LinkedIn Learning - Data Science Foundations Fundamentals [CoursesGhar]

Creation Time	June 1, 2024, 10:01 p.m.
Last Access Time	Nov. 9, 2024, 11:07 a.m.
File Size	703.5 MB
Keywords	CoursesGhar Fundamentals Science - LinkedIn Learning Foundations Data
Total Requests	52
Total Files	98
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[3] 2. The Place of Data Science in the Data Universe/[3] Deep learning neural networks.mp4 30.5 MB
[3] 2. The Place of Data Science in the Data Universe/[2] Machine learning.mp4 30.0 MB
[5] 4. Sources of Data/[5] Scraping data.mp4 29.7 MB
[3] 2. The Place of Data Science in the Data Universe/[1] Artificial intelligence.mp4 29.6 MB
[9] 8. Analyses for Data Science/[2] Predictive models.mp4 24.5 MB
[6] 5. Sources of Rules/[2] The derivation of rules from data analysis.mp4 23.8 MB
[2] 1. What Is Data Science/[3] The data science pathway.mp4 23.2 MB
[9] 8. Analyses for Data Science/[1] Descriptive analyses.mp4 23.2 MB
[5] 4. Sources of Data/[3] Open data.mp4 20.5 MB
[9] 8. Analyses for Data Science/[7] Dimensionality reduction.mp4 19.5 MB
[9] 8. Analyses for Data Science/[3] Trend analysis.mp4 19.2 MB
[9] 8. Analyses for Data Science/[4] Clustering.mp4 18.4 MB
[9] 8. Analyses for Data Science/[9] Validating models.mp4 18.2 MB
[9] 8. Analyses for Data Science/[6] Anomaly detection.mp4 18.2 MB
[9] 8. Analyses for Data Science/[8] Feature selection and creation.mp4 16.9 MB
[7] 6. Tools for Data Science/[1] Applications for data analysis.mp4 15.2 MB
[9] 8. Analyses for Data Science/[10] Aggregating models.mp4 15.0 MB
[5] 4. Sources of Data/[1] Data preparation.mp4 15.0 MB
[3] 2. The Place of Data Science in the Data Universe/[4] Big data.mp4 14.9 MB
[8] 7. Mathematics for Data Science/[1] Algebra.mp4 14.8 MB
[5] 4. Sources of Data/[6] Creating data.mp4 14.7 MB
[2] 1. What Is Data Science/[4] Roles and teams in data science.mp4 14.0 MB
[3] 2. The Place of Data Science in the Data Universe/[6] Prescriptive analytics.mp4 14.0 MB
[5] 4. Sources of Data/[8] Self-generated data.mp4 14.0 MB
[8] 7. Mathematics for Data Science/[3] Optimization and the combinatorial explosion.mp4 13.9 MB
[5] 4. Sources of Data/[7] Passive collection of training data.mp4 13.2 MB
[9] 8. Analyses for Data Science/[5] Classifying.mp4 13.0 MB
[7] 6. Tools for Data Science/[2] Languages for data science.mp4 12.9 MB
[6] 5. Sources of Rules/[1] The enumeration of explicit rules.mp4 12.7 MB
[4] 3. Ethics and Agency/[2] Agency of algorithms and decision-makers.mp4 12.6 MB
[4] 3. Ethics and Agency/[1] Legal, ethical, and social issues of data science.mp4 12.6 MB
[3] 2. The Place of Data Science in the Data Universe/[5] Predictive analytics.mp4 12.0 MB
[1] Introduction/[1] The fundamentals of data science.mp4 11.8 MB
[7] 6. Tools for Data Science/[3] Machine learning as a service.mp4 11.3 MB
[3] 2. The Place of Data Science in the Data Universe/[7] Business intelligence.mp4 11.1 MB
[8] 7. Mathematics for Data Science/[4] Bayes' theorem.mp4 10.6 MB
[5] 4. Sources of Data/[4] APIs.mp4 10.6 MB
[11] Conclusion/[1] Next steps.mp4 9.5 MB
[10] 9. Acting on Data Science/[1] Interpretability.mp4 9.3 MB
[6] 5. Sources of Rules/[3] The generation of implicit rules.mp4 9.2 MB
[8] 7. Mathematics for Data Science/[2] Calculus.mp4 8.7 MB
[2] 1. What Is Data Science/[2] The data science Venn diagram.mp4 8.6 MB
[2] 1. What Is Data Science/[1] Supply and demand for data science.mp4 8.3 MB
