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"WorldFacts/introduction.rst", "WorldFacts/toctree.rst", "index.rst", "preface.rst"], "titles": ["<span class=\"section-number\">7.3. </span>Finding the Cheaters", "<span class=\"section-number\">7.2. </span>Good Data Sources", "<span class=\"section-number\">7.1. </span>Introduction", "<span class=\"section-number\">7. </span>Ethical and Legal Considerations in Using Data", "&lt;no title&gt;", "<span class=\"section-number\">10.3. </span>Exploratory Data Analysis Questions", "<span class=\"section-number\">10.2. </span>Exploring Our Data Set", "<span class=\"section-number\">10.6. </span>Glossary", "<span class=\"section-number\">10.1. </span>Introduction", "<span class=\"section-number\">10.4. </span>Market Basket Analysis", "<span class=\"section-number\">10. </span>Exploring Market Basket Analysis", "<span class=\"section-number\">10.5. </span>Visualizing Data", "<span class=\"section-number\">1.2. </span>Glossary", "<span class=\"section-number\">1.1. </span>Introduction", "<span class=\"section-number\">1. </span>Introduction", "<span class=\"section-number\">5.4. </span>Filtering the Data", "<span class=\"section-number\">5.7. </span>Glossary", "<span class=\"section-number\">5.5. </span>Numbers as Indices", "<span class=\"section-number\">5.1. </span>Introduction", "<span class=\"section-number\">5.2. </span>Jupyter Notebooks", "<span class=\"section-number\">5.6. </span>Dealing with Multiple DataFrames", "<span class=\"section-number\">5.3. </span>Pandas Exercises", "<span class=\"section-number\">5. </span>Learning Pandas with Movie Data", "<span class=\"section-number\">9.7. </span>Aggregation or Group By", "<span class=\"section-number\">9.4. </span>Getting Started with the Bike Data", "<span class=\"section-number\">9.2. </span>Predicting Bike Rentals", "<span class=\"section-number\">9.10. </span>Mapping Bike Stations Using Colab", "<span class=\"section-number\">9.5. </span>Filtering", "<span class=\"section-number\">9.15. </span>Glossary", "<span class=\"section-number\">9.1. </span>Introduction", "<span class=\"section-number\">9.3. </span>Exploring Bike Rental Data with SQL", "<span class=\"section-number\">9.8. </span>Joining", "<span class=\"section-number\">9.13. </span>Improving our Pizza Price Predictions", "<span class=\"section-number\">9.9. </span>Getting SQL Data into a DataFrame", "<span class=\"section-number\">9.12. </span>Predicting Pizza Prices - Linear Regression", "<span class=\"section-number\">9.14. </span>Predicting Daily Bike Rentals", "<span class=\"section-number\">9.6. </span>Sorting", "<span class=\"section-number\">9.11. </span>Visualizing Bike Share Data as a Time Series", "<span class=\"section-number\">9. </span>Predictive Analytics", "<span class=\"section-number\">4.4. </span>Installing Anaconda", "<span class=\"section-number\">4.7. </span>Glossary", "<span class=\"section-number\">4.5. </span>Using Google Colaboratory Notebooks", "<span class=\"section-number\">4.3. </span>Jupyter Notebooks", "<span class=\"section-number\">4.1. </span>Introduction", "<span class=\"section-number\">4.6. </span>Markdown Cells", "Python", "<span class=\"section-number\">4.2. </span>Python Review", "Python Review", "Python Review", "<span class=\"section-number\">4. </span>Python and Jupyter Notebooks", "<span