Free webinar on Analytics in a Big Data World by Bart Baesens.

Baesens_Bart_small

Nice overview on how analytics and datasciences are used in a bigdata world.

Professor Baesens will be present at the Data Innovation Summit on March 26th in Brussels.

He will present his latest book about Big Data Science.

Join us, you can get your free full day access pass when you  answer the Data Innovation Survey 2015.

Enjoy the webinar …

Join our next event

Please register using this meetup page:

Summit: Data Innovation Summit – Made in Belgium

Thursday, Mar 26, 2015, 8:00 AM

AXA building
boulevard du Souverain, 25 Watermael-Boitsfort, BE

450 Business & Data Science pro’s Attending

Toon Vanagt- Laurent Fayet – Filip Maertens – Kris Peeters – Vincent Blondel –David Martens – Hans ConstandtThe Data Innovation Summit in Brussels is a one day conference gathering all the Belgian actors facilitating data innovation. It is an action packed conference where more than 50 speakers will demonstrate what they do that helps us compete i…

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Training – UGent – Big Data – 26/2 to 4/6

Ugent Big Data

download the brochure here

Big Data

Wetenschappelijke coördinatie:
Prof. dr. Guy De Tré
Vakgroep Telecommunicatie en Informatieverwerking, UGent

  • Module 1: Gegevensbeheer
    26 februari, 5, 12, 19, 26 maart 2015

  • Module 2: Gegevensanalyse
    2, 23 en 30 april, 7 en 21 mei 2015

  • Module 3: Visualisatie
    28 mei en 4 juni 2015

Wetenschappelijk Coördinator

  • Prof. dr. Guy De Tré, Vakgroep Telecommunicatie en Informatieverwerking, Universiteit Gent

Lesgevers

  • Vivek Bajaj, IBM
  • Antoon Bronselaer, Vakgroep Telecommunicatie en Informatieverwerking, Universiteit Gent
  • Hans Constandt, Ontoforce
  • Thomas Demeester, Vakgroep Informatietechnologie, Universiteit Gent
  • Wesley De Neve, Vakgroep Elektronica en Informatiesystemen, Universiteit Gent
  • Guy De Tré, Vakgroep Telecommunicatie en Informatieverwerking, Universiteit Gent
  • Filip De Turck, Vakgroep Informatietechnologie, Universiteit Gent
  • Wim De Wispelaere, Amplidata
  • Erik Duval, Departement Computerwetenschappen, KU Leuven
  • Jan Fostier, Vakgroep Informatietechnologie, Universiteit Gent
  • Alain Houf, Intersystems
  • Joris Klerkx, Departement Computerwetenschappen, KU Leuven
  • Peter Lambert, Vakgroep Elektronica en Informatiesystemen, Universiteit Gent
  • Femke Ongenae, Vakgroep Informatietechnologie, Universiteit Gent
  • Dirk Van den Poel, Vakgroep Marketing, Universiteit Gent
  • Inge Van Nieuwerburgh, Directie Onderzoeksaangelegenheden, Afdeling Universiteitsbiblioitheek, Universiteit Gent
  • Nico Verplancke, Vlaams Instituut voor Archivering

 

Overzicht
Waarom
Doelpubliek
Getuigschrift
Lesgevers

MODULES

Module 1
Module 2
Module 3

Prijzen
Inschrijven
Praktisch
Doctoraatsopleiding

 

 

Meetup – DataScience & Telco – February 19th – Brussels

meetup

 

How often do you use your mobile phone every day?

Yes, the telecom industry is one of the richest in terms of customer data!

This gold mine of data has led telecom operators to be at the forefront of Big Data applications and has attracted leading researchers from the academic world. This session will reveal how these exceptional data sources can be leveraged, from 3 different points of view: the telecom operator, the academia and the telecom Big Data solution provider.

Join us to understand why the telecom industry is attracting so many data scientists.

Agenda

18h30 – Opening

Update on the activities of the community

– Doing the Data Science Moocs together
– Vitual Global Hackathon – HACKSKI
– Overview of the outstanding datascience vacancies in Belgium
– Agenda of coming events, trainings, …

Introduction by moderator

Overview of data Science in the Telecom Industry and speakers introduction

Moderator: Gautier Krings – Head of Research @ Real Impact Analytics

  • Jan Sonck – Head of Enterprise Innovation @ Proximus
    How do we use Data Science in a Telecom Operator?
    Example of external use cases: which business question, which data? What about privacy concerns?
  • Renaud Lambiotte – Assistant Professor @ FUNDP
    Social networks, from Granovetter to Zuckerberg 
  • Wim Hellemans – Head of Knowledge @ Real Impact Analytics
    How to convert Telecom data into insights?
    What are the benefits and challenges of processing telecom data.

