welcome to the third video in the series data science for beginners in this one
you'll get some tips for formulating a question you can answer with data you
might get more out of this video if you first watch the two earlier videos in
this series the five questions data science can answer and is your data
ready for data science we've talked about how data science is the process of
using names and numbers to predict an answer to a question you can also call
names categories or labels now the trick here is that it can't be just any
question it has to be a sharp question a vague question doesn't have to be
answered with the name or a number but a sharp question must imagine you found a
magic lamp with the genie will truthfully answer any question you ask
but it's a mischievous Genie and he'll try to make his answer as vague and
confusing as he can get away with you want to put him down with a question so
airtight that he can't help but tell you what you want to know if you were to ask
a vague question like what's going to happen with my stock the genie might
answer the price will change as the truthful answer but it's not very
helpful yeah but if you were to ask a sharp question like what will my stock
sale price be next week the genie can't help but give you a
specific answer and predict a sale price
now once you formulate your question check to see whether you have examples
of the answer and your data so if your question is what will my stock sale
price be next week then we have to make sure that our data includes the stock
price history if our question is which car in my fleet is going to fail first
then we need to make sure that our data includes information about previous
failures these examples of answers are called a target a target is what we are
trying to predict about future data points whether it's a category a name or
a number if you don't have any target data you'll
need to get some you won't be able to answer your question without it now to
take this to the next level sometimes you can reword your question and get a
more useful answer the question is this data point A or B predicts the category
of something and then to answer it we use a classification algorithm the
question of how much or how many predicts an amount and to answer that we
use a regression algorithm to see how we can transform one of these into the
other let's look at the question which news story is the most interesting to
this reader it asks for a prediction of a single choice from many possibilities
in other words is this a or B or C or D is it this story or that one or that one
or that one and it would use a classification algorithm but this
question may be easier to answer if you reword it such as how interesting is
each story on this list to this reader now you can give each article a
numerical score and then it's easy to identify the highest scoring article
this is a rephrasing of a classification question into a regression question how
you ask a question is a clue to which algorithm can give you an answer you'll
find that certain families of algorithms like the ones are used in our news story
example are closely related you can reformulate your question to use
the algorithm that gives you the most useful answer but most importantly ask
the sharp question the question you can answer with data and be sure you have
the right data to answer it we talked about some basic principles
for asking a question that you can answer with data be sure to check out
the other videos in data science for beginners for Microsoft Azure machine
learning