class=\"section-number\">3.4. </span>Optimizing Your Class Schedule", "<span class=\"section-number\">3.5. </span>Glossary", "<span class=\"section-number\">3.1. </span>Introduction", "<span class=\"section-number\">3.3. </span>Small Problems To Try", "<span class=\"section-number\">3.2. </span>Optimization Using Solver", "<span class=\"section-number\">3. </span>Optimization", "<span class=\"section-number\">2.5. </span>Challenge in Case Study 1: Calculating a Correlation Matrix", "<span class=\"section-number\">2.2. </span>Case Study 1: The Happiness Report", "<span class=\"section-number\">2.3. </span>Case Study 1: Adding More Happiness Data", "<span class=\"section-number\">2.4. </span>Case Study 1: Comparing Happiness Data across Years", "<span class=\"section-number\">2.8. </span>Case Study 2: How is Business Over Time?", "<span class=\"section-number\">2.9. </span>Case Study 2: Calculating a Correlation Matrix for Business Data", "<span class=\"section-number\">2.6. </span>Case Study 2: Considering Starting a Business?", "<span class=\"section-number\">2.7. </span>Case Study 2: Where Should We Start Our New Business?", "<span class=\"section-number\">2.10. </span>Glossary", "<span class=\"section-number\">2.1. </span>Introduction", "<span class=\"section-number\">2. </span>Exploring the Data Science Pipeline via Descriptive Statistics", "<span class=\"section-number\">8.8. </span>Glossary", "<span class=\"section-number\">8.6. </span>Graphing Relationships Between Data", "<span class=\"section-number\">8.1. </span>Introduction", "<span class=\"section-number\">8.3. </span>Merging and Tidying Data", "<span class=\"section-number\">8.5. </span>Working with Text", "<span class=\"section-number\">8.7. </span>Sentiment Analysis", "<span class=\"section-number\">8.2. </span>Text Mining", "<span class=\"section-number\">8.4. </span>Tidying Text: Most and Least Common Words", "<span class=\"section-number\">8. </span>Textual Analysis", "<span class=\"section-number\">6.2. </span>Case Study 1: Exploratory Data Analysis", "<span class=\"section-number\">6.5. </span>Case Study 1: Comparing Forms of Government", "<span class=\"section-number\">6.3. </span>Case Study 1: Graphing Infant Mortality on a Map", "<span class=\"section-number\">6.4. </span>Case Study 1: Screen Scraping the CIA", "Case Study 1: Summarzing Data Using Pandas Pivot Table", "<span class=\"section-number\">6.9. </span>Case Study 2: Comparing CIA Government Forms", "<span class=\"section-number\">6.6. </span>Case Study 2: Analyzing Protecting Minority Investors", "<span class=\"section-number\">6.7. </span>Case Study 2: Graphing Business Data on a Map", "<span 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25, 32, 65, 68, 73, 84, 86], "explicitli": [1, 13, 76], "proce": [1, 33, 83], "quit": [1, 13, 17, 32, 57, 58, 68, 73, 78, 89], "finish": [1, 78], "respons": [1, 2, 57, 73, 78], "next": [1, 9, 11, 17, 26, 33, 34, 35, 38, 45, 46, 47, 48, 54, 56, 58, 59, 60, 63, 68, 78, 79, 82, 83, 84, 88], "robot": 1, "txt": [1, 46, 47, 48], "tell": [1, 6, 7, 9, 13, 23, 28, 32, 35, 41, 45, 46, 47, 54, 57, 58, 62, 63, 68, 73, 78, 79, 83, 84], "autom": 1, "program": [1, 13, 16, 19, 28, 41, 42, 43, 44, 45, 46, 47, 48, 52, 68, 72, 73, 78, 85], "like": [1, 8, 9, 11, 13, 15, 17, 19, 20, 23, 24, 26, 28, 30, 31, 32, 33, 34, 35, 37, 41, 42, 44, 