TOP 6 – 2h Technical workshops during #DISUMMIT

Technical Workshops di-Summit

In addition to the series of management workshops and over 40 presentations, we’d like to bring to your attention that in addition to our excellent line-up of speakers, you may be interested in one of our technical workshops that provide hands-on training:

  1. Pieter Buteneers – Data Strategist and Machine Learning Consultant  – Hands-on Machine Learning with Python
  2. Geert Verstraeten – Managing Partner at Python Managing – Projects in Predictive Analytics
  3. Thierry Turpin and Stephane Tridetti: Optimize DHS with AWS
  4. Faisal Orakzai & Morad Masnaoui: BigData Architecture
  5. Nicolas Deruytter – Managing Director at DataTonic – Hands-on  Workshop  Using  Tensorflow
  6. IBM: Watson Data Platform –  Ann-Elise Delbecq Niko Tambuyser Willem Hendriks Luc Goossens
  • Please check disummit.com form more exciting presentations.

These are only some of the technical workshops we have available.

Thank you to our trainers for sharing their knowledge with us: Pieter Buteneers, Geert Verstraeten, Thierry Turpin, Stéphane Tridetti, Faisal Orakzai, Morad Masnaoui, Nicolas Deruytter, Ann-Elise Delbecq, Niko Tambuyser, Willem Hendriks & Luc Goosens

 

#disummit – Executive Workshops – Brussels 30 March – Register Now

Executive WorkshopsDuring this year’s summit you will hear stories on how data is used to build a better world.

On March 30th we have invited an impressive lineup of top speakers and prepared in depth training classes for managers and data scientists.

We are also offering you 5 exclusive C-Level workshops:

  • 29 March 16:00 – 3h Workshop from Dirk BorneCommunicating Data Literacy and the Value of Data to Clients and Colleagues
  • 30 March 10:45 – 2h Workshop from Geert VerstraetenIntroducing Predictive Analytics – more
  • 30 March 13:45 – 1h Workshop from Natalino BusaPositioning Open Source in your existing software architecture
  • 30 March 13:45 – 1h Workshop from Stephen BrobstLeadership and Digital Transformation – more
  • 30 March 17:00 – Closing Keynote from Kirk BorneA Data-rich World for a Better World: From Sensors to Sense-Making
  • Please check disummit.com form more exciting presentations.

Summer Data Science activities in Belgium.

summer edition

We wish you happy holidays, in case you get bored check out our educational channel on youtube.

The European Data Innovation Hub is active during the summer.
Here is a short update from what to expect in the coming weeks:

Thank you for supporting the European Data Innovation Hub, we had a great academic year.

Philippe Van Impe
@pvanimpe
www.di-academy.com

Please forward the information about the data science bootcamp to your peers and friends.

job – Python Predictions – data scientists.

Screenshot 2016-06-30 13.51.09

Hi Philippe,

We’re looking for some great new people again.
Would be great if you could give us some visibility for our search.
Candidates can simply send CV and (e)mail of motivation to jobs@pythonpredictions.com
More details in the links or text below
Thanks!!!
Geert
Data Scientist
Python Predictions – Bruxelles Woluwe-Saint-Pierre
Python Predictions is a Brussels-based consulting firm founded in 2006 and specialized in data science and predictive analytics. We are currently looking for data scientists.

Responsibilities

  • In-company data science projects for our clients
  • Contribute to explorative, descriptive and predictive analysis

Required skills or education

  • Proven interest and skills in data science and analytics
  • Proven interest and skills in at least one analytical programming language
  • Work flexibly in rapidly changing environments
  • Good visualisation and communication skills
  • Understand business problems

Personality

  • Analytical mindset
  • Open minded
  • Integrity
  • Critical of the output produced

Language skills

  • Working knowledge of Dutch, French and English

How to apply?
Send us your curriculum vitae and brief letter of motivation.We need both documents in order to consider your application.