21h00: Open Sessions

Attendants are invited on the spot to share in a few minutes interesting DataScience issues they have  encountered in the last month. Recent startups, other meetup groups and teamleaders recruiting experts are welcome to do their pitch.

21h30: Networking @ KultuurKaffee

 

Register on the Meetup page please.

Data Sciences in the Telecom Sector

Thursday, Feb 19, 2015, 6:30 PM

VUB – Aula QB
Pleinlaan 2B – 1050 Brussels, BE

90 Business & Data Science pro’s Attending

How often do you use your mobile phone every day?Yes, the telecom industry is one of the richest in terms of customer data!This gold mine of data has led telecom operators to be at the forefront of Big Data applications and has attracted leading researchers from the academic world.This session will reveal how these exceptional data sources can b…

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Building a team to follow together the new Datascience Coursera courses starting next week

coursera            jhu_new_logo_large

 

Hendrik D’Oosterlinck is taking the lead to organize this initiative.
hendrik.doosterlinck@gmail.com

 

Coursera and Johns Hopkins University starts a full Data Science training. Why don’t we build a teal and do this together. Please comment on this post if you want to participate.

jhu_data_science

Let’s do this together !

Learn Data Science from one of the world’s top universities.
Johns Hopkins professors developed the Data Science Specialization to guide you from fundamental principles to advanced competency.
  • Gain hands-on Data Science experience with a Capstone Project
  • Showcase your knowledge with a Verified Certificate on your LinkedIn profile and resume
  • Adapt to your schedule with courses repeating monthly
  • Have unlimited retries for up to two years while available
Happy learning,
Coursera Team

Job – Universiteit Gent – PhD student – pricing and revenue management

universiteit_gent

PhD student

  • Last application date: Jan 23, 2015 09:00
  • Department: EB07 – Department of Marketing
  • Contract: Limited duration
  • Degree: Master’s degree in Business Engineering (or other master’s degree with profound background in statistics and data analysis)
  • Occupancy rate: 100%
  • Vacancy Type: Research staff

Job description

The research project will be conducted under the supervision of prof. dr. Dries Benoit and addresses new developments in pricing and revenue management. Many industries that depend on ticket sales started using yield management in order to improve occupancy and increase revenue. The models currently in use, however, often estimate price-response relationships with very basic functions. The goal of this research is to develop advanced Bayesian models and to apply these in the context of, e.g., soccer or movie theatres. You will engage in various research activities, including literature review, preparing and conducting multiple simulation as well as field experiments, interacting with organizations, conducting data-analysis, writing scientific articles and presenting at scientific conferences. This will allow you to prepare for and obtain a doctoral degree.

We offer:

  • A challenging job in an intellectually stimulating environment
  • Many opportunities for learning and development
  • Flexibility and autonomy
  • A full-time position for four years (pending positive evaluation after 12 months)
  • A gross monthly salary of 3.145 EUR and 38 days of paid vacation

The department is located at Tweekerkenstraat 2, Ghent, Belgium

Starting date is October 1, 2015

Profile of the candidate

We are looking for:

  • A master’s degree in Business Engineering (or other master’s degree with profound background in statistics and data analysis). Final-year students are also welcome to apply.
  • Excellent study results and master’s thesis
  • Good programming skills (e.g. R, Python, SQL, C, etc.)
  • Good writing skills, especially in English
  • A strong interest in research, especially in the domain of pricing and revenue management
  • An intellectually curious, analytical, responsible, and proactive person who is able to work autonomously as well as in team

How to apply

Feel free to contact prof. dr. Dries Benoit for any further information or questions.

To apply, send your motivation letter, resume, and a one-page summary of your master’s thesis to dries.benoit@ugent.be no later than January 23, 2014 (9am).

Please include your detailed study results (list of obtained points for every course per study year) and at least one reference that we may contact (e.g., thesis supervisor). The thesis summary should be in English; the letter and resume can be in English or Dutch.

