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种子名称： LinkedIn Learning - Data Science Foundations Fundamentals [CoursesGhar]
文件类型： 视频
文件数目： 45个文件
文件大小： 703.09 MB
收录时间： 2023-10-29 01:10
已经下载： 3次
资源热度： 77
最近下载： 2024-4-22 16:28

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LinkedIn Learning - Data Science Foundations Fundamentals [CoursesGhar].torrent

[10] 9. Acting on Data Science/[1] Interpretability.mp49.3MB

[10] 9. Acting on Data Science/[2] Actionable insights.mp48.24MB

[11] Conclusion/[1] Next steps.mp49.54MB

[1] Introduction/[1] The fundamentals of data science.mp411.81MB

[2] 1. What Is Data Science/[1] Supply and demand for data science.mp48.28MB

[2] 1. What Is Data Science/[2] The data science Venn diagram.mp48.64MB

[2] 1. What Is Data Science/[3] The data science pathway.mp423.22MB

[2] 1. What Is Data Science/[4] Roles and teams in data science.mp414.03MB

[3] 2. The Place of Data Science in the Data Universe/[1] Artificial intelligence.mp429.55MB

[3] 2. The Place of Data Science in the Data Universe/[2] Machine learning.mp429.96MB

[3] 2. The Place of Data Science in the Data Universe/[3] Deep learning neural networks.mp430.53MB

[3] 2. The Place of Data Science in the Data Universe/[4] Big data.mp414.91MB

[3] 2. The Place of Data Science in the Data Universe/[5] Predictive analytics.mp412.04MB

[3] 2. The Place of Data Science in the Data Universe/[6] Prescriptive analytics.mp413.98MB

[3] 2. The Place of Data Science in the Data Universe/[7] Business intelligence.mp411.08MB

[4] 3. Ethics and Agency/[1] Legal, ethical, and social issues of data science.mp412.59MB

[4] 3. Ethics and Agency/[2] Agency of algorithms and decision-makers.mp412.62MB

[5] 4. Sources of Data/[1] Data preparation.mp415MB

[5] 4. Sources of Data/[2] In-house data.mp46.05MB

[5] 4. Sources of Data/[3] Open data.mp420.52MB

[5] 4. Sources of Data/[4] APIs.mp410.55MB

[5] 4. Sources of Data/[5] Scraping data.mp429.71MB

[5] 4. Sources of Data/[6] Creating data.mp414.73MB

[5] 4. Sources of Data/[7] Passive collection of training data.mp413.21MB

[5] 4. Sources of Data/[8] Self-generated data.mp413.97MB

[6] 5. Sources of Rules/[1] The enumeration of explicit rules.mp412.66MB

[6] 5. Sources of Rules/[2] The derivation of rules from data analysis.mp423.83MB

[6] 5. Sources of Rules/[3] The generation of implicit rules.mp49.18MB

[7] 6. Tools for Data Science/[1] Applications for data analysis.mp415.15MB

[7] 6. Tools for Data Science/[2] Languages for data science.mp412.86MB

[7] 6. Tools for Data Science/[3] Machine learning as a service.mp411.26MB

[8] 7. Mathematics for Data Science/[1] Algebra.mp414.83MB

[8] 7. Mathematics for Data Science/[2] Calculus.mp48.66MB

[8] 7. Mathematics for Data Science/[3] Optimization and the combinatorial explosion.mp413.91MB

[8] 7. Mathematics for Data Science/[4] Bayes' theorem.mp410.58MB

[9] 8. Analyses for Data Science/[10] Aggregating models.mp415.04MB

[9] 8. Analyses for Data Science/[1] Descriptive analyses.mp423.19MB

[9] 8. Analyses for Data Science/[2] Predictive models.mp424.51MB

[9] 8. Analyses for Data Science/[3] Trend analysis.mp419.19MB

[9] 8. Analyses for Data Science/[4] Clustering.mp418.41MB

[9] 8. Analyses for Data Science/[5] Classifying.mp413.01MB

[9] 8. Analyses for Data Science/[6] Anomaly detection.mp418.19MB

[9] 8. Analyses for Data Science/[7] Dimensionality reduction.mp419.46MB

[9] 8. Analyses for Data Science/[8] Feature selection and creation.mp416.9MB

[9] 8. Analyses for Data Science/[9] Validating models.mp418.2MB