JESSICA RUELENS

DATA S C I E N T I S T

DATA SCIENCE

STUDIES

CAREER PATH

FUTURE ASPIRATIONS

DATA SCIENCEAK A DATA MINING, MACHINE LEARNING , BIG DATA , ANALYTICS

INSIGHTS

DATA

!

420 million wearable wireless

health monitors

400 million tweets a day

6 billion cell phones

2,5 quintillion bytes (2,3

trillion gigabytes or ) of data

are created each day

100 terabytes is what most

companies have stored

DATA SCIENCEART AND SCIENCE

"Essentially, all

models are wrong, but

some are useful."--- Box, George E. P.; Norman R.

Draper (1987).

The aim is to discover meaningful insights and knowledge from data

BUILDING MODELS

Model captures, in some formulation, the essence of the discovered knowledge

Collection of observational

Data

StatisticsTo understand the veracity of model

TechnologyMachine learning

DATA SCIENCESMACK IN THE MIDDLE OF BUSINESS AND IT

Identify thebusiness problem

Identify thedata sources

Select thedata

Clean thedata

Transformthe data

Analyze the data

Interpret, evaluate and

deploy

DATA SCIENCEREAL WORLD EXAMPLES AND APPLICATIONS

Beer and

nappies are

bought together

on Friday

afternoon

Amazon ships

before you buy

Target knows

you’re

pregnant

before your

father does

Insurance uses

telematics to calculate

their premium

Sentiment

analysis on

tweets predicts

the stock

market

Location based

services

Google

knows

where you

are going

Class of Professor Bart BaesensDiscovery of Data Mining 10 years ago

STUDIESDISCOVERY

Bachelor and Master

Business Engineering,

Information Management

Cum laude

CAREER PATH 2009-2014EXPERIENCE IN SYDNEY, AUSTRALIA

Technical Support for all analytical tools

Analytics Consulting, Customer Intelligence

Campaign Targeting for largest supermarket

High end analytics of BIG DATA QUANTIUM

CAREER PATH 2015EXPERIENCE IN BELGIUM

Credit Risk Modeling Large Belgian Bank

Customer Segmentation Wolters Kluwer

THINGS I HAVE LEARNT (NON EXHAUSTIVE)

Lies, damned lies and statistics

Think big, act small ->

incremental and iterative

= agile

Sometimes it’s not about what you

know but who you know

adapt to a changing world, constantly

learn new things

be open minded, do not get stuck in

set ways

listen to the business, if the analysis

is not useful, you have wasted

everyone’s time, including your own

keep sensitive data well protected

Visuals are key in conveying messages

You need a multidisciplinary TEAM

FUTURE ASPIRATIONS

BECOME A BETTER DATA SCIENTIST

build up experience so that I am better able to service customers

willing to discover what their data is hiding

+ USE DATA FOR GOOD

e,g, health, crime detection and prevention, disaster risk mitigation

+ inspire other people

To acknowledge the potential and joy of analytics

We live in a data

flooded world,

And I am excited

to plunge in!

Thank you!