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0:00
Hello, there. My name
is Valentine Mwangi.
In the past, I
funded AfterWork,
which is a part-time
Data Science Program for
working professionals where I
am currently the Program Lead.
0:15
On a day-to-day basis,
I am a curriculum designer
and facilitator specializing in
data science programs for
B2B and B2C data
science programs.
Past clients have come
from various sectors,
not limited to Telecom,
Wildlife Conservation and
Humanitarian Aid
Organizations. In contrast,
institutions have included
boot camps such as AfterWork,
Practicum by Yandex, Mimo App,
Udemy, and Moringa School.
On the side, I organize
data science events
for the AfterWork Data
Science Community,
a Pan-African community of over
4,000 merging data
scientists from Kenya,
South Africa, and Nigeria.
0:59
In this talk, we'll be
going through tips, tools,
and techniques for using
data analytics in business.
With the example of
a real-life case study of
how I used data analytics to
inform the decisions taken
while undertaking students
recruitment at AfterWork and how
that affected the revenue made.
By the end of this talk,
you should be able to recall and
explain the data
analytic steps that are
important when working with
any kind of data to
solve business problems.
We'll go through a
brief history of how
the data analytics
workflow that we will
discuss came to be and generally
define what that
workflow entails.
Then introduce my case study,
the one that we've mentioned,
describing how the workflow
was introduced to the project.
Lastly, we'll talk
about the outcomes,
failures and lessons learned,
and the improvements made.