Job – PhD Vacancy – LIRIS – Process Mining

KULeuven-logo-2012
Prof. Dr. Jochen De Weerdt has a PhD Vacancy – Process Mining (expected)
Jochen De Weerdt  I am looking for a PhD candidate in the area of Process Mining. Within the Leuven Institute for Research on Information Systems (LIRIS), you will be conducting research in an upcoming field that is situated at the intersection of business process management and data mining. For more details, see pdf-file below.

Vacancy – Process Mining

Download File

Project

The PhD will be doing research on the topic of Process Mining. Process mining techniques focus on process
discovery (extracting process models from event logs), conformance checking (comparing normative models
with the reality recorded in event logs), and extension (extending models based on event logs). It is a fastgrowing
field at the intersection of the business process management and data mining fields. From a
business viewpoint, process mining mainly has an edge in terms of objectivity because it allows to extract
business process knowledge from organisational data rather than going through the cumbersome and errorprone
process of interviews, workshops, etc. As such, process mining is gaining lots of traction in industry
because it is an excellent approach for driving business process improvements.
The LIRIS research group has plenty of experience with research in this area as demonstrated by several
ongoing and completed PhD projects in this area. Concretely, you will work on topics ranging from trace
clustering, active learning-based process mining, predictive process mining, conformance checking, to
hybrid process modelling and mining. You will develop prototype software implementations and will be
able to demonstrate their usefulness in practical settings through use cases in collaboration with industry
partners.

Training – KULeuven – Data Science in Practice – 5-6 February 2015

Data Science in Practice

INTRODUCTION

Modern information and communication technology is increasingly capable of collecting and generating large amounts of data that need to be analyzed to become useful or profitable. In fact, these amounts quickly become too large for immediate human understanding, leading to a situation in which “we are drowning in data but starved for knowledge”.

Data science represents an essential technology to transform such data into knowledge. It allows the automated discovery of interesting regularities or anomalies in large databases, thereby surpassing standard statistical summarizing. Typical tasks include the construction of predictive and descriptive models for classification, regression, clustering, associations, and probabilistic inference.

The DTAI research group of the department of Computer Science, KU Leuven, presents a course that provides a gentle introduction to data science for professionals who need to analyze data themselves, interpret results obtained using data science techniques, or give guidance to data analysts. The course introduces the principles, techniques and methodology of data science. It provides the attendants with an overview of the wide variety of data science techniques available, insight in which techniques are useful for what kind of tasks, expertise with practical data science tools, and real-life case studies.

The target audience of this course consists of professionals who experience a need for a better understanding of data science: which tasks can be solved, which techniques can be used, which are their strengths and weaknesses.

IMPORTANT DATES

Registration deadline: 20 January 2015
Course: 5-6 February 2015

Click here to register.

More info: http://dsip.cs.kuleuven.be/

 

 

European Presentation – Prof R. Zicari from Goethe University of Frankfurth – Bigdata & datadriven society

Roberto Zicari

Profesor Roberto V. Zicari (@odbmsorg), from the Goethe University  in Frankfurt recently gave a talk at Stanford on Big Data: http://ee380.stanford.edu

This is a very original presentation where bigdata is viewed from an European perspective: Big data a Data driven society

It contains information on the projects around  data driven innovation  from the European Commission.

If you are interested, this is the page with a link to download the presentation: http://www.odbms.org/2014/07/big-data-a-data-driven-society/

and you can watch the video on Youtube: http://www.youtube.com/watch?v=IBhu2kkZXGQ

New e-learning course Credit Risk Analytics by professor Bart Baesens

Baesens_Bart_small     Big Data World

Beste mensen van de Brussels Data Science Community,
Na 6 maanden werk eraan, is het zover!
Mijn E-learning cursus staat online op:
Laat gerust weten als je nog vragen zou hebben.
Vriendelijke groeten,
Bart

Prof. Dr. Bart Baesens
Faculty of Economics and Business
KU Leuven
Naamsestraat 69
B-3000 Leuven
Belgium

www.dataminingapps.com

Master of Information Management

————–

New e-learning course Credit Risk Analytics by professor Bart Baesens

The outline of the course is as follows:
Lesson 1: Introduction to Credit Scoring
Lesson 2: The Basel Capital Accords
Lesson 3: Preparing the data for credit scoring
Lesson 4: Classification for credit scoring
Lesson 5: Measuring the Performance of Credit Scoring Classification Models
Lesson 6: Variable Selection for Classification
Lesson 7: Issues in Scorecard Construction
Lesson 8: Defining Default Ratings and Calibrating PD
Lesson 9: LGD modeling
Lesson 10: EAD modeling
Lesson 11: Validation of Credit Risk Models
Lesson 12: Low Default Portfolios
Lesson 13: Stress testing
You are invited to send an email to Bart.Baesens@gmail.com if interested in more information.

