Cloud Computing Network Online Internet Storage Concept Articles in English / Data

The amount of information around data science has done nothing but explode in the last few years, and the same applies to its sources of information: in a rapidly changing field like this, most of the fun takes place at community-level, where dynamic environments such as meetups, webinars, conferences, competitions and of course some companies take the lead. Following up with all this can be challenging, but what’s even more complicated is being able to filter all that content to exploit only what you really need.

This fact is especially true when you don’t have a technical profile but you’re more of a strategy/ business (or even C-level) person looking to get an overview of the field and its latest tendencies. This is a problem itself, since not enough understanding of big data, its value and applications is known to be one of the main obstacles to the use of these technologies… But don’t panic! This first article provides an initiation guide with a series of resources to help you navigate amongst all this information.

First things first, to get up to date on some aspects of the market and the field, there are some useful resources like EBG’s white paper on Big Data that  explains the basics of the field through testimonies of many companies and experts, or O’Reilly’s yearly wrap-up that describes important developments on big data.

Now, if by this point you are convinced on getting your organization’s hands dirty (and yours too) with data science you should get familiar with terms like “data-driven” or “data-oriented”: this is in fact the core culture that pioneer companies on the field like Google, Facebook and LinkedIn realized was essential to transform any (big) data into real business results, regardless of the costs. Many “elite” members of the data science community such as U.S. Chief Data Scientist are doing some efforts on passing the message to organizations on what that actually means, with advice on how to go in that direction.

Finding somebody to do the job might be the first obstacle you encounter. Everybody knows that finding the right talent is complicated, even more so for data science and analytical skills. There is a one truth that recruiters often don’t take into account when looking for data scientists: they like action. Most of them will be found interacting with other data scientists in different events like MeetUps or online competitions. If you want to attract their attention, your organization will have to go where they live and mingle among them. In fact, world-class companies like AXA and Walmart have changed their strategy on recruiting: they translate their day-do-day problems into data science competitions, to leverage the community in order not only to help solve these problems, but also to find the right talent they are looking for.

There are of course many other resources for you to exploit: yearly events that you might want to attend such as the Big Data Paris conference, some websites like bigdata-madesimple which explain complex subjects and use cases in a simple manner, and LinkedIn Groups which are ways to meet and interact with active players of the community… If time is not a problem for you, websites like BrightTalk also offer a huge selection of webinars about data science that will allow you to listen and ask questions to other organizations.

If you prefer face-to-face discussions instead of staring at your computer screen, one of the main ways to easily interact with people of all kind of profiles are MeetUps. For a few hours once in a while, people gather to discuss more technical or business-related data science subjects.

Almost everybody is aware of the enormous buzz there is on the topics and concepts discussed in this article… but many think, including myself, that there is nothing new in the field other than the revolution of the “data culture”, accompanied by an increase of computational capacity and less expensive storage. Some can even argue the fact that “data science exists as a field, is a colossal failure of statistics” because it is being able to democratize data, and bring all of its insights to the final user.

In the next part of this article we will be focusing on more technical profiles: We will discuss the educational evolution of MOOCs* in favor of data science and share many other useful resources.

*Massive Open Online Courses

Avatar de David Faria
David Faria

Vénézuélien d’origine portugais, David est doublement diplômé en ingénierie électronique et en “machine learning”. Avant de rejoindre Equancy en 2014, il travaillait chez Ubisoft, pour lequel il modélisait les comportements d’usage des jeux, afin d’en optimiser les scenarios et les fonctionnalités. Chez Equancy, David continue à développer son expertise (et intérêt) pour les techniques de modélisation et traitement de données : Il a mené principalement de missions de mesure de performance de campagnes pour DisneyLand Paris, Cartier et Michelin, ainsi que plusieurs missions de ciblage et connaissance client.


  1. Very promising first part 🙂
    Do you have some specific meetups to recommend ?

    • David Faria Says: August 19, 2015 at 3:54 pm

      Thank you Pierre. It actuailly depends very much on what is your objective and where you are located. If I assume you’re based in Paris and that you’re looking for a soft, non-technical meet-up, you might enjoy attending to “Paris Data Business”. Some other groups often propose “soft” talks about big data & data science within their technical meetups, so don’t hesitate to also check “Paris DataGeeks” and “Paris Machine Learning”. These two last have also very similar initiatives in other cities of Europe such as London or Berlin.

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