Phase 1: 7w Coding Summer Camp

This is a 7 weeks coding camp where the candidates  come to DigitYser to work on assignments based on the DataCamp e-learning platform. The mode is reversed classroom; The students will be assisted by a coach that is there to boost the efficiency of the group and answer the specific questions of the students. 

Python Track: 1 to 6

  • Intro to Python for
  • Data Science
  • Intermediate Python for Data Science
  • Python Data Science Toolbox (Part 1)
  • Python Data Science Toolbox (Part 2)
  • Importing Data in Python (Part 1)
  • Importing Data in Python (Part 2)

Special SAS week

Python track 7-10:

  • Cleaning Data in Python
  • pandas Foundations
  • Manipulating DataFrames with pandas
  • Merging DataFrames with pandas

Python Track 11-14:

  • Intro to SQL for Data Science
  • Introduction to Databases in Python
  • Introduction to Data Visualization with Python
  • Interactive Data Visualization with Bokeh

Python Track:

  • Statistical Thinking in Python (Part 1)
  • Statistical Thinking in Python (Part 2)
  • Joining Data in PostgreSQL

R Track (5 trainings because one week) – 20h

  • Introduction to R
  • Intermediate R
  • Introduction to the Tidyverse
  • Importing Data in R (Part 1)
  • Importing Data in R (Part 2)

Finally! After hard work of finding, cleansing, analyzing, crunching and modeling your data, you’ve finally discovered great insights. Beautiful gems that will make the world a better place.

Now is the time to share and present them. Now is the time to make them shine with importance and significance.

But presenting data analysis results can quickly become tricky.

 Did you know, most of the time, data-oriented presentations are boring to non-data experts? (At least for the first 40 minutes!)

Did you know this was mostly due to three misconceptions most data scientists have about presenting?

In this lecture, we’ll explore those three misconceptions: “people will get what I say,” “people will get what I show,” and “people will learn more if I spice it up.”

We’ll dig into the reasons why those three misconceptions are indeed, misconceptions.

This will lead us to the land of the learning process, with a specific focus on our different kinds of memory systems, on the brain communication channels, and on attention and distractions.

“The most common communication mistakes? Relating too much information, with not enough time devoted to connecting the dots” – John Medina, Brain Rules

Come have lunch and discover what prevents you from effective presentation of your gems, and how to make them shine in everybody’s eyes.

Who’s the lecturer?

Alexis is a pure blend of art and science: Computer Science & IT engineer, presentation skills and inter-personal communication trainer and coach, magician, corporate speaker, master of ceremonies, and improv theater comedian.

For the past 15 years, the common denominator of Alexis works has been about making people understand each other and work together. As such, he has put rules and principles about communication, graphic design, storytelling or neuroscience into practice to help non-experts understand technical topics and to help both experts and non-experts speak the same language and work better as a team.

Find his blog post here

Python Track 18 – 22:

  • Supervised Learning with scikit-learn
  • Machine Learning with the Experts: School Budgets
  • Unsupervised Learning in Python
  • Deep Learning in Python
  • Network Analysis in Python (Part 1)

When it comes to data visualization, it often comes to being either pretty or true.

What if there was a third way?

One where a data visualization is both appealing to your audience and showing accurately the underlying data to help you make your point.

Good news: that third way exists! 

In this lecture, we’ll explore what a good visualization is (spoiler: creating it with ggplot or plotly can only make it well built!) and we’ll redefine ‘pretty‘ in terms of laws of perceptions, i.e. how our brains see and make sense of what they see, rather than on frameworks and tools.

We’ll continue by building on the fundamental principles of graphic design that allows to create a good visualization, one that captivates your audience, guides them into understanding your insights, and helps you persuade them to act or change.

“Good visual communication should be used not just to produce better answers, but also create better conversations.” – Scott Berinato, Good Charts

Come have lunch and discover what will make your visualization start conversations about the data and insights, rather than understanding the chart that is displayed.

 Who’s the lecturer?

Alexis is a pure blend of art and science: Computer Science & IT engineer, presentation skills and inter-personal communication trainer and coach, magician, corporate speaker, master of ceremonies, and improv theater comedian.

For the past 15 years, the common denominator of Alexis works has been about making people understand each other and work together. As such, he has put rules and principles about communication, graphic design, storytelling or neuroscience into practice to help non-experts understand technical topics and to help both experts and non-experts speak the same language and work better as a team.

Link to his blogpost

The web contains a lot of data that could be useful to add to our analysis: exchange, tweets, movie ratings, … Sometimes it is accessible through API’s, but in other cases you need to grab it from the actual pages.

The workshop will introduce getting data from APIs (in json and xml format), and scraping it from web pages in case no API is available. And because not everything should just be scraped, we’ll also cover some scraping etiquette.

