In this video, learn how several cloud computing platforms such as IBM Watson Analytics, Microsoft Azure ML, Amazon Machine Learning, and Google AutoML now have built-in machine learning capabilities to facilitate data science work.
- [Instructor] One of the things that is most predictable … about technology is that things get faster, … smaller, easier, and better over time. … This is the essence of Moore's Law, … which originally talked about just the density … of transistors on circuits doubling every two years, … but think about, for instance, the women working here … on ENIAC, that's the Electronic Numerical … Integrator and Computer, … which was the first electronic general-purpose computer … back in 1945. … It was huge. … It filled up a room and it took a whole team of people … to run it. … Then things evolved, for instance, to very colorful … reel-to-reel computers, then you get your desktop Macintosh, … I still have my Classic II, and before you know it, … you're running your billion-dollar tech company … from your cell phone. … One of the most important developments in the internet era … has been SaaS, or software as a service. … Just think of anytime you've used an online application … like Excel Online instead of an application …
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: Machine learning as a service