Data science from scratch : first principles with Python.

IT Carlow ebook

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 -- Modules -- Functions -- 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 Exploration -- Chapter 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 Test -- Bayesian 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 APIs -- Getting 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 -- Example: The Iris Dataset -- The Curse of Dimensionality -- For Further Exploration -- Chapter 13. Naive Bayes -- A Really Dumb Spam Filter -- A More Sophisticated Spam Filter -- Implementation -- Testing Our Model -- Using Our Model -- For Further Exploration -- Chapter 14. Simple Linear Regression -- The Model -- Using Gradient Descent -- Maximum Likelihood Estimation -- For Further Exploration -- Chapter 15. Multiple Regression -- The Model -- Further Assumptions of the Least Squares Model -- Fitting the Model -- Interpreting the Model.

Goodness of Fit -- Digression: The Bootstrap -- Standard Errors of Regression Coefficients -- Regularization -- For Further Exploration -- Chapter 16. Logistic Regression -- The Problem -- The Logistic Function -- Applying the Model -- Goodness of Fit -- Support Vector Machines -- For Further Investigation -- Chapter 17. Decision Trees -- What Is a Decision Tree? -- Entropy -- The Entropy of a Partition -- Creating a Decision Tree -- Putting It All Together -- Random Forests -- For Further Exploration -- Chapter 18. Neural Networks -- Perceptrons -- Feed-Forward Neural Networks -- Backpropagation -- Example: Fizz Buzz -- For Further Exploration -- Chapter 19. Deep Learning -- The Tensor -- The Layer Abstraction -- The Linear Layer -- Neural Networks as a Sequence of Layers -- Loss and Optimization -- Example: XOR Revisited -- Other Activation Functions -- Example: FizzBuzz Revisited -- Softmaxes and Cross-Entropy -- Dropout -- Example: MNIST -- Saving and Loading Models -- For Further Exploration -- Chapter 20. Clustering -- The Idea -- The Model -- Example: Meetups -- Choosing k -- Example: Clustering Colors -- Bottom-Up Hierarchical Clustering -- For Further Exploration -- Chapter 21. Natural Language Processing -- Word Clouds -- n-Gram Language Models -- Grammars -- An Aside: Gibbs Sampling -- Topic Modeling -- Word Vectors -- Recurrent Neural Networks -- Example: Using a Character-Level RNN -- For Further Exploration -- Chapter 22. Network Analysis -- Betweenness Centrality -- Eigenvector Centrality -- Matrix Multiplication -- Centrality -- Directed Graphs and PageRank -- For Further Exploration -- Chapter 23. Recommender Systems -- Manual Curation -- Recommending What's Popular -- User-Based Collaborative Filtering -- Item-Based Collaborative Filtering -- Matrix Factorization -- For Further Exploration -- Chapter 24. Databases and SQL.

CREATE TABLE and INSERT -- UPDATE -- DELETE -- SELECT -- GROUP BY -- ORDER BY -- JOIN -- Subqueries -- Indexes -- Query Optimization -- NoSQL -- For Further Exploration -- Chapter 25. MapReduce -- Example: Word Count -- Why MapReduce? -- MapReduce More Generally -- Example: Analyzing Status Updates -- Example: Matrix Multiplication -- An Aside: Combiners -- For Further Exploration -- Chapter 26. Data Ethics -- What Is Data Ethics? -- No, Really, What Is Data Ethics? -- Should I Care About Data Ethics? -- Building Bad Data Products -- Trading Off Accuracy and Fairness -- Collaboration -- Interpretability -- Recommendations -- Biased Data -- Data Protection -- In Summary -- For Further Exploration -- Chapter 27. Go Forth and Do Data Science -- IPython -- Mathematics -- Not from Scratch -- NumPy -- pandas -- scikit-learn -- Visualization -- R -- Deep Learning -- Find Data -- Do Data Science -- Hacker News -- Fire Trucks -- T-Shirts -- Tweets on a Globe -- And You? -- Index -- About the Author -- Colophon.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.