Welcome to the first Data Science Jobfair of the Summer.

We are excited to introduce to your our talented Summer Coding Camp & Data Science Bootcamp participants!

This is the perfect opportunity for companies to meet their future members of their DataScience Team.

Program:

  • 15:30 – Arrival at DigitYser & Visit of the building
  • 16:00 – Presentation of the Summer Coding Camp & Data Science Bootcamp
  • 16:15 – Presentation of the companies eager to hire candidates and to sponsor the DataScience program.
  • 17:00 – 2′ presentation of each participant to the bootcamp.
  • 17:30 – Networking & One-on-One meetings candidate / companies.
  • 18:30 – End of the event & Start of the HIVHACK workchop

Each company can bring their stand or use one of our high tables in the Atrium. This event is free for our Training Partners and Innovation Partners.

We had the launch and 4 excellent workshops introducing us to the problem and 2 datasets and we are adding a third one about the viral load.

The information is available on our website hivhack.org and also stored on https://www.facebook.com/hivhack/ .

We have a forum to discuss the different issues https://hivhack.org/forum/ & https://www.facebook.com/hivhack

In the previous workshop Jenny explained the content of her global dataset and Serge explained what he did to get more information on Tanzania.

Our next workshop will be about going into more depth on these 2 different datasets and show some examples of data visualisation.

Jente also found excellent data in Kenia, he will present a study done in 2016 about HIV.

We also invite people to help us gathering more datasets and to step up and present what they have found.

Agenda:

17:00 – Opening doors for the teams to work on the data

19:00 – Presentation of the first examples

20:00 – Lunch and drinks

Check the Forum: https://hivhack.org/forum/

HIVHACK.org

As a data scientist, you work hard on finding hidden gems in your data sets. (Cleaning them is hard enough!)

Alas, even an analysis genius who found the best gems will be stuck if s/he cannot make the results and insights talk to non-experts (business people, marketing team, CxOs.)

Your gems will only perceived as plain dull rocks. And that’s a shame!

As most experts, data scientists suffer from a specific curse: the curse of knowledge. That is not knowing anymore what it is to not have the knowledge you now do have.

Fortunately, there is a cure to that curse! Using principles, methods, and tools, it is possible to defeat the curse of knowledge by crafting the right message for your audience, and by making it understandable and memorable.

Visuals (charts, graphs, illustrations, images, etc.) are also a fantastic help in achieving the goal of persuading others with your data insights. But sorry to disappoint: ggplotplotly and viridis are not enough! Neither is avoiding pie charts like plague (even if that’s a good starting point!) Because our brain is heavily dependent on vision, visuals need to be designed to respect the functioning of our brain. Principles of visual communication and of graphic design have been crafted over decades (when not centuries) to achieve just that.

In short, communication and presentation skills matter as much as technical skills to data scientists.

In this workshop, you will work on the tip of the iceberg: what is visible to others after you’ve worked hard on your data. You will learn, as well as put to practice, the components that will help you become a better communicator when it comes to explain and present the gems you found. In particular, you’ll learn about:

  • The SUCCESs principles that guide the creation of a persuading message that will stick in your audience minds, days or weeks after you shared it,
  • The storytelling elements to combine and to apply to your data that will help you spread your message,
  • The traps and misconceptions of communication and of our brain inner workings, and how to avoid falling into them,
  • The fundamentals of graphic design and visual communication to turn your data into persuasive (and ethical!) visualization.

Sign-up for this 3-hour workshop, combining theory, insights, and hands-on tasks, and join the tribe of data scientists who successfully make their insights so compelling they create ripples of action!

Who’s the workshop leader?

Alexis is a pure blend of art and science: Computer Science & IT engineer, presentation skills and inter-personal communication trainer and coach, magician, corporate speaker, master of ceremonies, and improv comedian.

For the past 15 years, the common denominator of Alexis works has been about making people understand each other and work together. As such, he has put rules and principles about communication, graphic design, storytelling or neuroscience, into practice to help non-experts understand technical topics and to help experts and non-experts speak the same language and work better as a team.

Read his blogpost

* The workshop is limited to 12 persons maximum to ensure everyone can apply right away the theory on their own pieces of work, and to improve via personalized and targeted feedback.

Phase 2: 12w Data Science Bootcamp

This is the 12 weeks program composed each week of 2 days hands-on training by a specialist bringing a business case followed by additional workshops and soft skills trainings. Each candidate works on one of the use case of the HIVHACK.

Frustration. As in “They didn’t get my message. But it was so much obvious!”

Sorry to disappoint: your story is not obvious to others. After all, you did all the analysis and they didn’t!

In fact, if you don’t lead your audience into your insights, it’s like letting your audience take a guess on what they should understand of the facts you share. A risky game for you!