45, 46, 47, 48, 53, 54, 57, 58, 59, 60, 61, 63, 67, 68, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 83, 84, 89], "crawler": 1, "page": [1, 13, 21, 32, 41, 44, 77, 78, 79, 80, 81, 83, 84], "top": [1, 9, 11, 19, 23, 26, 32, 33, 36, 41, 53, 57, 62, 76, 78, 79, 82, 84], "level": [1, 34, 37, 45, 46, 47, 48, 57, 62, 67, 71, 73, 78, 79, 84, 89], "domain": [1, 13, 73], "For": [1, 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35, 37, 45, 46, 47, 58, 62, 63, 64, 67, 70, 73, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86], "url": [1, 21, 28, 33, 41, 78, 79, 83, 84], "http": [1, 4, 6, 11, 17, 20, 21, 25, 26, 33, 35, 73, 78, 79, 83, 84], "www": [1, 4, 6], "user": [1, 5, 9, 11, 13, 21, 23, 25, 41, 42, 45, 58, 64, 70, 74, 79, 84, 86], "agent": [1, 53], "disallow": 1, "too": [1, 13, 17, 35, 37, 45, 48, 54, 73, 76], "repeat": [1, 13, 34, 40, 45, 46, 47, 48, 62, 64, 68, 76, 79, 82], "hit": 1, "quick": [1, 13, 33, 46, 68, 76, 83], "litefind": 1, "yahoo": 1, "slurp": 1, "baidu": 1, "line": [1, 17, 19, 24, 28, 32, 34, 35, 37, 40, 41, 46, 47, 48, 52, 67, 68, 70, 72, 73, 76, 79, 82, 84, 89], "sai": [1, 9, 27, 32, 34, 35, 41, 46, 47, 48, 56, 70, 73, 77, 80, 82], "gener": [1, 13, 19, 34, 35, 41, 56, 57, 67, 68, 72, 73, 74, 79], "read": [1, 3, 10, 13, 16, 19, 21, 24, 25, 28, 33, 35, 37, 41, 42, 46, 47, 48, 55, 57, 58, 66, 67, 68, 70, 71, 75, 76, 77, 79, 80, 81, 82, 84, 87, 88, 89], "howev": [1, 13, 17, 19, 23, 32, 35, 41, 43, 45, 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35, 58, 68, 73, 76, 79], "text": [2, 14, 16, 22, 24, 41, 43, 44, 45, 64, 67, 68, 69, 70, 72, 75, 76, 78, 79, 83, 84, 85, 88, 89], "pictur": [2, 52, 84], "social": [2, 30, 50, 57, 73], "media": [2, 79, 84], "upload": [2, 41], "interact": [2, 30, 37, 42, 45, 68, 70, 76, 82], "reflect": [2, 54, 68, 73], "who": [2, 13, 17, 21, 35, 39, 42, 46, 57, 70, 73, 83], "incredibli": 2, "pattern": [2, 8, 60, 68, 73, 79, 84, 85], "inher": 2, "both": [2, 9, 13, 20, 25, 26, 31, 32, 34, 35, 41, 42, 45, 46, 47, 57, 60, 62, 68, 70, 72, 73, 74, 76, 78, 89], "peopl": [2, 5, 13, 20, 21, 32, 39, 52, 54, 57, 59, 68, 71, 72, 73, 74, 76, 79, 84], "rest": [2, 13, 37, 70, 78, 83], "world": [2, 13, 21, 30, 34, 35, 46, 48, 54, 57, 58, 62, 63, 68, 70, 72, 73, 74, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89], "To": [2, 9, 11, 21, 23, 24, 25, 31, 32, 33, 34, 35, 36, 41, 44, 46, 47, 48, 54, 55, 56, 57, 58, 60, 61, 62, 63, 68, 70, 71, 72, 73, 76, 79, 80, 82, 83, 88], "ensur": [2, 31, 33, 35, 73], "scientist": [2, 14, 18, 34, 35, 41, 42, 43, 58, 68, 78, 79, 89], "oblig": [2, 73], "themselv": [2, 68, 73], "question": [2, 9, 10, 13, 20, 22, 27, 31, 35, 40, 45, 46, 47, 52, 54, 56, 58, 63, 66, 74, 75, 79, 87, 88, 89], "greater": [2, 20, 45, 46, 47, 57, 62, 73], "good": [2, 3, 9, 11, 13, 33, 34, 35, 39, 45, 46, 47, 54, 56, 58, 59, 68, 70, 71, 72, 73, 77, 78, 79, 80, 84, 88], "invad": 2, "privaci": 2, "my": [2, 17, 20, 70, 73], "code": [2, 13, 16, 22, 24, 26, 33, 34, 37, 40, 42, 43, 44, 45, 46, 47, 48, 49, 58, 63, 67, 68, 70, 71, 73, 76, 79, 82, 84, 87, 88], "conduct": [2, 34], "being": [2, 13, 26, 31, 32, 42, 44, 46, 47, 48, 52, 57, 70, 73, 79], "each": [2, 5, 6, 7, 9, 11, 13, 17, 19, 20, 21, 23, 24, 26, 28, 31, 32, 33, 34, 35, 37, 40, 41, 42, 45, 46, 47, 48, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 77, 78, 79, 80, 81, 83, 84, 85, 