You are invited – The business value of the ‘Internet of things’

The Belgian Internet of Things Community and the Brussels Data Science Community is happy to invite you to their seminar about IoT analytics that is taking place at the VUB on November 18th 18:30. You are welcome to join more that 200 experts for this exclusive & non-commercial event in Brussels.

IOT2

Interconnected sensors and objects will soon deliver big business benefits

The IoT (Internet of things) has the potential to transform business. Promising unprecedented connectivity between objects and the gathering of massive amounts of data, IoT will soon deliver significant business benefits to organizations. But how can these organisations prepare themselves and discover what opportunities and efficiencies IoT can bring to them.

Already the first projects have delivered positive ROI. Resource monitoring, usage pattern tracking, just-in-time delivery of goods and services — some IoT pioneers have launched or are deploying projects, and they’re seeing positive results.

But if your organization is looking to explore IoT as a business strategy, be warned that a number of technical and administrative challenges await you. Here’s a look at the opportunities, hurdles, and new skills required to make the most of this intersection of Web-enabled physical objects and the deluge of data they will bring.

 

What exactly is the Internet of things?

internet-of-things

At its heart, IoT is a wide-ranging ecosystem of everyday physical objects connected to the Internet, capable of identifying themselves and communicating data to other objects on the network.

The concept initially gained traction via the Auto-ID Center, a nonprofit collaboration of private businesses and academic institutions that created a Web-like infrastructure to track goods through the use of RFID tags that carry EPCs (Electronic Product Codes).

By Web-enabling just about any type of product or equipment (vehicles, construction equipment, gas and electric meters, appliances, vending machines, and so on) the IoT will allow information about these objects to be captured, resulting in a network of “smart objects” that can actively participate in a variety of business processes.

Fueling the IoT revolution is a combination of ubiquitous connectivity, low-cost sensors, and microelectronics that allow almost anything to be connected to the Internet. The greatest enabler of IoT applications for business may be the smartphone, with its ability to optically scan bar codes or RFID tags.

 

Business opportunities

IoT presents compelling business benefits, especially for organizations prepared to make the most of its stream of real-time data.

Cisco estimates that 50 billion devices and objects will be connected to the Internet by 2020. Yet today, more than 99 percent of things in the physical world remain unconnected. The growth and convergence of processes, data, and things on the Internet will make networked connections more relevant and valuable than ever before. This growth creates unprecedented opportunities for industries, businesses, and people .

digitaluniverse_wsj

 

EMC and IDC see the Internet of Things (IoT) creating new opportunities for business in five main ways:

4-9-14-Bill-Figure-1

  • New business models:  The IoT will help companies create new value streams for customers, institute processes that speed time to market, triage market performance, and respond rapidly to customer needs.
  • Real-time information on mission-critical systems:  With IoT, organizations can capture more data about their processes and products in a more timely fashion to create new revenue streams, improve operational efficiency, and increase customer loyalty.
  • Diversification of revenue streams:  The IoT can help companies create new services and new revenue streams on top of traditional products, e.g., vending machine vendors offering inventory management to those who supply the goods in the machine.
  • Global visibility:  The IoT will make it easier for enterprises to see across the business regardless of location, including tracking effectiveness and efficacy from one end of the supply chain to the other.
  • Efficient, intelligent operations:  Access to information from autonomous end points, as today’s smart grid already supplies to utility companies, will allow organizations to make on-the-fly decisions on pricing, logistics, sales, and support deployment, etc.