Stories are well known to be fantastic tools of persuasion. Stories even surpass data visualizations in explanatory mode. And we are not talking about Red Riding Hood here (even though it still beats most data visualizations in terms of understanding and creating lasting memories!)

We are rather talking about the story held by your data. It is the story of how your business has evolved over time, about the next move you have to make, about the hidden root cause of an issue, about the one thing you uncovered that could make the world a better place.

In this lecture, we’ll explore why so many data scientists are frustrated when presenting to non-data experts (like business, marketing or CxOs) and why stories are the best tool you have to share your insights and to invite action.

We’ll then dig into basic storytelling elements that you can apply to your data, and to the message you hold dear to your heart.

 

“Facts are facts. Stories are how we learn” – Alan Webber, Rules of Thumb

Come have lunch and discover what makes stories the best way to convey complex ideas and to move people, and how to start crafting your own.

 

Who’s the lecturer?

Alexis is a pure blend of art and science: Computer Science & IT engineer, presentation skills and inter-personal communication trainer and coach, magician, corporate speaker, master of ceremonies, and improv theater comedian.

For the past 15 years, the common denominator of Alexis works has been about making people understand each other and work together. As such, he has put rules and principles about communication, graphic design, storytelling or neuroscience into practice to help non-experts understand technical topics and to help both experts and non-experts speak the same language and work better as a team.

Link to his blog post

DESCRIPTION
DataBeers is a free event (including the beers!), however registration is mandatory.

Program:
19:30 – We open the doors (and beers!)
20:00 – The talks start
21:30 – After the talks we move downstairs to MuntPunt Grand Cafe for more beers and networking

Sponsored by: Dataiku

[DSB2018] Text Mining with R / Jan Wijffels / BNOSAC (session #03-04)

Day 1 of  this 2-day module, Jan Wijffels (from BNOSAC) will explain the use of text mining tools for the purpose of data analysis. It covers basic text handling, natural language engineering and statistical modelling on top of textual data.

Target:  Data Science professionals.

Prerequisites: Knowledge of R; 

follow one of these courses:

1. http://lstat.kuleuven.be/training/coursedescriptions/statistical-machine-learning-with-r
or
2. https://lstat.kuleuven.be/training/coursedescriptions/AdvancedprogramminginR.html

   Knowledge of basic Statistics lm/glm

   Knowledge of Data Manipulation

   Basic knowledge of Predictive Modelling

Session outline
1. Text encodings
2. Cleaning of text data
3. Regular expressions
4. String distances
5. Graphical displays of text
6. Natural language processing: stemming, parts-of-speech tagging, tokenization, lemmatisation
7. Sentiment analysis
8. Statistical topic detection modelling and visualization (latent diriclet allocation)
9. Visualisation of correlations & topics
10. Word embeddings
11. Document similarities
12. Text alignment.

[DSB2018] Text Mining with R / Jan Wijffels / BNOSAC (session #03-04)

Day 2 of  this 2-day module, Jan Wijffels (from BNOSAC) will explain the use of text mining tools for the purpose of data analysis. It covers basic text handling, natural language engineering and statistical modelling on top of textual data.

Target:  Data Science professionals.

Prerequisites: Knowledge of R; 

follow one of these courses:

1. http://lstat.kuleuven.be/training/coursedescriptions/statistical-machine-learning-with-r
or
2. https://lstat.kuleuven.be/training/coursedescriptions/AdvancedprogramminginR.html

   Knowledge of basic Statistics lm/glm

   Knowledge of Data Manipulation

   Basic knowledge of Predictive Modelling

Session outline
1. Text encodings
2. Cleaning of text data
3. Regular expressions
4. String distances
5. Graphical displays of text
6. Natural language processing: stemming, parts-of-speech tagging, tokenization, lemmatisation
7. Sentiment analysis
8. Statistical topic detection modelling and visualization (latent diriclet allocation)
9. Visualisation of correlations & topics
10. Word embeddings
11. Document similarities
12. Text alignment.

25 May marked the end of an intense start, but DPO’s have a long road ahead in building a robust privacy office and data protection practices across their organizations. This full-day seminar focuses on getting ready for the long haul with a wide range of speakers and interactive sessions.

Program

  • The future of the DPO – Bart Van Buitenen
  • DPO-pro tools – Wim De Keyser
  • Data sharing between Joint Controllers – Koenraad Flamant
  • Data protection by design – Florence de Villenfagne
  • Definite Do’s and Dont’s for DPOs – Bart Van Buitenen
  • Beyond the GDPR – Bavo Van den Heuvel
  • DPO campfire – facilitated session between small groups of DPOs to share experiences and learn from peers

 

There!  &  in  –

S05E01 of the Data Science Bxl  scheduled on the 11/10 !

