BigDataFr recommends: Beginner’s Guide to the History of Data Science […]“Big data” and “data science” may be some of the bigger buzzwords this decade, but they aren’t necessarily new concepts. The idea of data science spans many different fields, and has been slowly making its way into the mainstream for over fifty years. In fact, […]
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[Forbes] BigDataFr recommends: Six Ways Mobile And Big Data Are A CMO’s Best Friend
BigDataFr recommends: Six Ways Mobile And Big Data Are A CMO’s Best Friend […]Contemporary consumers are constantly bombarded with content across countless channels. CMOs are facing enormous challenges when it comes to engaging with an audience and consumer base to drive awareness and sales. The public’s habits are changing, however, and people are growing more […]
[Marketingweek – Tip] BigDataFr recommends: Top ways to use data to build relationships
BigDataFr recommends: Top ways to use data to build relationships Marketers are under pressure to produce more personalised messages through different channels. However, with the power of data comes responsibility, and consumers only want relationships with brands they trust. Trust is top of the agenda According to the Direct Marketing Association’s (DMA) Customer Attitudes to […]
[Nvidia] BigDataFr recommends: Why Enrollment Is Surging in Machine Learning Classes
BigDataFr recommends: Why Enrollment Is Surging in Machine Learning Classes Machine learning is sweeping through industries, transforming scientific research and already changing aspects of everyday life, making the impossible possible. No wonder university students are flocking to it. Enrollment in machine learning classes is soaring, and universities are scrambling to add classes to meet an […]
[Mc Kinsey Report] BigDataFr recommends: Creating value from car data
BigDataFr recommends: Creating value from car data Car data: paving the way to value-creating mobility Perspectives on a new automotive business model […] The expected growth of the value pool from car data and shared mobility could add up to more than USD 1.5 trillion by 2030, and the foreseeable proliferation of new features and […]
[O’R – Tutorial] BigDataFr recommends: Training and serving NLP models using Spark MLlib
BigDataFr recommends: Training and serving NLP models using Spark MLlib Identifying critical information out of a sea of unstructured data, or customizing real-time human interaction are a couple of examples of how clients utilize our technology at Idibon—a San Francisco startup focusing on Natural Language Processing (NLP). The machine learning libraries in Spark ML and […]
[L’Usine Digitale] BigDataFr recommande : Big Data : La SNCF, bientôt capable de prédire les pannes des Transiliens avec 30 minutes d’avance ?
BigDataFr recommande: Big Data : La SNCF, bientôt capable de prédire les pannes des Transiliens avec 30 minutes d’avance ? « Nous voulons avoir des trains de Transilien qui fonctionnent et opérationnels en service à un coût raisonnable », lance Philippe de Lahappe, chef de projet télédiagnostic à la direction du matériel à la SNCF. C’est tout […]
[Dataconomy] BigDataFr recommends: Hadoop and Spark: A Match Made in (Big Data) Heaven
BigDataFr recommends: Hadoop and Spark: A Match Made in (Big Data) Heaven […]If you listen in on what people are talking about at Big Data conferences, chances are you’ll hear a lot of buzz around Hadoop and Spark. People often think of Hadoop and Apache Spark as key tools for tackling a wide range of […]
[Kdnuggets] BigDataFr recommends: The Data Science Process
BigDataFr recommends: The Data Science Process What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem. At Springboard, our data students often ask us questions like […]
[Data Science Central] BigDataFr recommends: 19 Worst Mistakes at Data Science Job Interviews
BigDataFr recommends: 19 Worst Mistakes at Data Science Job Interviews […] This applies to many tech job interviews. But here we provide specific advice for data scientists and other professionals with a similar background. More advice is being added regularly. Here’s the list: 1) Not doing any research on the company prior to the interview. […]