimating the Gradient; Using the Gradient; Choosing the Right Step Size; Using Gradient Descent to Fit Models; Minibatch and Stochastic Gradient Descent; For Further Exploration; Chapter 9. Getting Data; stdin and stdout; Reading Files; The Basics of Text Files; Delimited Files; Scraping the Web; HTML and the Parsing Thereof; Example: Keeping Tabs on Congress; Using APIs; JSON and XML; Using an Unauthenticated API; Finding APIs

Example: Using the Twitter APIsGetting Credentials; For Further Exploration; Chapter 10. Working with Data; Exploring Your Data; Exploring One-Dimensional Data; Two Dimensions; Many Dimensions; Using NamedTuples; Dataclasses; Cleaning and Munging; Manipulating Data; Rescaling; An Aside: tqdm; Dimensionality Reduction; For Further Exploration; Chapter 11. Machine Learning; Modeling; What Is Machine Learning?; Overfitting and Underfitting; Correctness; The Bias-Variance Tradeoff; Feature Extraction and Selection; For Further Exploration; Chapter 12. k-Nearest Neighbors; The Model

Online resource; title from PDF title page (EBSCO, April 16, 2019).

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