With: 

After a first successful season (167 people registered to our last meetup!), we have decided to pursue the activities of the Data Science Liège community!

The next session will take place at HEC on Thursday 18th October with, as usual, a solid lineup of three inspiring short talks.

The mission of Data Science Liège is to offer a forum, upon which participants can leverage to federate data science initiatives, showcase projects and ideas, call for support and partnerships, disseminate knowledge and stimulate public awareness.

Participation is free but registration is required. You can register for our next meetup as of now by clicking here.

Follow us on Twitter : @DSLiege for updates and latest news!

PS: We are looking for participants to present and showcase their past or ongoing data science projects. Feel free to submit your propositions by email to datascience@uliege.be

Program
6.30 pm: Doors opening
7.00 pm: Short talks (see abstract below)
8.00 pm: Panel discussion & closing remarks
8.15 pm: Drink & Networking
9.30 pm: Closing

Abstracts
Talk 1: Lean Data Science or how to enable data science with wise investment?

by Michaël Hooreman, Data Scientist – Barco

Technologies commonly used in data science and big data involves high investment in terms of infrastructure (cloud, proprietary software, etc.), without any assurance of success. This talk will show a low investment approach for data science based on underlying early stages optimizations, which has proven his reliability on processing of relatively high volume of IoT data. It also provides a learn-as-you-go approach which privileges tailored solutions using the most used data science tools.

Talk 2: Au service de la data science, l’architecture des données : retour d’expérience

by Maryse Colson, Manager – Eura Nova

Les modèles de data science sont aujourd’hui de plus en plus abordables pour les entreprises. Là où les entreprises rencontrent encore des difficultés techniques et des coûts importants, c’est dans la multiplication des business cases et l’intensification de l’exploitation des données sur le long terme. En effet, pour pleinement profiter de la puissance de la data science à l’échelle, il est nécessaire pour les entreprises de mettre en place une architecture qui met les données au centre et facilite le travail des équipes techniques, de data science et business, ainsi elles pourront contrôler le coût de l’exploitation des données. Il s’agira donc, dans cette présentation, d’aborder quelques cas extrêmement concrets qui montrent comment une architecture ad hoc est garante de la pérennité de la data science.

Talk 3: L’intelligence artificielle : des outils pragmatiques à la portée des opérateurs d’un processus de production industriel

by Philippe Mack, CEO – PEPITe

La société PEPITe développe depuis plus des 15 ans des solutions avancées d’analyses de données pour optimiser les processus de production industriels. Au travers d’un cas pratique et réel, nous montrerons comment les ingénieurs d’une usine sont amenés à utiliser cette nouvelle génération de logiciels pour améliorer la performance de la production et résoudre plus rapidement les aléas quotidiens d’une ligne de fabrication.



M_Hooreman_0.jpg

Michaël Hooreman
Data Scientist – Barco

Maryse Colson_0.jpg

Maryse Colson
Manager – Eura Nova

Untitled design (12).png

Philippe Mack
CEO – PEPITe

In het kader van het initiatief “#dataforbeterhealth” worden verschillende events georganiseerd. Ondermeer een “workshop over FAIR Data & Open Data”. Deze workshop richt zich in het bijzonder tot de beheerders van door de overheid gefinancierde (onderzoeks-)databanken en wil hen informeren over de concepten FAIR data & Open Data, hun belang en potentieel, de toelichting van de beleidsinitiatieven terzake, en de impact op hun activiteiten.

Het is voor dit event dat de initiatiefnemers u wensen uit te nodigen als gastspreker. Gezien uw expertise en betrokkenheid in het domein van het Open Data beleid in België zouden wij u willen vragen of u een inleiding (30min.) in Open Data (wat, waarom, hoe, …) wil geven.

De workshop gaat door op Donderdag 18 oktober, vanaf 17u00 tot 20u00. De locatie is Brussel, meerbepaald bij Digityser, de “data factory” van België en digitaal clubhuis van de European Data Innovation Hub. 

Het programma dat wij voor ogen hebben:

  1. Inleiding in de FAIR-data leidende principes voor wetenschappelijk gegevensbeheer (door Prof. Dr. Barend Mons)
  2. Het FAIR data stewardship plan: een praktische aanpak (door Prof. Dr. Barend Mons)
  3. Toelichting bij de FAIR- & REQUEST-portals voor respectievelijk de beschrijving van en toegang tot door de overheid gefinancierde gegevensbanken in de volksgezondheid (door medewerker Sciensano)
  4. Inleiding in Open Data beleid in België
  5. OntoForce: Illustratie van toepassing van Open en FAIR data in België (door Hans Constandt / Filip Pattyn)
  6. Voorstelling van het “Koninklijk Besluit tot bepaling van de voorwaarden voor het hergebruik van overheidsinformatie” (door medewerker van de beleidscel van de federale Minister Digitale agenda)
  7. Voorstelling acties vanuit het beleid (door medewerker van de beleidscel van de federale Minister Volksgezondheids
Data Science Ghent is back! October 18th is kick-off for the year to come. We’re happy to have Crunch Analytics as host for October 18th (at co.station Ghent), and soon will announce dates for next meetups & trainings. We’re searching one more speaker for October 18th. No rules, great talks… except: no sales pitches. Please contact me or Hendrik D’Oosterlinck Davio Larnout Matthias Feys if interested as speaker.

