Big data projects can often deliver significant insight and value without requiring the full data science arsenal.
- [Instructor] You're familiar by now … with the 3 Vs of big data, … the Volume and Velocity and Variety of the data. … And you're also familiar with data science, … which involves Coding and Quantitative ability and Context. … Now, just as I have shown you that you can do data science … without needing big data, you can actually do big data … without needing the full data science toolkit, … and you can still get valuable and actionable insights … from those analyses. … So let's take a look at, for instance, when you would use … the coding and the quantitative abilities of data science … without necessarily needing the context … or the domain expertise. … The best example in this case is machine learning … or specifically, deep learning neural networks. … In this case, you can create a black box, … which means you don't necessarily know … exactly how the algorithm is processing the data, … but you can still get very useful categorizations … and identifications of cases that fit, … cases that don't fit, of anomalies, …
Author
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
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Video: Big data without data science