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LinkedIn Learning - Data Science Foundations Fundamentals

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视频 2023-1-23 21:44 2024-11-24 12:55 142 703.09 MB 45
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LinkedIn Learning - Data Science Foundations Fundamentals

文件列表
  1. [10] 9. Acting on Data Science/[1] Interpretability.mp49.3MB
  2. [10] 9. Acting on Data Science/[2] Actionable insights.mp48.24MB
  3. [11] Conclusion/[1] Next steps.mp49.54MB
  4. [1] Introduction/[1] The fundamentals of data science.mp411.81MB
  5. [2] 1. What Is Data Science/[1] Supply and demand for data science.mp48.28MB
  6. [2] 1. What Is Data Science/[2] The data science Venn diagram.mp48.64MB
  7. [2] 1. What Is Data Science/[3] The data science pathway.mp423.22MB
  8. [2] 1. What Is Data Science/[4] Roles and teams in data science.mp414.03MB
  9. [3] 2. The Place of Data Science in the Data Universe/[1] Artificial intelligence.mp429.55MB
  10. [3] 2. The Place of Data Science in the Data Universe/[2] Machine learning.mp429.96MB
  11. [3] 2. The Place of Data Science in the Data Universe/[3] Deep learning neural networks.mp430.53MB
  12. [3] 2. The Place of Data Science in the Data Universe/[4] Big data.mp414.91MB
  13. [3] 2. The Place of Data Science in the Data Universe/[5] Predictive analytics.mp412.04MB
  14. [3] 2. The Place of Data Science in the Data Universe/[6] Prescriptive analytics.mp413.98MB
  15. [3] 2. The Place of Data Science in the Data Universe/[7] Business intelligence.mp411.08MB
  16. [4] 3. Ethics and Agency/[1] Legal, ethical, and social issues of data science.mp412.59MB
  17. [4] 3. Ethics and Agency/[2] Agency of algorithms and decision-makers.mp412.62MB
  18. [5] 4. Sources of Data/[1] Data preparation.mp415MB
  19. [5] 4. Sources of Data/[2] In-house data.mp46.05MB
  20. [5] 4. Sources of Data/[3] Open data.mp420.52MB
  21. [5] 4. Sources of Data/[4] APIs.mp410.55MB
  22. [5] 4. Sources of Data/[5] Scraping data.mp429.71MB
  23. [5] 4. Sources of Data/[6] Creating data.mp414.73MB
  24. [5] 4. Sources of Data/[7] Passive collection of training data.mp413.21MB
  25. [5] 4. Sources of Data/[8] Self-generated data.mp413.97MB
  26. [6] 5. Sources of Rules/[1] The enumeration of explicit rules.mp412.66MB
  27. [6] 5. Sources of Rules/[2] The derivation of rules from data analysis.mp423.83MB
  28. [6] 5. Sources of Rules/[3] The generation of implicit rules.mp49.18MB
  29. [7] 6. Tools for Data Science/[1] Applications for data analysis.mp415.15MB
  30. [7] 6. Tools for Data Science/[2] Languages for data science.mp412.86MB
  31. [7] 6. Tools for Data Science/[3] Machine learning as a service.mp411.26MB
  32. [8] 7. Mathematics for Data Science/[1] Algebra.mp414.83MB
  33. [8] 7. Mathematics for Data Science/[2] Calculus.mp48.66MB
  34. [8] 7. Mathematics for Data Science/[3] Optimization and the combinatorial explosion.mp413.91MB
  35. [8] 7. Mathematics for Data Science/[4] Bayes' theorem.mp410.58MB
  36. [9] 8. Analyses for Data Science/[10] Aggregating models.mp415.04MB
  37. [9] 8. Analyses for Data Science/[1] Descriptive analyses.mp423.19MB
  38. [9] 8. Analyses for Data Science/[2] Predictive models.mp424.51MB
  39. [9] 8. Analyses for Data Science/[3] Trend analysis.mp419.19MB
  40. [9] 8. Analyses for Data Science/[4] Clustering.mp418.41MB
  41. [9] 8. Analyses for Data Science/[5] Classifying.mp413.01MB
  42. [9] 8. Analyses for Data Science/[6] Anomaly detection.mp418.19MB
  43. [9] 8. Analyses for Data Science/[7] Dimensionality reduction.mp419.46MB
  44. [9] 8. Analyses for Data Science/[8] Feature selection and creation.mp416.9MB
  45. [9] 8. Analyses for Data Science/[9] Validating models.mp418.2MB
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