The 10th #DataScience #Leuven #Meetup will take place on Monday, the 22nd of October @KU_Leuven near @STUKLeuven, with speakers Prof. Luc De Raedt, @LaurentSorber and @KobeLeysen.

More info: https://www.meetup.com/nl-NL/Data-Science-Leuven/events/255161093/

“How to Shift from Wantrepreneur to Entrepreneur and Finally Start that Business you Want.” 

Masterclass:
 
This Masterclass reveals “How to Shift from Wantrepreneur to Entrepreneur and Finally Start that Business you Want.” 
 
This Masterclass is aimed at those people who have a business idea but are not taking any action or people that dream of starting their own business but are stuck.
 
During this Masterclass you will be taken on a  journey and find the answers to:
 
What is holding you back to take action towards your dream business?
Why you procrastinate and are busy with so many other things?
How to finally take action and follow your dream?
What is the right mindset to become a succesfull entrepreneur ?
Duration: 2h
Presented by: 
Johan Van Goidsenhoven
Mindset & Business Coach, Owner of InsideOutShift Coaching

Dear friend, join the fight and use your skills for a good cause.

Wednesday OCT 24 at DigitYser we will gather to get an update on the work that has been done so far.

Over the past months many volunteers have gathered useful data and prepared it for the different challenges of this hackathon.

Register for the workshop here: https://lnkd.in/gBKziAz

The aim is to prepare for the #HIVHACK that will take place on NOV 23&24 in our clubhouse.
You can register alone or with your team here: https://lnkd.in/gu8id_e

Please forward this message to your peers that could be interested to participate to our yearly #ai #data4good hackathon.

more info on HIVHACK.org

Agenda:

  • Presentation of some work that has already been prepared.
  • Presentation of some interesting datasets.
  • Presentation of what you can expect during the hackathon.
  • finger food and networking drink

Objectives of the hackathon:

Through this exercise we will get an understanding of epidemiological and non-epi factors influencing the emergence of HIV-DR and a model mapping the spots. This will result in dynamic and up-to-date graphical and visual representations of HIV-DR maps allowing governments, health workers and other stakeholders to be informed on the topic and to turn the understanding of the drivers into actionable items in the prevention and countering of HIV-DR. As part of our mission we intend to educate on the approach and outcomes locally.

We have datasets available but each team is free to use any publicly available dataset to achieve its objective of creating the perfect heath map.

You will have different challenges divided in different tiers to choose from (link to the detailed use cases):

  • First Tier

    • Data Integration

    • Hot Spot Mapping

    • Sexual Activity

  • Second Tier

    • People Movement

    • Finance

    • ART Guidelines

  • Third Tier

    • Getting a view of the HIV DR Research field

  • Data Required

    • HIV-DR Prevalence, Incidence

    • Drug Stock-outs

    • Rural/urban

    • Distance to healthcare facility

    • Other…

  • How could outcomes look like

    • Identifying weights and factors contributing significantly

    • Visualisation of prevalence across time and space

[DSB2018] Intro to Neuronal networks & deeplearning with Python – Loïc Quertenmont (session #11)

After a short theoretical introduction to neural networks principles and mathematics, we will start coding neural networks ourselves with an increasing complexity.

During the day, the pros and cons, so as the tips and tricks for using neural networks in real life problem will be discussed. Concepts like dropouts, data augmentations, transfer learning will all be explained.

– Theory introduction 
Goal: Make sure that everybody is on the same page

– Keras library in python (Hands-on)
Goal: Get familiar with the Keras library and python coding environment by coding a basic neural network that predicts the output of an XOR function.

– Multi-Layer Perceptron (Hands-on)
Goal: Code a Multi-Layer Perceptron that can be used to predict the response of an unknown (non-linear) function with several inputs using real-life use cases.

– Neural networks for Text or Image processing.
Goal: We will conclude the day with an introduction to the more advanced type of neural networks that are the Long Short Term Memory (LSTM) networks or the Convolutional Neural Networks that are heavily used in text and image processing, respectively.  If time permits it, we will code such networks too.

