Unstructured text includes social media posts, patent applications, scientific articles, and legal decisions, all of which have benefitted from the methods and insights of big data analyses.
- [Instructor] I've been teaching in universities … for over 20 years, … but it took me more than a decade to come to grips … with one common element of that job, grading papers. … The fact is, reading research papers, … giving useful feedback, … and assigning grades takes a lot of time, … and frequently I could get overwhelmed by the effort, … the cognitive energy it took. … But if you're working in the online world, then you know … that that sort of unstructured text information … can come in much, much faster than even student papers. … Dealing with that fire hose … of feedback is a huge challenge for anyone, … and that's the entire point of text analytics in big data. … Now, there are a few major challenges … that prompt all of this. … Number one, text is unstructured data. … It's there. … It's in sentences and paragraphs and words. … It's not broken down … into this is a variable, this is a topic. … Also, you can have text from multiple sources. … You can have comments and reviews 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: Text analytics