A common mistake made in data science projects is rushing into
data collection and analysis, which precludes spending sufficient
time plan and scope the amount work involved, understanding to of
requirements, even framing the business problem or properly.
The following data analytic lifecycle will be applied to f f in
achieving the scope this of project
Phase Discovery: Phase the team learns the business 1— In 1,
domain, including relevant history such whether the organization as
or business unit attempted similar projects the past from has in
which they can learn. The team assesses the resources available to
support the project terms technology, time, and data. in of people,
Important activities this phase include framing the business in
problem analytics challenge that can addressed as an be in
subsequent phases and formulating initial hypotheses (IHs) test to
and begin learning the data.
Phase Data preparation: Phase requires the presence 2— 2 of an
analytic sandbox, which the team can work with data and in
perform analytics for the duration the project. The team needs of
to execute extract, load, and transform (ELT) extract, transform or
and load (ETL) data into the sandbox. The ELT and ETL are to get
sometimes abbreviated ETLT. Data should ansformed the as be tr in
ETLT process team can work with and analyze it. this so the it In
