Data Science Trainings Belgium

Datascience - Training calendar datascience training

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The European Data Innovation Hub facilitates  a full series of Data Science and Big Data training programmes organized by its partners.

You can expect

  • a series of executive training to support your management in understanding the benefits of analytics
  • a series of coached MOOCs on machine learning and big data technology
  • a series of hands-on training on the different datascience technologies

All members of the European Data Science and Big Data communities are welcome to use our Brussels based professional facilities to give their training. The members of the hub will promote your training and include it on our e-learning platform for further use.

The full list is available here.

Here are some highlights for the coming months:

Check out the full agenda here.

How to get the best price:

You can always use Eventbrite to order and pay for your ad-hoc trainings but if you want to benefit from volume discounts then you could contact Philippe on 0477/23.78.42 | pvanimpe@dihub.eu .

Have you been to our Meetups yet ?

Each month we organize a Meetup in Brussels focused on a specific DataScience topic.

Brussels Data Science Meetup

Brussels, BE
1,608 Business & Data Science pro’s

The Brussels Data Science Community:Mission:  Our mission is to educate, inspire and empower scholars and professionals to apply data sciences to address humanity’s grand cha…

Next Meetup

IBM Bluemix and Analytics – Introduction

Tuesday, Feb 9, 2016, 6:30 PM
22 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

datasiencebe.com

Free Seminar: Science and Marketing meetup – October 16 2014 – VUB

Join us for our free monthly meetup at the VUB.

How can we use data science skills to serve our customers better and to convert more prospects.

Speakers:

Dhr Wouter Verbeke: 

Developments in customer churn prediction modeling: social network analysis & optimizing returns of retention campaigns.

Dhr Patrick Glenisson

Real live examples of the use of datascience tools en techniques.

Dhr Rik Van Bruggen

Using graph technology to identify potential communities, real live demo.

Dhr Frank Vanden Berghen

How to solve assignment problem for cross-selling campaigns.

Agenda:

18:30 Update on our first data for good hackathon activities for Doctors without Borders

19:00 Dr Wouter Verbeke: 

Developments in customer churn prediction modeling: social network analysis & optimizing returns of retention campaigns

19:25 Dhr Patrick Glenisson

Real live examples of the use of datascience tools en techniques.

19:50 Short break

20:00 Dhr Rik Van Bruggen

Using graph technology to identify potential communities, real live demo.

20:25 Dhr Frank Vanden Berghen

How to solve assignment problem for cross-selling campaigns.

21:00 Round Table, Question & Answers

21:30 up we go to the KultuurKaffee

Next Meetup in Brussels – Oct 16th, 2014 – Marketing and Datasciences

Marketing & DataScience Meetup Oct 16, 2014.

Marketing & DataScience Meetup Oct 16, 2014.

 

Data Science and Marketing

Thursday, Oct 16, 2014, 7:00 PM

VUB – lokaal D.0.02 – max 170 plaatsen !
Pleinlaan 2 B-1050 Brussel Brussels, BE

32 Business & Data Science pro’s Attending

How can we use data science skills to serve our cutomers better and to convert more prospects.

Check out this Meetup →

How can we use data science skills to serve our customers better and to convert more prospects.

Speakers:

Dr Wouter Verbeke: 

Developments in customer churn prediction modeling: social network analysis & optimizing returns of retention campaigns.

Dhr Patrick Glenisson

Real live examples of the use of datascience tools en techniques.

Dhr Rik Van Bruggen

Using graph technology to identify potential communities, real live demo.

Dhr Frank Vanden Berghen

How to solve assignment problem for cross-selling campaigns.

Agenda:

18:30 Update on our first data for good hackathon activities for Doctors without Borders

19:00 Speaker 1

19:25 Speaker 2

19:50 Short break

20:00 Speaker 3

20:25 Speaker 4

21:00 Round Table, Question & Answers

21:30 up we go to the KultuurKaffee

 

 

 

Graphs for HR Analytics by Rik Van Bruggen

 

Graphs for HR Analytics

Yesterday, I had the pleasure of doing a talk at the Brussels Data Science meetup. Some really cool people there, with interesting things to say. My talk was about how graph databases like Neo4j can contribute to HR Analytics. Here are the slides of the talk:

I truly had a lot of fun delivering the talk, but probably even more preparing for it.