89], "time": [2, 9, 11, 12, 13, 17, 19, 24, 26, 29, 31, 33, 34, 35, 38, 39, 40, 41, 45, 46, 47, 48, 52, 53, 54, 57, 58, 62, 63, 66, 68, 70, 71, 72, 73, 74, 76, 78, 79, 83, 84, 88, 89], "short": [2, 17, 35, 45], "imper": [2, 73], "implic": [2, 17], "must": [2, 23, 41, 43, 45, 46, 47, 50, 51, 54, 58, 62, 67, 70, 73, 74, 78, 79, 83], "merg": [2, 11, 30, 69, 75, 78, 83, 88], "multipl": [2, 13, 18, 19, 22, 23, 24, 27, 29, 31, 40, 41, 42, 45, 46, 47, 69, 70, 73, 82, 88], "sourc": [2, 3, 9, 13, 19, 29, 30, 35, 41, 42, 66, 67, 68, 73, 83, 84, 86, 88, 89], "Will": [2, 28, 64], "same": [2, 9, 13, 16, 17, 23, 25, 26, 28, 31, 32, 34, 35, 36, 40, 44, 45, 46, 47, 48, 54, 57, 58, 59, 60, 62, 63, 65, 68, 70, 71, 73, 78, 79, 83, 84], "merger": 2, "reveal": [2, 9, 85], "could": [2, 9, 13, 19, 23, 24, 26, 27, 31, 32, 33, 34, 35, 37, 41, 53, 54, 57, 65, 68, 70, 71, 73, 79, 82, 89], "harm": 2, "involv": [2, 21, 67, 73], "screen": [2, 3, 35, 43, 77, 80, 84, 85, 86, 87, 88, 89], "scrape": [2, 3, 13, 35, 43, 73, 77, 80, 81, 85, 86, 87, 88, 89], "legal": [2, 13, 62, 88], "misrepres": 2, "ten": [2, 24, 73], "simpl": [2, 9, 13, 24, 32, 35, 38, 40, 46, 47, 48, 58, 62, 73, 76, 78, 79, 82, 84, 85, 88], "rule": [2, 35, 45, 46, 47, 51, 54, 56, 61, 67, 73, 79], "big": [2, 6, 9, 13, 31, 32, 35, 41, 52, 72, 78, 79], "suspect": [2, 20, 70], "A": [2, 9, 12, 13, 15, 16, 21, 26, 28, 30, 32, 33, 35, 37, 38, 40, 42, 45, 46, 47, 51, 53, 54, 56, 57, 58, 59, 60, 62, 63, 64, 68, 70, 73, 78, 79, 83, 85, 86, 88], "lone": 2, "whiz": 2, "fight": [2, 21], "airbnb": 2, "win": 2, "introduct": [3, 10, 22, 35, 38, 46, 47, 49, 55, 66, 72, 75, 82, 87, 88, 89], "object": [3, 9, 10, 14, 15, 16, 17, 21, 22, 26, 30, 33, 34, 37, 38, 45, 46, 47, 49, 51, 53, 54, 55, 66, 67, 68, 70, 71, 73, 75, 76, 78, 79, 82, 84, 85, 87, 88], "list": [3, 9, 10, 15, 16, 17, 21, 23, 24, 26, 30, 31, 33, 37, 38, 40, 42, 49, 55, 56, 57, 58, 61, 62, 63, 66, 67, 68, 70, 71, 74, 75, 76, 77, 78, 80, 81, 82, 83, 87, 88, 89], "cheater": [3, 88], "glossari": [3, 10, 14, 22, 38, 49, 55, 66, 75, 87, 88, 89], "pymvpa": 4, "datadb": 4, "mnist": 4, "html": [4, 9, 16, 32, 42, 79, 84, 85], "yann": 4, 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"item": [5, 6, 7, 8, 10, 13, 40, 45, 46, 47, 48, 54, 70, 73, 79, 84, 88], "add": [5, 6, 9, 13, 19, 20, 23, 25, 26, 29, 32, 33, 34, 35, 36, 41, 42, 46, 47, 48, 54, 56, 58, 59, 61, 62, 63, 68, 70, 71, 72, 73, 76, 77, 78, 79, 80, 83, 84, 89], "cart": [5, 6, 9], "chang": [5, 13, 19, 30, 34, 40, 41, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 60, 61, 62, 63, 68, 71, 73, 77, 79, 80, 81, 83, 84, 89], "dai": [5, 6, 13, 17, 25, 26, 28, 33, 35, 37, 46, 47, 48, 54, 57, 62, 73], "week": [5, 6, 13, 25, 26, 33, 35, 37, 73], "shop": [5, 6, 8, 9, 54], "checkout": [5, 21], "second": [5, 24, 26, 31, 33, 34, 35, 36, 39, 40, 41, 45, 58, 63, 68, 70, 73, 78, 79, 86, 88], "depart": [5, 9, 10, 11, 88, 89], "aisl": [5, 9, 10, 11, 88], "frequent": [5, 7, 9, 13, 31, 52, 68], "last": [5, 9, 13, 15, 17, 23, 25, 27, 28, 32, 33, 35, 41, 45, 46, 47, 57, 58, 68, 73, 79, 84], "creat": [5, 8, 9, 11, 13, 15, 16, 17, 18, 19, 20, 21, 23, 26, 28, 29, 32, 33, 35, 37, 39, 40, 41, 42, 44, 46, 47, 48, 52, 53, 57, 58, 59, 60, 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"numbers-as-indices"]], "Practice Questions": [[17, "practice-questions"], [76, "practice-questions"], [82, "practice-questions"]], "Jupyter