More details
http://pythonpredictions.com/jobs/come-mine-with-us/

About us
Why should you apply for a position at Python Predictions? We believe we understand as no others what makes analysts tick. We believe that successful analysts must possess and develop a number of very distinct skills, ranging from social to technical, from intuitive to analytical. Putting these skills to work on real-life analytical projects is rewarding. And we provide a stimulating environment with focus on innovation and cooperation. Find our more about our activities on www.pythonpredictions.com

Job Type: Full-time

Required languages:

  • Dutch
  • English
  • French

Blog – Predictive Analytics – a Soup Story by Geert Verstraeten

geert 1brasserie octopus

Predictive Analytics – a Soup Story

A simple metaphor for projects in predictive analytics 

By: Geert Verstraeten, Predictive Analytics advocate, Managing Partner and Professional Trainer, Python Predictions

The analytical scene has recently been dominated by the prediction that we would soon experience an important shortage of analytical talent. As a response, academic programs and massive open online courses (MOOCs) have sprung up like mushrooms after the rain, all with the purpose of developing skills for the analyst or its more modern counterpart, the data scientist. However, in the original McKinsey article, the shortage of analytics-oriented managers was predicted to become ten times more important than the shortage of analysts[1]. But how do we offer relevant concepts and tools to managers without drowning our ‘sweet victims’ in technology and jargon?

For managers, most analytics training falls short in a critical way. The vast majority of newfound analytics training focuses on core analytics algorithms and model building, not on the organizational process needed to apply it. In my opinion, the single most important tool for any manager lies in understanding the process of what should be managed. The absolute essence when asked to supervise predictive analytical developments lies in having a solid understanding of the main project phases. Obviously, we are not the first to realize that this is vital. Tools have been developed to describe the process methodology for developing predictive models[2]. However, it is difficult for non-experts to become excited about these tools, as they describe phases in a rather dry way.

We have experimented with different ways to present process methodology in a more fun and engaging way. Today, we no longer experiment. In our meetings and trainings with managers, we present the development of analytical models as simple as the process of making soup in a soup bar.

Project definition

geert phase 1  This first phase is concerned with understanding the organization’s needs, priorities, desires and resources. Taking the order basically means we should start by carefully exploring what it is that we need to predict. Do we want to predict who will leave our organization in the next year, and if so – how will we define this concretely? At this time, when the order becomes clear, it is time to check the stock to make sure we will be able to cook the desired dish. This is equivalent to checking data availability. Additionally, it is important to have an idea about timing: will our client need to leave timely in order to catch the latest movie? This is pretty similar to drawing a project plan.

Data preparation

geert phase 2The second phase deals with preparing all useful data in a way that they are ready to be used subsequently in the analysis. For those not familiar with (French) cooking jargon, mise en place is a term used in professional kitchens to refer to organizing and arranging the ingredients (e.g. freshly chopped vegetables, spices, and other components) that a cook will require for his shift[3]. Data are for predictive analytics what ingredients are for making soup. In predictive analytics, data are gathered, cleaned and often sliced and diced such that they are ready to be used in a later analytical stage.

Model building

gert phase 3The main task in cooking the soup lies in choosing exactly those ingredients that blend into a great result. This is no different in predictive modeling, where the absolute essence lies in selecting those variables that are jointly capable of predicting the event of interest. One does not make a great soup with only onions. Obviously, not only the presence of ingredients is relevant, also the proportions in which they are used – compare this to the parameters of predictors: not every predictor is equally important for obtaining a high quality result. Finally, cooking techniques matter just as much as algorithms do in predictive analytics – they represent essentially different ways to combine the same data into the best soup.

Model validation

geert phase 4In cooking it is crucial to taste a dish before it is served. This is very similar to model validation in predictive model building. Both technical and business relevant measures can be used to objectively determine whether a model built on a specific data set will hold true for new data. As long as the soup does not taste well, we can iterate back to cooking, until the final soup is approved – i.e. the champion model is selected.

Model usage

geert phase 5This phase is all about presentation and professional serving. A great soup served in an awful bowl may not be fully appreciated. The same holds true for predictive models – a model with fantastic performance may fail to convince potential users when key insights are missing. Drawing a colorful profile of the results may prove instrumental in convincing the audience of the model’s merit. If done successfully, this will likely result in an in-field experiment, for example designing a set of retention campaigns targeting those with the highest potential to leave. At that point, the engaged analyst should check in whether the meal is enjoyed.

Conclusion
title

This simple, intuitive process has been important to us to allow managers to engage in the process in a fun way. Presenting the process in a non-technical way makes the process digestible (to be fair, I’ve stolen this phrase from my friend Andrew Pease, Global Practice Analytics Lead at SAS because it makes such great sense in this context). However, it should remain clear that it is only a metaphor. At some point, building predictive models is obviously also different that making soup. Every phase, especially project definition, involves many more components than those where a link with soup can be found. But the metaphor gets us where we want to be – a point where a discussion is possible on what is needed to develop predictive models, and where a minimum of trust can exist: it ensures that we get on speaking terms with decision makers and all those who will be impacted by the models developed.

