Big data is unique in more ways than its size. Learn ten ways in which big data differs in its nature, its requirements, and its benefits from conventional, small data.
- [Instructor] Big data isn't just a supersized version … of small data. … It's qualitatively different in a number of ways … that actually matter to what you're doing. … In the book, Principles of Big Data: … Preparing, Sharing, and Analyzing Complex Information, … Jules Berman outlines 10 ways, … maybe you could say they're the top 10, … that big data is different from small data, … and I want to walk you through those. … First, they differ in their goals. … For small data, … the goal is usually a specific, singular goal. … We're trying to accomplish this one task … by analyzing the data. … On the other hand, with big data, … the goals evolve and they can redirect over time. … You may have one when you start, … but things can take unexpected directions. … Second is location. … Small data is usually in one place and often, … in one computer file or one floppy disk. … But big data is much larger because it is big, … and the data may be spread across multiple servers … in multiple locations anywhere on the internet. …
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: Ten ways big data is different from small data