Notebooks": [[19, "jupyter-notebooks"], [42, "jupyter-notebooks"]], "Text Cells": [[19, "text-cells"], [42, "text-cells"]], "Code Cells": [[19, "code-cells"]], "Dealing with Multiple DataFrames": [[20, "dealing-with-multiple-dataframes"]], "Pandas Exercises": [[21, "pandas-exercises"]], "Exploring the Data": [[21, "exploring-the-data"]], "Learning Pandas with Movie Data": [[22, "learning-pandas-with-movie-data"]], "Learning Pandas": [[22, null]], "Aggregation or Group By": [[23, "aggregation-or-group-by"]], "Practice Exercises": [[23, "practice-exercises"], [27, "practice-exercises"], [31, "practice-exercises"], [36, "practice-exercises"]], "Getting Started with the Bike Data": [[24, "getting-started-with-the-bike-data"]], "Verify access to the dataset": [[24, "verify-access-to-the-dataset"]], "Predicting Bike Rentals": [[25, "predicting-bike-rentals"]], "Mapping Bike Stations Using Colab": [[26, "mapping-bike-stations-using-colab"]], "Practice and Further Exploration": [[26, "practice-and-further-exploration"], [33, "practice-and-further-exploration"]], "Filtering": [[27, "filtering"]], "Exploring Bike Rental Data with SQL": [[30, "exploring-bike-rental-data-with-sql"]], "Query Language": [[30, "query-language"]], "Joining": [[31, "joining"]], "Improving our Pizza Price Predictions": [[32, "improving-our-pizza-price-predictions"]], "Introducing Scikit-Learn": [[32, "introducing-scikit-learn"]], "Practice": [[32, "practice"], [74, "practice"], [76, "practice"]], "Getting SQL Data into a DataFrame": [[33, "getting-sql-data-into-a-dataframe"]], "Mapping Bike Stations Using Anaconda": [[33, "mapping-bike-stations-using-anaconda"]], "Predicting Pizza Prices - Linear Regression": [[34, "predicting-pizza-prices-linear-regression"]], "Closed-Form Solution": [[34, "closed-form-solution"]], "The Payoff - Supervised Learning": [[34, "the-payoff-supervised-learning"]], "A Simple Machine Learning Approach (Optional)": [[34, "a-simple-machine-learning-approach-optional"]], "Predicting Daily Bike Rentals": [[35, "predicting-daily-bike-rentals"]], "The Train-Test Split": [[35, "the-train-test-split"]], "Evaluating the Model": [[35, "evaluating-the-model"]], "Feature Engineering": [[35, "feature-engineering"]], "Version 1.0 Task List": [[35, "version-1-0-task-list"]], "Refining the Model": [[35, "refining-the-model"]], "Version 2.0 Task List": [[35, "version-2-0-task-list"]], "Version 2.5 Task List": [[35, "version-2-5-task-list"]], "Version 3.0": [[35, "version-3-0"]], "Feature Engineering - Re-Scaling": [[35, "feature-engineering-re-scaling"]], "Where to go from here?": [[35, "where-to-go-from-here"]], "Sorting": [[36, "sorting"]], "Visualizing Bike Share Data as a Time Series": [[37, "visualizing-bike-share-data-as-a-time-series"]], "Indexing with a DatetimeIndex": [[37, "indexing-with-a-datetimeindex"]], "Next Step: Predicting Rentals": [[37, "next-step-predicting-rentals"]], "Working with ZIP Files (Optional)": [[37, "working-with-zip-files-optional"]], "Predictive Analytics": [[38, "predictive-analytics"]], "Table of Contents": [[38, null], [88, "table-of-contents"]], "Installing Anaconda": [[39, "installing-anaconda"]], "Starting Jupyter Lab": [[39, "starting-jupyter-lab"]], "Using Google Colaboratory Notebooks": [[41, "using-google-colaboratory-notebooks"]], "Getting