Notes and further reading

brasserie octopusWe fully realize this is not completely different from CRISP-DM, the Cross Industry Standard Process for Data Mining, which has been developed in 1996, and is still the leading process methodology used by 43% of analysts and data scientists. However, except if you are a veteran and/or an analyst, it is difficult to get really excited about CRISP-DM or its typical visualization. For those looking for a more in-depth understanding of the process, I recommend reading the modern answer to CRISP-DM, the Standard Methodology for Analytical Models (by Olav Laudy, Chief Data Scientist, IBM).

[1] In a previous post, we have also argued that the analytics-oriented manager is main lever for success with predictive analytics.

[2] for the sake of clarity: a predictive model is a representation of the way we understand a phenomenon – or if you will, a formulaic way to combine predictive information in a way to optimally predict future behavior or events.

[3] see the Wikipedia definition of mise en place

About Geert

Geert VerstraetenGeert Verstraeten is Managing Partner at Python Predictions, a niche player in the domain of Predictive Analytics. He has over 10 years of hands-on experience in Predictive Analytics and in training predictive analysts and their managers. His main interest lies in enabling clients to take their adoption of analytics to the next level. His next training will be organised in Brussels on October 1st 2015.

 

Gratitude goes to Eric SiegelAndrew Pease and our team at Python Predictions for delivering great suggestions on an earlier version of this article. All remaining errors are my own.

Link to the next training details from Geert.

Video

Job – Python Predictions – JUNIOR OR SENIOR ANALYST

Python Predictions
Geert Verstraeten Geert Verstraeten, managing Partner at Python Predictions is looking for Analytical Talents !
Due to a growing demand in our services, we are again looking for analytical talent. Why should you apply for a position at Python Predictions? We believe we understand as no others what makes analysts tick. We believe that successful analysts must possess and develop a number of very distinct skills, ranging from social to technical, from intuitive to analytical. Putting these skills to work on real-life analytical projects is rewarding. And we provide a stimulating environment with focus on innovation and cooperation.
View this announcement in Prezi

What makes you special

  • You believe in analytics: You understand that organizations today can and should make the most of the available information, in order to turn this information into value. You are deeply interested in analytics, and have required analytical skills.
  • You have a feeling for Marketing, Risk or Operations: At least one of these domains really interests you. You have gained experience in one of these domains, and you want to apply and further develop your domain understanding.
  • You communicate: You are able to explain technical items in a non-technical way, you are open-minded and have no fear of expressing your opinion in a constructive way. Summarized, you communicate easily within internal or external teams.
  • You are driven by results: You are motivated by obtaining concrete deliverables and results. You want to make a difference for the organizations you work for, and you like to be recognized for your input.

What makes us special

  • Our focus on Predictive Analytics: Predictive Analytics is key in every single project. We passionately apply advanced analytics to help create and finetune organizations’ actions and strategy.Typical projects include: Profitability Analysis & Segmentation, Customer Cloning, Response Modeling, Customized Offers, Churn modeling, Credit Scoring, Fraud Detection, Forecasting…
  • Our focus on innovative applications: Each business problem deserves a well-considered solution. In each challenge, we focus on delivering high-quality custom-made solutions that seamlessly fit the business requirements. In this way, our projects offer great job variety. And we like to take on new challenges.
  • A young and dynamic team: We work closely together in a young and dynamic team, where projects are considered as mutual challenges, and where merit is based on contribution, skills, creativity and enthusiasm.
  • Our long-term references: A large number of our projects naturally evolve into long-term relationships. We are considered as trusted adviser for companies in banking and insurance, telecommunications, retail, mail-order, postal services, energy and utilities.

What are we looking for

JUNIOR ANALYST
Main tasks

  • contribute to advanced analytical projects
  • cooperate closely with senior analysts

General skills

  • motivation to learn new skills and tools
  • team player with good communication skills
  • critical on own output
  • understanding of business relevance

Specifics

  • university degree (or similar by experience) with proven interest in analytics
  • project experience in analytics is a plus
  • knowledge SAS or IBM SPSS Modeler or R

SENIOR ANALYST
Main tasks

  • lead advanced analytical projects
  • translate business problems into concrete projects
  • interest to develop new opportunities

General skills

  • team player with good communication skills
  • critical on own output
  • understanding of business relevance

Specifics

  • university degree (or similar by experience) with proven interest in analytics
  • project experience in analytics
  • good knowledge SAS or IBM SPSS Modeler or R

Apply:

Make sure that you are a member of the Brussels Data Science Community linkedin group before you apply. Join  here.

