Jan Sonck – Head of Enterprise Innovation @ Proximus – slides How do we use Data Science in a Telecom Operator? All PostsExample of external use cases: which business question, which data? What about privacy concerns?
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.
Registration deadline: 20 January 2015
Course: 5-6 February 2015
I’m please to invite you to our next meetup focused on Big Data and Data Science.
Here is the agenda:
Dedicated session on Big Data. Our friends from bigdata.be wil explain what issues that terabites of data bring and what architectures are required to be able to manage it. We will have Agoria talking about key projects they support where data analytics plays a major role, followed by talks from companies involved in BigData projects.
• 19:00 Wim Van Leuven about Big Data and the influence of data science
• 19:30 Daan Gerits about the technological challenges around BigData and how the ideal IT architecture should look like
• 20:00 Ferdinand Casier about 2 running projects at Agoria (EluciDATA and Made Different) where companies of different sectors are supported in their data innovation and data analytics challenge..
• 20:45 Karim Douïeb The pro & cons of the open-source data analytics cluster computing framework called Spark. How it is helping us to perform analytics on large amount of telco/banking data. How to convey the insights provided by this analysis through data visualisation using D3.js
VUB – lokaal D.0.02 – max 170 plaatsen ! Pleinlaan 2 B-1050 Brussel Brussels, BE
95 Business & Data Science pro’s Attending
Dedicated session on Big Data. Our friends from bigdata.be wil explain what issues that terabites of data bring and what architectures are required to be able to manage it. We will have Agoria talking about key projects they support where data analytics plays a major role, followed by talks from companies involved in BigData projects.Agenda:•18:…
This post was written by the team behind DataCamp, the online interactive learning platform for data science.
After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting ring to it. But it is exactly the name that also raises a lot of questions. Because what is a data scientist and what do data scientists do exactly? Many of us who devote their lives to data science have frequently been confronted with questions like these.
The answers to these questions are mostly not as straightforward as you would expect: a short search on Google with the string of words “How to become a data scientist” shows that the concept has different meanings to different people. In addition, many articles indeed suggest various tools, courses and applications for people to become a data scientist, and with good reason: the options are unlimited. But let’s face it, for someone that is not familiar with the field, this advice may sometimes seem like a jungle of information. What’s more, they could work demotivating: the descriptions are sometimes fearfully long and the many details often hit the readers as an overwhelming avalanche.
DataCamp’s Guide to Becoming a Data Scientist
With all this in mind, DataCamp decided to help those who can’t see the forest for the trees: we designed a step-by-step infographic that clearly outlines how you can become a data scientist in 8 easy steps. This visual guide is meant for everyone that is interested in learning data science or for everyone that has already become a data scientist but wants some additional resources for further perfection. The infographic is called “Become a data scientist in 8 easy steps”. Have a look at it!
If you are thinking about becoming a data scientist, do not be taken aback by the eight steps that are presented in the infographic. We would like to emphasize that becoming a data scientist takes time and personal investment, but that the journey is everything but dull! And don’t forget, there are plenty of courses available to set you on the right way.
If you are already a data scientist, drop us a line firstname.lastname@example.org if you think of other steps that you have undertaken in your professional journey.