Pre-requirement for the lecture:
Have a python3 development environment installed on your computer (ideally using the jupyter notebook). Everything can be installed easily via the Anaconda Distribution: https://www.anaconda.com/distribution/

 

The speaker, Loïc Quertenmont has a doctorate in particle physics from UCL.  Among other things, Loïc used to analyze the 50PB of data produced at CERN every year and he was involved in the discovery of the Higgs boson at CERN in 2012.  In 2018, Loïc has funded Deeper Data Analytics  (http://deeperanalytics.be/) in order to help companies with their big data, data science, and machine learning problems.

Deeper Data Analytics tackles projects from the data architecture up to the development of web/mobile applications to deliver the data analysis outputs.

[DSB2018] Graph Databases with Rik Van Bruggen / Neo4J (session #12

In this session, Rik Van Bruggen and Tom Geudens, from Neo4j, will guide you through a hands-on experience with the graph database model and it’s most popular implementation, Neo4j.

Target

  • Data Science professionals interested to learn graph databases.

Prerequisites

  • General database / SQL experience is useful but not mandatory.

  • The participant must have Neo4j installed on his laptop before coming to the course. You can download the correct latest version from https://neo4j.com/download/ . When you surf to https://localhost:7474 you should see the start screen of the Neo4j “browser”.

Session outline

 1. Introduction
 2. What is a graph database
 3. Why a graph database 4. Graph database use cases
 5. Graph Query Languages – (open)Cypher
 6. Graph query assignments 
 7. Graph query assignments
 8. Future reading and sources of info.
 9. Q&A

Practicalities

  • Lunch is included in your ticket.

  • Doors are open at 8.30 am.

  • Training starts at 9 am and finishes at 5 pm
    (be on time or inform us if delay, respect for the audience/trainer).

  • Accessibility: public transport (stop Yser / Ijzer – subway line 2-6 ).
    We do not have parking but normally there are some parking slots at Rue des Commerçants.

  • Training partners and community can contact us via e-mail to request their discount code (training@di-academy.com).

This 2 hours training will teach you the basics of presentation skills. The aim is to allow the candidates to prepare themselves to  the JobFair of 15/11 where they will be asked to present themselves in 2 minutes.

Please prepare your elevator pitch beforehand.

[DSB2018] When Reality Bites in Data Science Projects – Kris Peeters / Data Minded (session #13)

Reality can really bite when working in Data Science projects… and that is why we are honoured to have Kris Peeters, from Dataminded, delivering our first session of the Data Science Bootcamp 2017 edition. In his frank and charismatic style, Kris will share his view on the four pillars (the WHY, PROCESSES, TEAM and LEADERSHIP) related to working on Data Science projects, sharing his secret recipe to reach success in a Data Science project in a hands-on way.

There will be will be a lot of interaction, discussions and whiteboarding!

Target

  • Data Science professionals (management included) eager to deliver their their projects in a more effiicient way.

Prerequisites

  • Basic understanding on Data Science tools (Python and R). It is not required to install anything.

  • Mandatory reading: Kris’s post on his experience in Data Science projects.

Program

    1. The WHY

  • Why do you want to do data science projects?

  • What do you want to get out of it?

  • Where do you see yourself in 5 years?

  • What are the expectations of employers and clients?

  • How do you do interviews?

  • What can possibly go wrong in this relationship?

  • What are some war stories?

    2. PROCESSES

  • What are the possible outcomes of a data science project?

  • What does a typical data science project look like?

  • Which methodologies do you have?

  • What do we see in reality?

  • What can possibly go wrong with processes?

  • What are some war stories?

    3. TEAM

  • What are the skills you need to do a data science project?

  • What are the different roles in a team?

  • How important is seniority?

  • How do you create team alignment?

  • What’s the difference between a data scientist, a data engineer and a DWH developer?

  • What can possibly go wrong with team setup?

  • What are some war stories?

    4. LEADERSHIP

  • Who is a leader?

  • What does it mean to be a leader?

  • How can you show leadership from the bottom?

  • Who are some inspirational leaders in the field?

  • What kind of leadership do we see in practice?

  • What are some war stories?

  • What can possibly go wrong with leadership?”

Tips from an employer on how to present yourself during a job interview. Meric will present to the junior data professional what is important for an employer. Meric has been in charge of creating and hiring different datascience teams in 2 different insurance companies. Meric wants to help the bootcampers to understand what he can expect during a job interview.

[DSB2018] Big Data Architectures – Kris Peeters / Data Minded (session #14)

During this session, Kris Peeters, from Data Minded, will provide an overview of the technologies and architectures that exist, and discuss the pro’s and con’s of each. Based on a concrete example, the objective is to make the right trade-offs and build your own data architecture using gamestorming techniques.

There will be will be a lot of interaction, discussions and whiteboarding!