Google Colab Set Up": [[41, "getting-google-colab-set-up"]], "The UI": [[41, "the-ui"]], "Colab Code Cells": [[41, "colab-code-cells"]], "Notebook Functionalities": [[42, "notebook-functionalities"]], "Using Jupyter Notebooks": [[42, "using-jupyter-notebooks"]], "Markdown Cells": [[44, "markdown-cells"]], "Python": [[45, "python"]], "Data types": [[45, "data-types"]], "Floating point numbers are numbers that are broken down into smaller parts by decimal points.": [[45, "floating-point-numbers-are-numbers-that-are-broken-down-into-smaller-parts-by-decimal-points"]], "Table 1: Relational and Logical Operators": [[45, "id5"], [46, "id1"], [47, "id2"]], "lists": [[45, "lists"]], "you can add or remove members from the list.": [[45, "you-can-add-or-remove-members-from-the-list"]], "Table 2: Operations on Any Sequence in Python": [[45, "id6"], [46, "id2"], [47, "id3"]], "Table 3: Methods Provided by Lists in Python": [[45, "id7"]], "Immutable means that they can not be altered once they are created.": [[45, "immutable-means-that-they-can-not-be-altered-once-they-are-created"]], "The location of the value is in your computer\u2019s memory.": [[45, "the-location-of-the-value-is-in-your-computer-s-memory"]], "Table 4: Methods Provided by Strings in Python": [[45, "id8"]], "Python Review": [[46, "python-review"], [47, "python-review"], [48, "python-review"]], "Variables": [[46, "variables"], [47, "variables"]], "Numeric Data Types": [[46, "numeric-data-types"], [47, "numeric-data-types"]], "Booleans": [[46, "booleans"], [47, "booleans"]], "Strings": [[46, "strings"], [47, "strings"]], "Table 3: Operations on Any String in Python": [[46, "id3"], [47, "id4"]], "Conditional Statements": [[46, "conditional-statements"], [47, "conditional-statements"]], "Lists": [[46, "lists"], [47, "lists"]], "Table 4: Operations on Any List in Python": [[46, "id4"], [47, "id5"]], "Range": [[46, "range"], [47, "range"]], "For Loops": [[46, "for-loops"]], "Dictionaries": [[46, "dictionaries"], [47, "dictionaries"]], "Functions": [[46, "functions"], [47, "functions"]], "Map Functions": [[46, "map-functions"]], "Lambda Functions": [[46, "lambda-functions"], [47, "lambda-functions"]], "List Comprehensions": [[46, "list-comprehensions"], [47, "list-comprehensions"]], "Some Additional Important Python Knowledge": [[46, "some-additional-important-python-knowledge"], [47, "some-additional-important-python-knowledge"]], "Opening Files": [[46, "opening-files"], [47, "opening-files"]], "Random Number Generators": [[46, "random-number-generators"], [47, "random-number-generators"]], "For loops": [[47, "for-loops"]], "The map function": [[47, "the-map-function"]], "List Comprehension Exercises": [[48, "list-comprehension-exercises"]], "Python and Jupyter Notebooks": [[49, "python-and-jupyter-notebooks"], [49, null]], "Optimizing Your Class Schedule": [[50, "optimizing-your-class-schedule"]], "Small Problems To Try": [[53, "small-problems-to-try"]], "Optimization Using Solver": [[54, "optimization-using-solver"]], "Optimization": [[55, "optimization"]], "Using Solver": [[55, null]], "Challenge in Case Study 1: Calculating a Correlation Matrix": [[56, "challenge-in-case-study-1-calculating-a-correlation-matrix"]], "Case Study 1: The Happiness Report": [[57, "case-study-1-the-happiness-report"]], "Introducing the Happiness