![]() Why Heuristics are Essential for an AI System.Heuristics as Information in the Making.Chapter 11 The Role of Heuristics in Data Science.Other Considerations for Choosing the Right Model.Combining Different Options in an Ensemble Setting.Choosing the Right Model for a Classification Methodology.Evaluating the Data at Hand and Pairing It with a Model.Preventing Erroneous Situations in the Pipeline.Strategies for Coping with High-level Mistakes.Chapter 9 Mistakes Through the Data Science Process.Some Useful Considerations on Programming Bugs.The Importance of Understanding and Dealing with Programming Bugs.Some Useful Considerations on Sensitivity Analysis.Local Sensitivity Analysis Employing “What If?” Questions. ![]() Global Sensitivity Analysis Using Resampling Methods.Chapter 7 Sensitivity Analysis of Experiment Conclusions.Evaluating the Results of an Experiment.Experiments for Assessing the Performance of a Predictive Analytics System.Chapter 6 Data Science Experiments and Evaluation of Their Results.Should We Remove X from the Feature Set?.Do Features X and Y Collaborate Well with Each Other for Predicting Variable Z?.Is Subset X Significantly Different to Subset Y?.Questions Related to Most Common Use Cases.Importance of Asking (the Right) Questions.Chapter 5 Data Science Questions and Hypotheses.Part 2 Setting the Stage for Data Analytics.The Most Useful Packages for Julia and Python.Non-negative Matrix Factorization (NMF or NNMF).Insight, Deliverance, and Visualization.The Ever-growing Need for Data Science Professionals.What Does a Data Scientist Actually Do?.The Need for Data Scientists and the Products/Services Provided.AI – The Scientific Field, Not the Sci-fi Movie!.Part 1 Overview of Data Science and the Data Scientist’s Work.Chapter 4 The Data Scientist’s Toolbox by Zacharias Voulgaris PhD Data Science: Mindset, Methodologies, and Misconceptions
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