10/25/2023 0 Comments Data dredging eda![]() ![]() This exploration involves transforming, visualizing, and summarizing data to build and confirm our understanding, identify and address potential issues with the data, and inform subsequent analysis.ĮDA is fun! But it takes practice. ![]() We use plots to uncover features of the data, examine distributions of values, and reveal relationships that cannot be detected from simple numerical summaries. In EDA, we enter a process of discovery, continually asking questions and diving into uncharted territory to explore ideas. Indeed, the work described in Chapter 9 to clean and transform the data relied heavily on EDA to guide our quality checks and transformations. Convex and Differentiable Loss FunctionsĮxploratory data analysis is actively incisive, rather than passively descriptive, with real emphasis on the discovery of the unexpected.Īs a data scientist, you will want to use EDA in every stage of the data lifecycle from checking the quality of your data to preparing for formal modeling to confirming that your model is reasonable. From Probabilities to ClassificationĢ0.3. A Loss Function for the Logistic Modelġ9.5. Modeling Proportions (and Probabilities)ġ9.4. Probability for Inference and Predictionġ9.3. Distributions: Population, Empirical, Samplingġ7.6. Feature Engineering for Categorical Measurementsġ7.1. Feature Engineering for Numeric Measurementsġ5.8. ![]() Example: Where Is the Land of Opportunity?ġ5.7. Example: A Simple Linear Model for Air Qualityġ5.6. Creating a Model to Correct PurpleAir Measurementsġ5.2. Exploring PurpleAir and AQS Measurementsġ2.6. Wrangling and Cleaning AQS Sensor Dataġ2.5. Case Study: How Accurate Are Air Quality Measurements?ġ2.3. Comparisons in Multivariate Settingsġ1.3. Example: Wrangling Restaurant Safety Violationsġ0.4. Measurements from the Mauna Loa Observatoryĩ.6. Transforming and Common Table Expressions How Are Dataframes Different from Other Data Representations?ħ.4. Case Study: Why Is My Bus Always Late?Ħ.5. Example: Simulating a Randomized Trial for a Vaccineĥ. Example: Simulating Election Poll Bias and Varianceģ.3. Target Population, Access Frame, and Sampleģ.2. ![]()
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