Lesson 3: Computing Concepts

Prompt:
Identifying Muddiest Points:
Reflect on the data analytics terms: descriptive, predictive, and prescriptive. Identify any specific concepts or aspects that you find particularly confusing or unclear.
Share your muddiest points with the group, providing a brief description of the confusion.
Peer Clarification:
Engage with your peers' muddiest points by offering insights, explanations, or examples that might help clarify the confusion.
If you have a clear understanding of a concept, share your perspective on how you approached that particular concept and what helped you grasp it.

Post:
I find data very interesting as I am thinking about majoring in data science. Data needs to be descriptive it needs to be able to give the people looking at the data a good idea of what's going on. When data is just a bunch of numbers and isn't descriptive its not helpful to anyone. Data can also help us predict what's going to happen. This I find very interesting as when we I see data about sport's and the percentage about a play I find it very interesting because then I can make a observation on what is the chance of a team scoring or getting the first done or often the announcers will tell me because a data analytic told him based of the data. When I try to make a decision I often look at what the success rate of something is which is prescriptive data analytics.
After reading over other peoples my idea has changed more about there are data analyst who change data and make the data descriptive so we can read it. Also predictive so that we can predict what's gonna happen. Finally how big prescriptive and predictive data analytics are big in sports and that data about what an athlete does is huge in determining if a team will sign them or pay how much they will pay them.