Insights on this problem

- [Instructor] This problem challenged me…on several levels,…and I came away with a lot of insights.…First of all,…data science isn't always cut and dry.…Data can be incomplete or confusing.…Sometimes it's missing,…as in the case of the missing population data…for children younger than 15.…And the questions being asked may not be concise.…In this case,…we're only asked for educational demand…for the state of California,…but what about individual counties?…What about non-binary genders?…In the end,…we can only provide answers based on the data we have.…

Next, the very first thing I did…was to decide how to answer the question.…I developed an algorithm to approximate the question…and then developed a strategy…for how that question would be answered.…This actually took me a few attempts,…and I discovered errors…in the way I was approaching the problem.…I've saved some of those examples in the experiments folder…in the exercise files.…Also, data can be messy.…What may be obvious to you and I…may not be obvious to a computer program.…