Hello,
we are here in SAP Analytics Cloud story interface
We are leveraging the United State college scorecard dataset,
a fantastic material for data analysis.
In a previous video we used Augmented Analytics capabilities
to explore the factors influencing
students earnings 10 years after college
an open-ended question that would be
extremely difficult to answer with traditional data discovery.
We explored the top influencers
the college Carnegie Classification
the share of students over 23,
the predominant degree awarded,
the college expenditure,
the share of female students,
All have major impact.
But above all we found that out of the top 10 factors
driving future earnings
four are related to the student family income
Let’s come back to the degree awarded
obviously, this has a major influence on future earnings
and that’s a good thing!
Let’s add this to our dashboard:
students holding a bachelor earn more than those holding an
associate degree and associates earn more
than individuals that have a certificate
Let’s combine this with another earnings influencer the share of
women in college which we found so puzzling in the previous video
Here I’d like to pause for one second to reflect on how we are
building our dashboard, how we progress in our analysis
It is radically different from what we would be doing
using a traditional Data Discovery tool.
SAP Analytics Cloud guides us and makes us focus
on our questions, we don’t have to worry about selecting charts,
dimensions & measures or calculations
Ok so now from the treemap that was created for us
let’s filter out colleges that did not provide
their share of women students.
What we see very clearly is that for bachelors
earnings is the highest if you come from a college
with a high share of women, between 79% and 93%,
but for associate and certificate degrees it’s the opposite
earnings are the highest
if you come from a college with a minority of women.
This an exceptional finding.
SAP Analytics Cloud also enables to pin point
out of the thousands of colleges included in the dataset,
colleges with unexpected future earnings, outliers if you will.
So here we see that earnings are unusually low in a law school
and a school of optometry in Porto Rico.
That could well be specific to Porto Rico were the cost of living
is lower than in mainland US
And of course you also have outliers the other way around
where earnings is higher than expected
To conclude this exploration of the College Scorecard dataset
Let's ask SAP Analytics Cloud to analyze earnings
10 years after college another time
If we go back to the top colleges in terms of earnings
and add the predominant undergraduate degree awarded
we see that the top colleges only award graduate diplomas,
so PhDs and masters .
This skews the analysis
of undergraduate studies performance, right?
So, lets ask SAP Analytics Cloud to analyze earnings
but this time only for colleges that deliver undergraduate degrees.
And that’s the beauty of SAP Analytics Cloud,
as we progress in our analysis,
we can ask more specific questions,
and we can do that as many times as you want.
We see that the distribution of earnings has a similar shape
we can find here the filter we just defined
On the second page, we see that the Carnegie classification is not
a key influencer anymore,
that's what we assumed would happen.
Some key influencers though remain:
like expenditure per student,
the share students over 23, the share of women
But some are new including the top 3
the share of students with a Pell grant, completion rate,
and admission rate.
All this make sense.
Expenditure per full time student still has
a positive impact on future earnings.
The larger the college is, the better the future earnings are,
that’s a bit surprising.
And the next influencer looks related.
The number of male students that completed their studies
within 8 years has a similar influence.
The open admission after high school has a negative impact
on future earnings, that’s expected.
Now, check this out!
This is the share of students whose parents
got a diploma after high school.
SAP Analytics Cloud found that the parent's academic education
was correlated with the share of first-generation students.
And we see that the more educated the parents are,
the more money the children will make.
This is a terrific finding!
We found that not only family income has a major influence
on future earnings
but that the student parent's education
also has a major impact
which shows how little of our financial success
we can claim credit for.
We have tendency to search for information
that confirms our beliefs. I bet that without
Augmented Analytics we would have ignored this possibility
Look at this now!
I added the share of first-generation students and I immediately
see the relationship with the parent's educations
and the student future earnings
SAP Analytics Cloud augments us in a way that removes bias,
personal beliefs, personal opinions.
It pulls us out of our comfort zone, the place where the simpler
and the more familiar a idea is, the truer it feels.
Let’s go back to the college scorecard dataset,
10 minutes ago, we were here,
in a situation of not knowing how to analyze this dataset
with its hundreds of fields.
secretly holding on to our comfort zone, hoping to find
a correlation between future earnings and students
admission scores or admission rates.
And we did, but we got so much more.
We got, a crash course in sociology, on gender issues, racial issues.
This is what Augmented Analytics is about,
an eye-opening exploration even on data you think you know,
an amazing tool to excel in data analysis,
a tool to grasp the intricacies of the world,
the subtleties of your business
To conclude I'd like to make a call to action
build rich and wide datasets
this how you will get the full value of Augmented Analytics
For a long time, the best practice was to expose to BI users
a limited, a focused set of dimensions & measures
so that the human mind could get a grasp on it.
We need to reconsider this.
With the College Scorecard dataset
SAP Analytics Cloud selected, handpicked 25 fields,
the most relevant to our questions,
that's less than 3% of the available objects
Thanks for watching!