Differences

This shows you the differences between two versions of the page.

--- b:data_science_from_scratch [2018/02/01 22:25] – created hkimscil
+++ b:data_science_from_scratch [2018/02/02 02:29] (current) – hkimscil
@@ Line 2: / Line 2: @@
 owned book
+====== Data Science from Scratch ======
+====== Preface ======
+===== Data Science =====
+===== From Scratch =====
+===== Conventions Used in This Book =====
+===== Using Code Examples =====
+===== Safari® Books Online =====
+===== How to Contact Us =====
+===== Acknowledgments =====
+====== 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 ====
+====== 2. A Crash Course in Python ======
+===== The Basics =====
+==== Getting Python ====
+==== The Zen of Python ====
+==== Whitespace Formatting ====
+==== Modules ====
+==== Arithmetic ====
+==== Functions ====
+==== Strings ====
+==== Exceptions ====
+==== Lists ====
+==== Tuples ====
+==== Dictionaries ====
+=== defaultdict ===
+=== Counter ===
+==== Sets ====
+==== Control Flow ====
+==== Truthiness ====
+===== The Not-So-Basics =====
+==== Sorting ====
+==== List Comprehensions ====
+==== Generators and Iterators ====
+==== Randomness ====
+==== Regular Expressions ====
+==== Object-Oriented Programming ====
+==== Functional Tools ====
+==== enumerate ====
+==== zip and Argument Unpacking ====
+==== args and kwargs ====
+==== Welcome to DataSciencester! ====
+===== For Further Exploration =====
+====== 3. Visualizing Data ======
+===== matplotlib =====
+===== Bar Charts =====
+===== Line Charts =====
+===== Scatterplots =====
+===== For Further Exploration =====
+====== 4. Linear Algebra ======
+===== Vectors =====
+===== Matrices =====
+===== For Further Exploration =====
+====== 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 =====
+====== 6. Probability ======
+===== Dependence and Independence =====
+===== Conditional Probability =====
+===== Bayes’s Theorem =====
+===== Random Variables =====
+===== Continuous Distributions =====
+===== The Normal Distribution =====
+===== The Central Limit Theorem =====
+===== For Further Exploration =====
+====== 7. Hypothesis and Inference ======
+===== Statistical Hypothesis Testing =====
+===== Example: Flipping a Coin =====
+===== Confidence Intervals =====
+===== P-hacking =====
+===== Example: Running an A/B Test =====
+===== Bayesian Inference =====
+===== For Further Exploration =====
+====== 8. Gradient Descent ======
+===== The Idea Behind Gradient Descent =====
+===== Estimating the Gradient =====
+===== Using the Gradient =====
+===== Choosing the Right Step Size =====
+===== Putting It All Together =====
+===== Stochastic Gradient Descent =====
+===== For Further Exploration =====
+====== 9. Getting Data ======
+===== stdin and stdout =====
+===== Reading Files =====
+==== The Basics of Text Files ====
+==== Delimited Files ====
+===== Scraping the Web =====
+==== HTML and the Parsing Thereof ====
+==== Example: O’Reilly Books About Data ====
+===== Using APIs =====
+==== JSON (and XML) ====
+==== Using an Unauthenticated API ====
+==== Finding APIs ====
+===== Example: Using the Twitter APIs =====
+==== Getting Credentials ====
+=== Using Twython ===
+===== For Further Exploration =====
+====== 10. Working with Data ======
+===== Exploring Your Data =====
+==== Exploring One-Dimensional Data ====
+==== Two Dimensions ====
+==== Many Dimensions ====
+===== Cleaning and Munging =====
+===== Manipulating Data =====
+===== Rescaling =====
+===== Dimensionality Reduction =====
+===== For Further Exploration =====
+====== 11. Machine Learning ======
+===== Modeling =====
+===== What Is Machine Learning? =====
+===== Overfitting and Underfitting =====
+===== Correctness =====
+===== The Bias-Variance Trade-off =====
+===== Feature Extraction and Selection =====
+===== For Further Exploration =====
+====== 12. k-Nearest Neighbors ======
+===== The Model =====
+===== Example: Favorite Languages =====
+===== The Curse of Dimensionality =====
+===== For Further Exploration =====
+====== 13. Naive Bayes ======
+===== A Really Dumb Spam Filter =====
+===== A More Sophisticated Spam Filter =====
+===== Implementation =====
+===== Testing Our Model =====
+===== For Further Exploration =====
+====== 14. Simple Linear Regression ======
+===== The Model =====
+===== Using Gradient Descent =====
+===== Maximum Likelihood Estimation =====
+===== For Further Exploration =====
+====== 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 =====
+====== 16. Logistic Regression ======
+===== The Problem =====
+===== The Logistic Function =====
+===== Applying the Model =====
+===== Goodness of Fit =====
+===== Support Vector Machines =====
+===== For Further Investigation =====
+====== 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 =====
+====== 18. Neural Networks ======
+===== Perceptrons =====
+===== Feed-Forward Neural Networks =====
+===== Backpropagation =====
+===== Example: Defeating a CAPTCHA =====
+===== For Further Exploration =====
+====== 19. Clustering ======
+===== The Idea =====
+===== The Model =====
+===== Example: Meetups =====
+===== Choosing k =====
+===== Example: Clustering Colors =====
+===== Bottom-up Hierarchical Clustering =====
+===== For Further Exploration =====
+====== 20. Natural Language Processing ======
+===== Word Clouds =====
+===== n-gram Models =====
+===== Grammars =====
+===== An Aside: Gibbs Sampling =====
+===== Topic Modeling =====
+===== For Further Exploration =====
+====== 21. Network Analysis ======
+===== Betweenness Centrality =====
+===== Eigenvector Centrality =====
+==== Matrix Multiplication ====
+==== Centrality ====
+===== Directed Graphs and PageRank =====
+===== For Further Exploration =====
+====== 22. Recommender Systems ======
+===== Manual Curation =====
+===== Recommending What’s Popular =====
+===== User-Based Collaborative Filtering =====
+===== Item-Based Collaborative Filtering =====
+===== For Further Exploration =====
+====== 23. Databases and SQL ======
+===== CREATE TABLE and INSERT =====
+===== UPDATE =====
+===== DELETE =====
+===== SELECT =====
+===== GROUP BY =====
+===== ORDER BY =====
+===== JOIN =====
+===== Subqueries =====
+===== Indexes =====
+===== Query Optimization =====
+===== NoSQL =====
+===== For Further Exploration =====
+====== 24. MapReduce ======
+===== Example: Word Count =====
+===== Why MapReduce? =====
+===== MapReduce More Generally =====
+===== Example: Analyzing Status Updates =====
+===== Example: Matrix Multiplication =====
+===== An Aside: Combiners =====
+===== For Further Exploration =====
+====== 25. Go Forth and Do Data Science ======
+===== IPython =====
+===== Mathematics =====
+===== Not from Scratch =====
+==== NumPy ====
+==== pandas ====
+==== scikit-learn ====
+==== Visualization ====
+==== R ====
+===== Find Data =====
+===== Do Data Science =====
+==== Hacker News ====
+==== Fire Trucks ====
+==== T-shirts ====
+==== And You? ====
+===== Index =====