Report": [[57, "introducing-the-happiness-report"]], "Happiness Index Research Questions": [[57, "happiness-index-research-questions"]], "Summary Statistics": [[57, "summary-statistics"], [65, "summary-statistics"]], "Visualizing Happiness": [[57, "visualizing-happiness"]], "Case Study 1: Adding More Happiness Data": [[58, "case-study-1-adding-more-happiness-data"]], "Happiness by Region": [[58, "happiness-by-region"]], "Joining Data from Other Sources": [[58, "joining-data-from-other-sources"]], "Introducing Pivot Tables": [[58, "introducing-pivot-tables"]], "Case Study 1: Comparing Happiness Data across Years": [[59, "case-study-1-comparing-happiness-data-across-years"]], "Case Study 2: How is Business Over Time?": [[60, "case-study-2-how-is-business-over-time"]], "Case Study 2: Calculating a Correlation Matrix for Business Data": [[61, "case-study-2-calculating-a-correlation-matrix-for-business-data"]], "Case Study 2: Considering Starting a Business?": [[62, "case-study-2-considering-starting-a-business"]], "Thinking About Starting Your Business": [[62, "thinking-about-starting-your-business"]], "Business Start-Up Data Analysis Research Questions": [[62, "business-start-up-data-analysis-research-questions"]], "Visualizing How to Start a Business": [[62, "visualizing-how-to-start-a-business"]], "Case Study 2: Where Should We Start Our New Business?": [[63, "case-study-2-where-should-we-start-our-new-business"]], "Business Score by Region": [[63, "business-score-by-region"]], "Joining Data from Other Sources When Considering Starting a New Business": [[63, "joining-data-from-other-sources-when-considering-starting-a-new-business"]], "Summarizing Key Business Data Using Pivot Table": [[63, "summarizing-key-business-data-using-pivot-table"]], "Time Required": [[65, "time-required"]], "Exploring the Data Science Pipeline via Descriptive Statistics": [[66, "exploring-the-data-science-pipeline-via-descriptive-statistics"]], "Descriptive Statistics": [[66, null]], "Defintions": [[67, "defintions"]], "Graphing Relationships Between Data": [[68, "graphing-relationships-between-data"]], "Preparing the Data": [[68, "preparing-the-data"]], "Visualizing the Relationships with a Heatmap": [[68, "visualizing-the-relationships-with-a-heatmap"]], "Visualizing the Relationships with a Graph": [[68, "visualizing-the-relationships-with-a-graph"]], "Projects for Further Exploration": [[68, "projects-for-further-exploration"]], "Merging and Tidying Data": [[70, "merging-and-tidying-data"]], "Tidy Data": [[70, "tidy-data"]], "Tidying the Movie Genres": [[70, "tidying-the-movie-genres"]], "Working with Text": [[71, "working-with-text"]], "Research Questions": [[71, "research-questions"]], "Text Complexity": [[71, "text-complexity"]], "Sentiment Analysis": [[72, "sentiment-analysis"]], "Naive Bayes": [[72, "naive-bayes"]], "Using NLTK to Score the Speeches": [[72, "using-nltk-to-score-the-speeches"]], "Questions for Investigation": [[72, "questions-for-investigation"]], "Text Mining": [[73, "text-mining"]], "Exploratory Questions": [[73, "exploratory-questions"]], "OH NO, what the heck!!": [[73, "oh-no-what-the-heck"]], "Tidying Text: Most and Least Common Words": [[74, "tidying-text-most-and-least-common-words"]], "Textual Analysis": [[75, "textual-analysis"]], "Case Study 1: Exploratory Data