My name is Eoin Lane. I am a director
here of emerging analytics at Dun &
Bradstreet. My team generally takes kind
of proof of concept ideas you know
within Dun & Bradstreet and usually
tries to implement, stuff like that. For
example, we would have built
recommendation engines very similar to
what you have on, you know, your Netflix
watchlist, we build them for companies.
There's always kind of problems to be
kind of solved here,
you know, they're particularly
mathematical in nature, but I enjoy that
aspect of it and a lot of problems, tackling them is very enjoyable. My
background is in chemistry. Part of my
graduate work I did what would be now
considered IoT, pulling
information from sensors and then
analysing that information. I just happened
to apply that to molecules colliding at
high-energy collisions. From there, I made
my way to IBM and I became part of a
team that was responsible for the
Smartest Cities Smarter Planet
initiative, and I in particular was
responsible for water and energy.
My primary advice to people would be to,
you know, learn a programming language, in
particular I would learn Python. The
industry has really standardised on
Python at this point for data science,
and you know, knowing, kind of, you know,
basic data science structures like, you
know, hash maps, hash tables, linked lists,
Q Stacks, these will all be things that
you will you will need as you move on.
What I would then focus on is to get
some real-world expertise. You can go
online to place like Kaggle and download
real-world data sets that you can start
to analyse, maybe first with tools like
core [business intelligence]
and Python and and build your expertise
from there. And once you become
comfortable, then come work for us.