[10] 9. Acting on Data Science/[2] Actionable insights.mp4 8.2 MB
[5] 4. Sources of Data/[2] In-house data.mp4 6.1 MB
[3] 2. The Place of Data Science in the Data Universe/[1] Artificial intelligence.srt 14.2 KB
[9] 8. Analyses for Data Science/[2] Predictive models.srt 13.7 KB
[3] 2. The Place of Data Science in the Data Universe/[2] Machine learning.srt 13.7 KB
[3] 2. The Place of Data Science in the Data Universe/[6] Prescriptive analytics.srt 13.7 KB
[8] 7. Mathematics for Data Science/[1] Algebra.srt 13.3 KB
[9] 8. Analyses for Data Science/[1] Descriptive analyses.srt 12.4 KB
[3] 2. The Place of Data Science in the Data Universe/[3] Deep learning neural networks.srt 12.3 KB
[9] 8. Analyses for Data Science/[3] Trend analysis.srt 11.7 KB
[8] 7. Mathematics for Data Science/[3] Optimization and the combinatorial explosion.srt 11.6 KB
[9] 8. Analyses for Data Science/[8] Feature selection and creation.srt 11.0 KB
[9] 8. Analyses for Data Science/[4] Clustering.srt 10.5 KB
[5] 4. Sources of Data/[6] Creating data.srt 10.3 KB
[9] 8. Analyses for Data Science/[5] Classifying.srt 10.3 KB
[9] 8. Analyses for Data Science/[7] Dimensionality reduction.srt 9.7 KB
[2] 1. What Is Data Science/[3] The data science pathway.srt 9.7 KB
[3] 2. The Place of Data Science in the Data Universe/[4] Big data.srt 9.5 KB
[5] 4. Sources of Data/[1] Data preparation.srt 9.5 KB
[9] 8. Analyses for Data Science/[6] Anomaly detection.srt 9.4 KB
[4] 3. Ethics and Agency/[2] Agency of algorithms and decision-makers.srt 9.1 KB
[5] 4. Sources of Data/[3] Open data.srt 8.8 KB
[5] 4. Sources of Data/[5] Scraping data.srt 8.5 KB
[3] 2. The Place of Data Science in the Data Universe/[5] Predictive analytics.srt 8.5 KB
[9] 8. Analyses for Data Science/[9] Validating models.srt 8.3 KB
[2] 1. What Is Data Science/[4] Roles and teams in data science.srt 8.1 KB
[7] 6. Tools for Data Science/[1] Applications for data analysis.srt 7.9 KB
[2] 1. What Is Data Science/[2] The data science Venn diagram.srt 7.9 KB
[8] 7. Mathematics for Data Science/[2] Calculus.srt 7.8 KB
[8] 7. Mathematics for Data Science/[4] Bayes' theorem.srt 7.8 KB
[4] 3. Ethics and Agency/[1] Legal, ethical, and social issues of data science.srt 7.8 KB
[6] 5. Sources of Rules/[2] The derivation of rules from data analysis.srt 7.6 KB
[6] 5. Sources of Rules/[1] The enumeration of explicit rules.srt 7.1 KB
[3] 2. The Place of Data Science in the Data Universe/[7] Business intelligence.srt 7.0 KB
[5] 4. Sources of Data/[7] Passive collection of training data.srt 6.9 KB
[9] 8. Analyses for Data Science/[10] Aggregating models.srt 6.9 KB
[2] 1. What Is Data Science/[1] Supply and demand for data science.srt 6.8 KB
[7] 6. Tools for Data Science/[2] Languages for data science.srt 6.6 KB
[6] 5. Sources of Rules/[3] The generation of implicit rules.srt 6.1 KB
[5] 4. Sources of Data/[8] Self-generated data.srt 5.8 KB
[10] 9. Acting on Data Science/[1] Interpretability.srt 5.8 KB
[7] 6. Tools for Data Science/[3] Machine learning as a service.srt 5.7 KB
[11] Conclusion/[1] Next steps.srt 4.9 KB
[10] 9. Acting on Data Science/[2] Actionable insights.srt 4.9 KB
[5] 4. Sources of Data/[4] APIs.srt 4.7 KB
[5] 4. Sources of Data/[2] In-house data.srt 3.8 KB
[1] Introduction/[1] The fundamentals of data science.srt 2.5 KB
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LinkedIn Learning - Data Science Foundations Fundamentals [CoursesGhar]

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