Analysis": [[76, "case-study-1-exploratory-data-analysis"]], "Loading Data into a DataFrame from a CSV File": [[76, "loading-data-into-a-dataframe-from-a-csv-file"]], "Describing the Data": [[76, "describing-the-data"]], "Visualizing Distribution with Histograms": [[76, "visualizing-distribution-with-histograms"]], "Scatter Plots for Discovering Relationships": [[76, "scatter-plots-for-discovering-relationships"]], "Power Tools: Scatter Matrix": [[76, "power-tools-scatter-matrix"]], "Developing Fluency": [[76, "developing-fluency"]], "Case Study 1: Comparing Forms of Government": [[77, "case-study-1-comparing-forms-of-government"]], "Project": [[77, "project"], [80, "project"], [81, "project"]], "Case Study 1: Graphing Infant Mortality on a Map": [[78, "case-study-1-graphing-infant-mortality-on-a-map"]], "Using a Web API to get Country Codes": [[78, "using-a-web-api-to-get-country-codes"]], "Using a Web API on Your Own": [[78, "using-a-web-api-on-your-own"]], "Case Study 1: Screen Scraping the CIA": [[79, "case-study-1-screen-scraping-the-cia"]], "Getting a List of All Fields": [[79, "getting-a-list-of-all-fields"]], "Loading All the Data in Rough Form": [[79, "loading-all-the-data-in-rough-form"]], "Cleaning the Data": [[79, "cleaning-the-data"]], "Saving the Data": [[79, "saving-the-data"]], "Comparing Across the Years": [[79, "comparing-across-the-years"]], "Case Study 1: Summarzing Data Using Pandas Pivot Table": [[80, "case-study-1-summarzing-data-using-pandas-pivot-table"]], "Case Study 2: Comparing CIA Government Forms": [[81, "case-study-2-comparing-cia-government-forms"]], "Case Study 2: Analyzing Protecting Minority Investors": [[82, "case-study-2-analyzing-protecting-minority-investors"]], "Loading Minority Investors Data into a DataFrame": [[82, "loading-minority-investors-data-into-a-dataframe"]], "Describing Minority Investors": [[82, "describing-minority-investors"]], "Visualizing Minority Investors with a Histogram": [[82, "visualizing-minority-investors-with-a-histogram"]], "Discovering Relationships with Scatter Plots": [[82, "discovering-relationships-with-scatter-plots"]], "Minority Investors: Scatter Matrix": [[82, "minority-investors-scatter-matrix"]], "Case Study 2: Graphing Business Data on a Map": [[83, "case-study-2-graphing-business-data-on-a-map"]], "Getting Country Codes from a Web API": [[83, "getting-country-codes-from-a-web-api"]], "Case Study 2: Scraping Business Data Using Panda and BeautifulSoup": [[84, "case-study-2-scraping-business-data-using-panda-and-beautifulsoup"]], "Getting a List of Business Fields": [[84, "getting-a-list-of-business-fields"]], "Loading Business Data in Rough Form": [[84, "loading-business-data-in-rough-form"]], "Cleaning Business Data": [[84, "cleaning-business-data"]], "Saving the Business Data": [[84, "saving-the-business-data"]], "Comparing Business Data Across the Years": [[84, "comparing-business-data-across-the-years"]], "Exploratory Data Analysis": [[87, "exploratory-data-analysis"]], "How to Think like a Data Scientist": [[88, "how-to-think-like-a-data-scientist"]], "Preface": [[89, "preface"]], "Preface from the Second Edition": [[89, "preface-from-the-second-edition"]], "Preface from the First Edition": [[89, "preface-from-the-first-edition"]], "More Traditional Topic Outline": [[89, "more-traditional-topic-outline"]]}, "indexentries": {}})