My basic points that I wanted to get across where these:

  • the HR function could really benefit from a more real world understanding of how information flows in its organization. Information flows through the *real* social network of people in your organization – independent of your “official” hierarchical / matrix-shaped org chart. Therefore it follows logically that it would really benefit the HR function to understand and analyse this information flow, through social network analysis.
  • In recruitment, there is a lot to be said to integrate social network information into your recruitment process. This is logical: the social network will tell us something about the social, friendly ties between people – and that will tell us something about how likely they are to form good, performing teams. Several online recruitment platforms are starting to use this – eg. Glassdoor uses Neo4j to store more than 70% of the Facebook sociogram – to really differentiate themselves. They want to suggest and recommend the jobs that people really want.
  • In competence management, large organizations can gain a lot by accurately understanding the different competencies that people have / want to have. When putting together multi-disciplinary, often times global teams, this can be a huge time-saver for the project offices chartered to do this.

For all of these 3 points, a graph database like Neo4j can really help. So I put together a sample dataset that should explain this. Broadly speaking, these queries are in three categories:

  1. “Deep queries”: these are the types of queries that perform complex pattern matches on the graph. As an example, that would something like: “Find me a friend-of-a-friend of Mike that has the same competencies as Mike, has worked or is working at the same company as Mike, but is currently not working together with Mike.” In Neo4j cypher, that would something like this
 match (p1:Person {first_name:"Mike"})-[:HAS_COMPETENCY]->(c:Competency)<-[:HAS_COMPETENCY]-(p2:Person),  
 (p1)-[:WORKED_FOR|:WORKS_FOR]->(co:Company)<-[:WORKED_FOR]-(p2)  
 where not((p1)-[:WORKS_FOR]->(co)<-[:WORKS_FOR]-(p2))  
 with p1,p2,c,co  
 match (p1)-[:FRIEND_OF*2..2]-(p2)  
 return p1.first_name+' '+p1.last_name as Person1, p2.first_name+' '+p2.last_name as Person2, collect(distinct c.name), collect(distinct co.name) as Company;  
  1. “Pathfinding queries”: this allows you to explore the paths from a certain person to other people – and see how they are connected to eachother. For example, if I wanted to find paths between two people, I could do
 match p=AllShortestPaths((n:Person {first_name:"Mike"})-[*]-(m:Person {first_name:"Brandi"}))  
 return p;  

and get this:

Which is a truly interesting and meaningful representation in many cases.

  1. Graph Analysis queries: these are queries that look at some really interesting graph metrics that could help us better understand our HR network. There are some really interesting measures out there, like for example degree centrality, betweenness centrality, pagerank, and triadic closures. Below are some of the queries that implement these (note that I have done some of these also for the Dolphin Social Network). Please be aware that these queries are often times “graph global” queries that can consume quite a bit of time and resources. I would not do this on truly large datasets – but in the HR domain the datasets are often quite limited anyway, and we can consider them as valid examples.
 //Degree centrality  
 match (n:Person)-[r:FRIEND_OF]-(m:Person)  
 return n.first_name, n.last_name, count(r) as DegreeScore  
 order by DegreeScore desc  
 limit 10;  
   
 //Betweenness centrality  
 MATCH p=allShortestPaths((source:Person)-[:FRIEND_OF*]-(target:Person))  
 WHERE id(source) < id(target) and length(p) > 1  
 UNWIND nodes(p)[1..-1] as n  
 RETURN n.first_name, n.last_name, count(*) as betweenness  
 ORDER BY betweenness DESC  
   
 //Missing triadic closures  
 MATCH path1=(p1:Person)-[:FRIEND_OF*2..2]-(p2:Person)  
 where not((p1)-[:FRIEND_OF]-(p2))  
 return path1  
 limit 50;  
   
 //Calculate the pagerank  
 UNWIND range(1,10) AS round  
 MATCH (n:Person)  
 WHERE rand() < 0.1 // 10% probability  
 MATCH (n:Person)-[:FRIEND_OF*..10]->(m:Person)  
 SET m.rank = coalesce(m.rank,0) + 1;  

I am sure you could come up with plenty of other examples. Just to make the point clear, I also made a short movie about it:

The queries for this entire demonstration are on Github. Hope you like it, and that everyone understands that Graph Databases can truly add value in an HR Analytics contect.

Feedback, as always, much appreciated.

Rik