Join Barton Poulson for an in-depth discussion in this video Velocity, part of Big Data Foundations: Techniques and Concepts.
- So for velocity, this is when data is coming in very fast.…In conventional scientific research,…it could take months to gather data from 100 cases,…weeks to analyze the data,…and years to get that research published.…Not only is this kind of data time consuming to gather,…it's generally static once it's entered,…that is, it doesn't change.…As an example, perhaps the most familiar data set…for teaching the statistical procedure, cluster analysis,…is the Iris data collected by Edgar Anderson…and analyzed by Ronald Fisher,…both of whom published their papers in 1936.…
This data set contains four measurements:…The widths and lengths of the petals and sepals…for three species of Iris.…It's got about 150 cases,…and this data set is used everyday.…It's one of the built in data sets…in the statistical programming language, R,…and it hasn't changed in nearly 80 years.…At the other end of the scale,…if you're interested in using data…from a social media platform, like Twitter,…you may have to deal with the so-called "fire hose".…
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- What makes big data "big"
- Understanding how big data impacts consumers, businesses, and research
- Exploring the intersection of data science and big data
- Facing big data's ethical challenges
- Understanding the sources and structures of big data
- Storing big data
- Prepping big data for analysis
Skill Level Beginner
What Is Big Data?
2. How Is Big Data used?
3. Big Data and Data Science
4. Ethics in Big Data
Challenges with anonymity5m 51s
5. Sources and Structures of Big Data
6. Storing Big Data
7. Preparing Data for Analysis
8. Big Data Analysis
Next steps1m 58s
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