Please note that we also manage other vacancies that are not public, if you want us to bring you in contact with them too, just send your CV to datasciencebe@gmail.com .

Here is the link to the original job add.

Please apply by sending your CV and brief letter of motivation to jobs@pythonpredictions.com

 

Why don’t you follow these courses to boost your datascience skills ?

The European Data Innovation Hub organizes trainings and hands-on workshops about bigdata and datascience.

Here is the agenda:

Data Innovation Training Hub

Brussels, BE
51 Fast learners

This group is an initiative from the nonprofit European Data Innovation Hub, the premier networking space where business, startup, academic and political decision makers share…

Next Meetup

Coached Mooc – Introduction to Big Data with Apache Spark

Tuesday, Jun 2, 2015, 7:00 PM
21 Attending

Check out this Meetup Group →

 

Data Innovation Summit – Datascience made in Belgium

333

Statistics about  the Summit (5/3/2015):

  • 12 hour Live streaming of the Summit
  • 6 cameras, 3 camera teams
  • 7 journalists
  • 59 Speakers,
  • 1 Deputy Prime Minister
  • 1 Rector
  • 22 CEO
  • 10 Professors
  • 15 Start-ups
  • 333 Participants
  • 22 sponsors
  • 1 time keeper
  • main exhibition area
  • vast networking area
  • 1 VIP area
  • 6 main tracks

Agenda:

  1. The Data Innovation Ecosystem
  2. The Enterprise Business Cases
  3. The Innovation Facilitators
  4. The University Challenge
  5. The Startup Sessions
  6. The Open Ignite Sessions

Confirmed speakers:

Kris Peeters | Vincent Blondel |   Philippe Van Impe |Hans Constandt | Toon Vanagt | Damien Bourgeois | Steven Noels | Elena Tsiporkova | Geert Verstraeten | Jan Sonck | Els Descheemaeker | Steven Beeckman | Laurent Fayet | Julie Coyette | Guillaume Gorge | Rik Van Bruggen | Wim Van Leuven | Jonathan Duplicy | Dries Benoit | Erik Mannens |Roald Sieberath | Thibaut Claes | Bart Hamers | Wannes Meert | Dieter Devlaminck | Andrea Dal Pozzolo | David Martens | Daniele Marinazzo | Renaud Lambiotte | Daniel Berckmans | Omar Mohout | Filip Maertens | Kristof Mertens | Mark Turcksin | Daan Gerits | Wannes Rosiers | Eric Charles | Katya Vladislavleva | Erik Laurijssen | Mehdi El Fadil | Kris Peeters | Henk De Metsenaere | Jonas Tundo | Istvan Hajnal | Eric Lecoutre |  Cain Ransbottyn | Sebastien Leempoel | Sam Rédelé | Antoon Dierick | Zbigniew ‘Zibi’ Paszkiewicz | Jochen François | Matthias Vallaey | Tijmen Weddepohl | Frank Vanden Berghen | Ferdinand Casier |

Companies presenting:

Axa euroclear_0 Agoria

sas-logo Keyrus

abl artsen zonder grenzen AXA Belga datainnovation Essent  proximus PwC_fl_c Sign2pay  startups.be swan insight yazzoom Agoria ARHS-Data Arrow-Group2 Bigdata.be Big-Industries-stamp-logo Business Insight Colruyt_logo  datalayer Dataminded_Logo_transparant_96px_0 datylon Deloitte-logo DLA Piper esperity Essent_Logo_NL  evolved-analytics finaxys Graph  iMinds_logo_CMYK Infofarm Informatica-logo Innoviris KBC Startit logo_businessdecision  Logo_Vlaanderen logo-sirris mathworks  microsoft microstrategy Neo4j 1 NG-DATA-logo ontoforce opinum 1 opinum pépite porphyrio predicube proximus_icon Python Predictions sentiance StartUps.be tangent works tribalytics-logo

 

More info:

Sponsors of the Data Innovation Summit – Brussels

This event is organized by

Brussels Data Science Community

We love doing data for good

Structural Summit Partners

Axa  Agoria  Euroclear

Academic Partners

ucl_logo KUL ULB  Ugent   ULG   UMONS   UNamur   Universiteit Antwerpen   vub_0

Summit Sponsors

sas-logo Keyrus

Exhibitors

Business Insight   logo_businessdecisionBig-Industries-stamp-logo  Arrow-Group2    finaxys  datalayer   Infofarm   Keyrus   mathworks microstrategy   neo4j   pépite    sentianceDeloitte-logoRIAInformatica-logo Dataminded_Logo_transparant_96px_0

Job-flash partners