Target

  • Data Science professionals interested to learn about Big Bata architecture.

Prerequisites

  • General notions of data architecture

Session outline

1. Brainstorm data analytics idea: List all ideas you have for data analytics, and rate them in complexity and value.

2. Map your data sources: List all data sources you have available for these ideas, and scale them in terms of volume and speed.

3. Select big data ideas: Select one idea that qualifies as a big data idea.

4. Choose big data technologies: Choose the right big data technologies for that idea.

5. Build a big data architecture: Combine those technologies in a big data architecture.

6. Identify potential risks: What can go wrong with your setup, where do you have to pay attention to.

Since 2014, when we started building the Data Science Community in Belgium, we realised the need not only to share knowledge, but also to find ways to bridge the gap between data talent and companies searching for it. Hence, a two ago -when we had the first edition of our Data Science Bootcamp- we thought on the best way to ensure a truly dynamic interaction between our Bootcampers -vetted professionals acquiring real field knowledge from top experts- and the organisations needing their talent.

After considering the options, we realised the need of a “double pitch” scheme, in which companies would present themselves and share the type of opportunities they offer, and candidates would showcase their professional experience and skills. This has proven to be a great way for both sides to break the ice, test both parties “chemistry”, and trigger a conversation in a faster and more effective manner.

During the Data Science Bootcamp current edition, we kept on performing our Job Fairs using this “double pitch” format, but decided to allow companies to kick-off the conversation in advance through the web. By allowing companies to access an online portfolio, organisations can get acquainted with our Bootcampers upfront, contacting them before the Job Fairs, leveraging the efforts of their HR departments.

Are you considering joining our Job Fairs? If the answer is “YES”, have a look at the following recap;

1.Reserve your seat here & we will advertise your job post and your presence at the job fair.

Please note that your reservation is valid for the Job Fair on November 15th & December 20th.

2. Review the online portfolio of our candidates Afterwards, during the Job Fair, you will have the opportunity to pitch your company, listen to the candidates pitches, and meet them to kick-off the discussion on the collaboration opportunities. We also opened our event to non bootcampers to allow you to meet other data experts too.

3. Hire your Data Expert: After meeting your candidate, you have the flexibility to define in which terms do you want to hire the candidate, which can be for a short period (even on a daily basis, if it is your preference), or for a longer one. We are sure that you will try to go for the last option!

Easy peasy! You are just 3 steps from having the top Data Science talent in your organisation. If you have any question, please do not hesitate to contact us: we will be glad to answer to your questions… and to meet you on November 15th & December 20th 2018 at DigitYser! 

HACKATHON #1 (voorbereidende Workshop):  

16 november 2018 (doors open 13h00; start program 14h00 – until 18h00) : https://dataforbetterhealth.be/hackathons/ 

In this hackathon, an anonymised database of one of the federal administrations is made available. The participating data scientists are introduced to the objective, use and metadata of the database by the employees of the relevant administration.

These days, predictive analytics are driving better decisions in numerous domains like marketing, risk, operations and HR in diverse industries. In this session, Nele Verbiest, from Python Predictions, will show how to approach a typical predictive analytics project. At the end of the hands-on sessions, the participants will understand the fundamentals of predictive analytics, be able to build a stable predictive model, and know how to present their results to business in an elegant fashion. The participants can demonstrate their new predictive analytics skills at the end of the day on a business case in fundraising using R. Target Data Science professionals looking to gain experience with predictive analytics with R. Prerequisites Knowledge of R. R or RStudio (preferred) should be installed Session outline 1. Introduction to predictive analytics: definitions and usecases 2. Predictive analytics algorithms and evaluation techniques 3. Data preparation for predictive analytics 4. How to align a predictive analytics project with business 5. Presenting your predictive analytics project to business
n this session, Nele Verbiest, from Python Predictions, will introduce to the participants the fundamentals of segmentation, the process that divides customers into groups with similar profiles and behaviour, used by many organisations as a strategic tool to understand customers and monitor evolutions throughout the customer base. During hands-on sessions in R, participants will learn the intuition, methodology and code needed to construct a useful, data-driven, cluster-based segmentation. The day is concluded with a hands-on lab in R, in which the participants will apply their newly gained segmentation skills to make a segmentation of the Brussels Data Science Community members using R. Target Data Science professionals interested in creating a cluster based segmentation. Prerequisites Knowledge of R. R or RStudio (preferred) should be installed. Session outline 1. Introduction to segmentation: definitions and usecases 2. Clustering: data driven segmentation 3. Data preparation for clustering 4. How to align a segmentation project with business 5. Presenting your segmentation results to business

The HIV epidemic has claimed numerous lives since its discovery in the 80’s. Over the years, the efforts and coordinated work of national programs, the civil society and development partners have allowed to achieve tremendous progress.

