LinkedIn Learning - Data Science Foundations Fundamentals

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LinkedIn Learning - Data Science Foundations Fundamentals
  • !!! More Courses !!!.txt (1.1 KB)
    • [10] 9. Acting on Data Science
    • [1] Interpretability.mp4 (9.3 MB)
    • [1] Interpretability.srt (5.8 KB)
    • [2] Actionable insights.mp4 (8.2 MB)
    • [2] Actionable insights.srt (4.9 KB)
    [11] Conclusion
    • [1] Next steps.mp4 (9.5 MB)
    • [1] Next steps.srt (4.9 KB)
    [1] Introduction
    • [1] The fundamentals of data science.mp4 (11.8 MB)
    • [1] The fundamentals of data science.srt (2.5 KB)
    [2] 1. What Is Data Science
    • [1] Supply and demand for data science.mp4 (8.3 MB)
    • [1] Supply and demand for data science.srt (6.8 KB)
    • [2] The data science Venn diagram.mp4 (8.6 MB)
    • [2] The data science Venn diagram.srt (7.9 KB)
    • [3] The data science pathway.mp4 (23.2 MB)
    • [3] The data science pathway.srt (9.7 KB)
    • [4] Roles and teams in data science.mp4 (14.0 MB)
    • [4] Roles and teams in data science.srt (8.1 KB)
    [3] 2. The Place of Data Science in the Data Universe
    • [1] Artificial intelligence.mp4 (29.6 MB)
    • [1] Artificial intelligence.srt (14.2 KB)
    • [2] Machine learning.mp4 (30.0 MB)
    • [2] Machine learning.srt (13.7 KB)
    • [3] Deep learning neural networks.mp4 (30.5 MB)
    • [3] Deep learning neural networks.srt (12.3 KB)
    • [4] Big data.mp4 (14.9 MB)
    • [4] Big data.srt (9.5 KB)
    • [5] Predictive analytics.mp4 (12.0 MB)
    • [5] Predictive analytics.srt (8.5 KB)
    • [6] Prescriptive analytics.mp4 (14.0 MB)
    • [6] Prescriptive analytics.srt (13.7 KB)
    • [7] Business intelligence.mp4 (11.1 MB)
    • [7] Business intelligence.srt (7.0 KB)
    [4] 3. Ethics and Agency
    • [1] Legal, ethical, and social issues of data science.mp4 (12.6 MB)
    • [1] Legal, ethical, and social issues of data science.srt (7.8 KB)
    • [2] Agency of algorithms and decision-makers.mp4 (12.6 MB)
    • [2] Agency of algorithms and decision-makers.srt (9.1 KB)
    [5] 4. Sources of Data
    • [1] Data preparation.mp4 (15.0 MB)
    • [1] Data preparation.srt (9.5 KB)
    • [2] In-house data.mp4 (6.1 MB)
    • [2] In-house data.srt (3.8 KB)
    • [3] Open data.mp4 (20.5 MB)
    • [3] Open data.srt (8.8 KB)
    • [4] APIs.mp4 (10.6 MB)
    • [4] APIs.srt (4.7 KB)
    • [5] Scraping data.mp4 (29.7 MB)
    • [5] Scraping data.srt (8.5 KB)
    • [6] Creating data.mp4 (14.7 MB)
    • [6] Creating data.srt (10.3 KB)
    • [7] Passive collection of training data.mp4 (13.2 MB)
    • [7] Passive collection of training data.srt (6.9 KB)
    • [8] Self-generated data.mp4 (14.0 MB)
    • [8] Self-generated data.srt (5.8 KB)
    [6] 5. Sources of Rules
    • [1] The enumeration of explicit rules.mp4 (12.7 MB)
    • [1] The enumeration of explicit rules.srt (7.1 KB)
    • [2] The derivation of rules from data analysis.mp4 (23.8 MB)
    • [2] The derivation of rules from data analysis.srt (7.6 KB)
    • [3] The generation of implicit rules.mp4 (9.2 MB)
    • [3] The generation of implicit rules.srt (6.1 KB)
    [7] 6. Tools for Data Science
    • [1] Applications for data analysis.mp4 (15.2 MB)
    • [1] Applications for data analysis.srt (7.9 KB)
    • [2] Languages for data science.mp4 (12.9 MB)
    • [2] Languages for data science.srt (6.6 KB)
    • [3] Machine learning as a service.mp4 (11.3 MB)
    • [3] Machine learning as a service.srt (5.7 KB)
    [8] 7. Mathematics for Data Science
    • [1] Algebra.mp4 (14.8 MB)
    • [1] Algebra.srt (13.3 KB)
    • [2] Calculus.mp4 (8.7 MB)
    • [2] Calculus.srt (7.8 KB)
    • [3] Optimization and the combinatorial explosion.mp4 (13.9 MB)
    • [3] Optimization and the combinatorial explosion.srt (11.6 KB)
    • [4] Bayes' theorem.mp4 (10.6 MB)
    • [4] Bayes' theorem.srt (7.8 KB)
    [9] 8. Analyses for Data Science
    • [10] Aggregating models.mp4 (15.0 MB)
    • [10] Aggregating models.srt (6.9 KB)
    • [1] Descriptive analyses.mp4 (23.2 MB)
    • [1] Descriptive analyses.srt (12.4 KB)
    • [2] Predictive models.mp4 (24.5 MB)
    • [2] Predictive models.srt (13.7 KB)
    • [3] Trend analysis.mp4 (19.2 MB)
    • [3] Trend analysis.srt (11.7 KB)
    • [4] Clustering.mp4 (18.4 MB)
    • [4] Clustering.srt (10.5 KB)
    • [5] Classifying.mp4 (13.0 MB)
    • [5] Classifying.srt (10.3 KB)
    • [6] Anomaly detection.mp4 (18.2 MB)
    • [6] Anomaly detection.srt (9.4 KB)
    • [7] Dimensionality reduction.mp4 (19.5 MB)
    • [7] Dimensionality reduction.srt (9.7 KB)
    • [8] Feature selection and creation.mp4 (16.9 MB)
    • [8] Feature selection and creation.srt (11.0 KB)
    • [9] Validating models.mp4 (18.2 MB)
    • [9] Validating models.srt (8.3 KB)
  • logo.jpg (72.1 KB)

Description


LinkedIn Learning - Data Science Foundations Fundamentals
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 45 lectures (3h 42m) | Size: 703 MB

Course description
Data science is driving a world-wide revolution that touches everything from business automation to social interaction. It’s also one of the fastest growing, most rewarding careers, employing analysts and engineers around the globe. This course provides an accessible, nontechnical overview of the field, covering the vocabulary, skills, jobs, tools, and techniques of data science. Instructor Barton Poulson defines the relationships to other data-saturated fields such as machine learning and artificial intelligence. He reviews the primary practices: gathering and analyzing data, formulating rules for classification and decision-making, and drawing actionable insights. He also discusses ethics and accountability and provides direction to learn more. By the end, you’ll see how data science can help you make better decisions, gain deeper insights, and make your work more effective and efficient.



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LinkedIn Learning - Data Science Foundations Fundamentals

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703.5 MB
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LinkedIn Learning - Data Science Foundations Fundamentals

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