Big Data V's

2. How does data spread among other users and applications?
4. Data science continues to provide ever-increasing value for users as more data becomes available and new techniques are developed.
5. Big data allows us a privileged view of complex systems.
6. As an increasing number of people are affected by models’ decisions Veracity and Validity become ever more important.
8. for accurate analysis.
10. In data science we work with many data formats
12. Data science often models variable data sources. Models deployed into production can encounter especially wild data.
14. With the advent of the Internet of Things (IoT) the “bigness” of big data is accelerating.

1. Data science provides the capability to peer behind the curtain and examine the effects of latent variables in the data.
2. With many data science applications based on large and often sensitive data sets data security is increasingly important.
3. The meaning of found data is often very unclear regardless of how much data is available.
4. Rigor in analysis is essential for valid predictions
5. A thriving data science community is vital and it provides insights ideas and support in all of our endeavors.
7. Reproducibility is
9. How end-users interact with our work matters and polish counts.
10. Not only is the volume of data ever increasing but the rate of data generation (from the Internet of Things social media etc.) is increasing as well.
11. Data science can aid decision making by pointing in the correct direction.
12. It is difficult to build robust models and it’s harder still to build systems that will be viable in production.
13. Data science work takes place in different locations and under different arrangements Locally on customer workstations and in the cloud.