In this video, learn how deep learning neural networks are a class of algorithms that are especially powerful in artificial intelligence and data science, however, data science is broader than deep learning, therefore you will need to choose your approaches wisely.
- [Instructor] If you've ever been around a baby, … you know that babies take very little steps. … But the thing about baby steps is that you still get moving … and eventually, babies grow and they take bigger steps. … And before you know it, you've got a world-class sprinter. … And there's a similar thing, I like to think, … that happens with neural networks. … And what happens here is that tiny steps with data … can lead to amazing analytical results. … Now, an artificial neural network in computing … is modeled roughly after the neurons … that are inside a biological brain. … Those neurons are nothing more … than simple on and off switches … that are connecting with each other, … but give rise to things like love and consciousness. … In the computing version, the idea is to take some … very basic pieces of information, … and by connecting it with many other nodes, … you can give rise to the sort of emergent behavior, … which really is very high-level cognitive decisions … and classifications. …
Author
Released
8/8/2019- Assess the skills required for a career in data science.
- Evaluate different sources of data, including metrics and APIs.
- Explore data through graphs and statistics.
- Discover how data scientists use programming languages such as R, Python, and SQL.
- Assess the role of mathematics, such as algebra, in data science.
- Assess the role of applied statistics, such as confidence intervals, in data science.
- Assess the role of machine learning, such as artificial neural networks, in data science.
- Define the components of effective data visualization.
Skill Level Beginner
Duration
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Introduction
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1. What Is Data Science?
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The data science pathway4m 51s
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2. The Place of Data Science in the Data Universe
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Artificial intelligence8m 22s
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Machine learning8m 6s
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Big data5m 36s
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Predictive analytics4m 57s
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Prescriptive analytics7m 42s
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Business intelligence4m 40s
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3. Ethics and Agency
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4. Sources of Data
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Data preparation5m 26s
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In-house data2m 6s
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Open data4m 49s
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APIs2m 40s
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Scraping data4m 44s
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Creating data5m 37s
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Self-generated data3m 30s
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5. Sources of Rules
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6. Tools for Data Science
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Languages for data science3m 55s
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7. Mathematics for Data Science
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Algebra7m 25s
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Calculus5m 3s
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Bayes' theorem4m 25s
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8. Analyses for Data Science
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Descriptive analyses6m 38s
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Predictive models7m 32s
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Trend analysis6m 22s
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Clustering5m 45s
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Classifying5m 34s
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Dimensionality reduction5m 42s
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Validating models4m 55s
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Aggregating models4m 8s
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9. Acting on Data Science
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Interpretability3m 17s
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Actionable insights2m 53s
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Conclusion
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Next steps2m 47s
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Video: Deep learning neural networks