Challenges to address

The real benefits of the Internet of Things will not, however, be realized until leading companies develop the next generation of applications that address specific business needs from this wealth of data. It is within the Internet of Things that we’ll see a new generation of applications, such as:

  • Predictive maintenance:  predict when and how a device will fail and what replacement and maintenance parts and service personnel skills will be required to preempt the failure
  • Loss prevention:  monitor device and network usage to flag unusual usage situations that may be indicators of revenue loss and theft
  • Asset utilization:  monitor and predict asset utilization under a number of different usage scenarios in order to improve asset, device, and node utilization
  • Inventory tracking:  monitor inventory levels and inventory assets to minimize loss and waste and improve inventory utilization
  • Disaster planning and recovery:  model different disaster scenarios and likely device and network usage requirements to proactively plan for disaster situations (e.g., hurricanes, ice storms, tornados, earthquakes, Zodiac Killers)
  • Downtime minimization:  leverage predictive maintenance and inventory tracking to identify high-probability downtime situations and ensure that the right maintenance and replacement parts are available, as well as the right skilled service personnel
  • Energy usage optimization:  optimize energy usage given current and historical device performance, historical and predicted energy costs, and device performance requirements
  • Device performance effectiveness:  monitor and optimize individual device performance/throughput based upon historical performance, given certain workloads and environmental conditions and coupled with a detailed profile of the performance behaviors of that device or node
  • Network performance management:  monitor and manage/fine-tune the performance of a network of devices given current load, required performance requirements (service level agreements), and forecasted performance requirements
  • Capacity utilization:  reallocate device resources and jobs to optimize network and device performance given the history of device interactions and current and forecasted performance requirements
  • Capacity planning:  predictive and prescriptive analytics that model product and device usage and in real time, make resource allocations and automate the provisioning of new capabilities (turning on and off capacity as dictated by the predictive models) in order to ensure the required capacity at the optimal price
  • Demand forecasting:  leverage device behavioral models, actual usage patterns and trends, and external factors (weather, traffic, events) to forecast longer-term network configurations and product and network build out
  • Pricing optimization:  understand device usage patterns, coupled with demand forecasting, to optimize device and network pricing—lowering pricing when demand is low and increasing pricing when demand is higher; almost like surge pricing, but hopefully without the same customer satisfaction issues
  • Yield management:  optimizing device and network usage to extract the most value out of the overall network capacity and capabilities
  • Loading balancing optimization:  balancing usage load in light of forecasted demand to ensure that all nodes are being utilized equally and to avoid performance bottlenecks

 

Is your organisation ready, are you ?

While the technology challenges in capturing, managing, backing up, securing, and analyzing these huge volumes of real-time data are significant, the biggest challenges are, first and foremost, organizational. Here are three steps all organizations must contemplate to prepare for these monetization opportunities:

  • Create a C-level position in charge of identifying and overseeing new digital business opportunities:  whether it involves creating a new position (e.g., Chief Digital Officer), or an enhancing an existing one (e.g., upgrading the CIO’s current responsibilities), the executive would be responsible for identifying and pursuing new analysis-driven revenue streams.
  • Develop and continuously revise an executive-team understanding of the new digital landscape for your enterprise:  who are the new (and potential) digital competitors? How are you going to cooperate with others in your industry to anticipate and thwart digital disruption? What are the short- and long-term steps you must take to ensure a smooth and timely digital transformation?
  • Design and execute a plan for accelerated investments in digital enterprise technologies and skills:  re-allocate resources across the business based on key business initiative, invest in promising data collection and analysis areas, and identify the gaps in talent and skills required for success in the era of the Third Platform.

Increased investment in human capital and the new skills required today is the first order of business for all organizations. However, organizations need to invest the time to make sure where and how to start; to drive collaborate between IT and key business executives to understand or envision the realm of what’s possible with respect to these new sources of data and advanced analytics. And organizations need to integrate and/or expand their user experience expertise and capabilities in order to exploit the move to the third platform.

Remember: organizations don’t need a Big Data strategy to exploit the digital universe; organizations need a business plan that integrates the data and capabilities enabled by Big Data and the digital universe.

 

Your next step:

Join or meetup on November 18th.

Data Science and the Internet of Things

Tuesday, Nov 18, 2014, 6:30 PM

VUB – Aula QD
Pleinlaan 2B – 1050, Brussels Brussels, BE

110 Business & Data Science pro’s Attending

In collaboration with the IoTBE – the Belgian Internet of Things CommunityAgenda:18:30 – Update on our Data4Good  activities with Doctors without borders (MSF)18:45 – Update on our Hands-On session about Social Network Analysis19:00 – Presentation – The Internet of Things & Data Sciences – Mr Frederik Santens (IoT be group)19:30 – Presentatio…

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