Even though there is still no cure for HIV, effective treatments have been developed and they can offer to patients long and healthy lives. However, treatments must be rigorously followed throughout life to successfully contain the virus. Failing to adhere to the treatment can lead to a mutation of the HIV into a form resistant to the available treatments. This is how HIV Drug Resistance can emerge.

 Following the massive increase of people on treatment (from less than 800 000 in 2000 to more than 18 millions in 2016), a non-negligible rise in the prevalence of resistant HIV strains has emerged and threatens the global commitment to end the HIV epidemic by 2030.

If no action is taken now, and this problem left unaddressed, HIV Drug Resistance could lead to a new crisis in the HIV epidemic.

1* algorithmic complexity of ETL operations:
how to choose the right algorithm for the right operation. Some examples.

2* How to organize the “data lake” to get maximum performance with Anatella?
What are the “best practices” to use? What are the special operators for Big Data processing that allows manipulating large data volume (e.g. a database that grows of 1TB per day) with Anatella?

Practicalities

  • Lunch is included in your ticket.

  • Doors are open at 8.30 am.

  • Training starts at 9 am and finishes at 5 pm
    (be on time or inform us if delay, respect for the audience/trainer).

  • Accessibility: public transport (stop Yser / Ijzer – subway line 2-6 ).
    We do not have parking but normally there are some parking slots at Rue des Commerçants.

  • Training partners and community can contact us via e-mail to request their discount code (training@di-academy.com).

Abstract

This one-day training highlights the features and functionality of the Shiny package created by RStudio.

No matter what you do with R, Shiny will transform your R world by making it easy for you to turn your R analyses into interactive web applications without the necessity to program in HTML or JavaScript.

 This course is not just pure theory. It also includes hands on with a comprehensive case that combines all the topics discussed in the training and more.

 

 You will:

– Understand Shiny logic, its framework and its main steps.
– Learn how to create amazing interactive visual applications.
– Understand how to effectively use “reactivity” in both the user interface and the server-side logic.
– Walk through useful extensions
– Javascript visualisation library,
– Javascript UI interface,
– Deploy applications

 

Important note:

 This session will be an adaptation of a 2 days trainings; which means some topics won’t be totally covered.

– Know how to debug your app.
– Style with customised CSS,
– Quickly building dashboards with Shinydashboard extension

When session objective is to give a quick and effective global overview, eventual external participants may wish to extend with on-the-job coaching activities for which WeLoveDataScience will propose reduced rates.

 

Support

Physical printed version of the 2day trainings are proposed to participants at a price of 30€ (150 slides).

 

Requirements:

– a computer with R and RStudio already installed.
– Some knowledge and use of R and RStudio (i.e. write a function, make a basic plot and call help).
– No knowledge of HTML, CSS or JavaScript is required. However, you’ll be able to make your hands-on more interesting if you know some of them. 
– An email will be sent to participants so that they install some required R packages before the training.

 

About the trainer:

Eric accumulates 20 years of experience in Analytics/Data Mining. His work at WeLoveDataScience, a young company focusing on data science value co-creation with offer dealing with trainings, coaching, collaborative R&D, prototyping and MVP building… During numerous Data Science projects, WeLoveDataScience worked with Shiny framework and now want to share their experience.

Thomas is one of the founders of DPO-pro. He will give us a data centric overview of GDPR.

 

The course zooms into the main techniques of (social) network analysis from a theoretical and practical perspective. Network theoretical concepts such as homophily, multipartite graphs, centrality, etc. are discussed and supported by hands-on examples.

 

  • Chapter 1: Introduction to key concepts of Network Analysis
  • Chapter 2: Creating networks
  • Chapter 3: Calculating centrality measures
  • Chapter 4: Visualizing networks
  • Chapter 5: Community Detection
  • Chapter 6: Other useful options for SNA
  • Chapter 7: Best Practices
  • Chapter 8: A glimpse on advanced network analysis

Accordion Content

Welcome to the first Data Science Jobfair of the Summer.

We are excited to introduce to your our talented Summer Coding Camp & Data Science Bootcamp participants!  

This is the perfect opportunity for companies to meet their future members of their DataScience Team.

Program:

15:30 – Arrival at DigitYser & Visit of the building

16:00 – Presentation of the Summer Coding Camp & Data Science Bootcamp 

16:15 – Presentation of the companies eager to hire candidates and to sponsor the DataScience program.

17:00 – 2′ presentation of each participant to the bootcamp.

17:30 – Networking & One-on-One meetings candidate / companies. 

18:30 – End of the event & Start of the HIVHACK workchop

Each company can bring their stand or use one of our high tables in the Atrium. This event is free for our Training Partners and Innovation Partners.