Consumers benefit from applications that draw on big data to provide direct services, such as travel directions, friend recommendations, and health feedback.
- One of the great paradoxes of big data is that … sometimes all of the technological wizardry, … all the equipment, all the huge amount … of effort that goes into it, … is put into motion to help with something … as theoretically simple as choosing whether … to go left or right at the fork in the road. … The idea here is that big data is … frequently used to create small data. … The small amount of information, the direction, … the couple of restaurant recommendations … that the end user wants. … It's really another example of the general principal … that all analysis, all data analysis, … is an act of simplification. … You are taking something very complex, … something potentially huge and overwhelming, … and reducing it with hopefully … still maintaining the general form and feel … to something that is more usable and actionable. … Now, in terms of applications based on big data … some of the really common ones are things like … directions from point A to point B, … or maybe something like finding jobs online, …
Author
Barton Poulson
Released
9/19/2019- Identify the components that make up big data.
- Examine how big data has grown over the last few years.
- Explain the importance of using big data in business organizations.
- Distinguish between knowledge requirements for using big data and for understanding data science.
- Justify the need for training on big data within an organization.
- Analyze the factors that go into utilizing big data on a project.
- Differentiate outcomes that are derived from big data from outcomes that are derived from observing behaviors.
Skill Level Beginner
Duration
Views
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Introduction
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How big data shapes AI1m 46s
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1. Defining Big Data
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2. How Is Big Data Used?
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Big data for applications4m 41s
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3. Big Data and Data Science
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4. Ethics in Big Data
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Big data and privacy5m 52s
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Data governance6m 2s
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5. Data Logistics
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An evolving data landscape5m 48s
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6. Analyzing Big Data
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Visualizing big data5m 13s
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Data mining4m 39s
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Text analytics4m 18s
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Sentiment analysis4m 48s
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Predictive analytics4m 7s
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Anomaly detection3m 59s
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Conclusion
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Next steps2m 56s
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Video: Big data for applications