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 [10] 9. Acting on Data Science

  [1] Interpretability.srt

  [2] Actionable insights.srt

 [11] Conclusion

  [1] Next steps.srt

 [1] Introduction

  [1] The fundamentals of data science.srt

 [2] 1. What Is Data Science

  [1] Supply and demand for data science.srt

  [2] The data science Venn diagram.srt

  [3] The data science pathway.srt

  [4] Roles and teams in data science.srt

 [3] 2. The Place of Data Science in the Data Universe

  [1] Artificial intelligence.srt

  [2] Machine learning.srt

  [3] Deep learning neural networks.srt

  [4] Big data.srt

  [5] Predictive analytics.srt

  [6] Prescriptive analytics.srt

  [7] Business intelligence.srt

 [4] 3. Ethics and Agency

  [1] Legal, ethical, and social issues of data science.srt

  [2] Agency of algorithms and decision-makers.srt

 [5] 4. Sources of Data

  [1] Data preparation.srt

  [2] In-house data.srt

  [3] Open data.srt

  [4] APIs.srt

  [5] Scraping data.srt

  [6] Creating data.srt

  [7] Passive collection of training data.srt

  [8] Self-generated data.srt

 [6] 5. Sources of Rules

  [1] The enumeration of explicit rules.srt

  [2] The derivation of rules from data analysis.srt

  [3] The generation of implicit rules.srt

 [7] 6. Tools for Data Science

  [1] Applications for data analysis.srt

  [2] Languages for data science.srt

  [3] Machine learning as a service.srt

 [8] 7. Mathematics for Data Science

  [1] Algebra.srt

  [2] Calculus.srt

  [3] Optimization and the combinatorial explosion.srt

  [4] Bayes' theorem.srt

 [9] 8. Analyses for Data Science

  [10] Aggregating models.srt

  [1] Descriptive analyses.srt

  [2] Predictive models.srt

  [3] Trend analysis.srt

  [4] Clustering.srt

  [5] Classifying.srt

  [6] Anomaly detection.srt

  [7] Dimensionality reduction.srt

  [8] Feature selection and creation.srt

  [9] Validating models.srt

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