With Magic ETL's new
data science actions,
you'll be able to discover smart
new insights into your business
data, even if you're
not a data scientist.
These new actions bring
the power of data science
directly to you within Magic
ETL's easy-to-use platform.
These new actions
provide coverage
for the most common
business-related data science
use cases.
Here's a quick explanation for
each of our six data science
actions.
The classification
action is used
when predicting
categorical output
values as opposed to numeric.
The classification action
includes both random forest
and naive Bayes algorithms.
Clustering allows you to
discover inherent groupings
in data.
The clustering action
includes both k means and k
median clustering algorithms.
Forecasting enables users to
get a better understanding
of their data or
forecast a future data
point within time series data.
The forecasting action uses
the ARIMA forecasting method
to provide a robust and
simple forecasting experience.
Multivariate outlier
detection allows
users to identify data
points that diverge away
from the overall data
pattern when evaluated
against two or more variables.
This is similar to
the outlier detection,
except that here, multiple
variables are being evaluated.
Outlier detection is used
to identify individual data
or data points that diverge
away from the overall pattern
within a larger DataSet.
The outlier detection
action includes
both standard deviation and mean
absolute deviation algorithms.
The regression tile is used
when predicting numerical output
values as opposed
to categorical.
The regression action includes
both linear regression
and random forest algorithms.
To use data science
in Magic ETL,
simply drag the desired
action onto the canvas,
connect to your DataSet,
and configure it.
Now your DataSet can be
analyzed and enhanced every time
your ETL DataFlow runs.
With the power of
data science in Domo,
you'll be better prepared
to make business decisions
and be more knowledgeable
about your business data.
