Archive for ‘Data Science’ Category

22
Feb
Big Data: future challenges
  • Synergic Partners
  • 9 Views
  • 0 Comment
  • Big Data & Business . Data Analytics . Data Science . digital transformation . Estrategia Empresarial . Innovation .

The path to success is not something simple, it always requires a level of work and effort to achieve it. Big Data technologies, presenting a great potential both for the present and in the future, are in constant transformation and expansion. The evolution of Big data and the level of adoption of these technologies by companies (depending on their level of maturity Big Data), makes us meet with different challenges in the medium term. To avoid limitations in this regard we must study some points that may imply a challenge in the future. Shortage of Big data experts Interestingly, one of the biggest difficulties that those companies that already develop Big Data initiatives is to find professionals with enoughtraining and…

09
Feb
Towards the Fourth Industrial Revolution
  • Carlos Lorenzo
  • 220 Views
  • 0 Comment
  • Big Data . Digitalización . Eficiencia operativa . Fabricación Aditiva . Industria 4.0 . Inteligencia Artificial . IoT . Realidad Aumentada . Revolución Industrial . Transformación digital .

The customization of mass production is the catalyst for change that, among other things, originates industry 4.0 Lot has passed since the steam engine changed the course of our civilization history with the mechanization of production in the First Industrial Revolution. Scientific advances at the end of the 19th century allowed the Second Industrial Revolution with the discovery of electricity that would be the basis for mass production, followed by a Third Industrial Revolution in the 20th century thanks to the power of information technology and electronics in the automation of production processes. Today, a Fourth Industrial Revolution is brewing, product of the merge of a series of exponential technologies such as Big Data, Artificial Intelligence, the Internet of Things…

30
Jan
From Sensorization to Industry 4.0
  • Maria Muñoz
  • 358 Views
  • 0 Comment
  • Advanced Analytics . Analítica Avanzada . Big Data . Industria 4.0 . IoT . Sector Industrial . Sensorización . Transformación digital .

The industrial sector is currently immersed in a process of change that makes evident the need to evolve its business model towards a less traditional business model. In recent years, digital technologies have appeared that can help the industry adapt to market demands. The incorporation of these technologies in industrial processes becomes key to remain relevant within the sector. Thanks to the integration of these technologies, focused on IoT and the analysis and collection of information on the own instrumentation of the machines, the industrial sector will be enriched with the appearance of a huge amount of information not contemplated nor analyzed to date. This analysis will significantly increase the knowledge that companies have on their own processes and their environment,…

11
Jan
The Deep Learning Hype
  • Alfonso Ibañez
  • 637 Views
  • 0 Comment
  • Algoritmos . Big Data . Computación . Data . Data Science . Data Scientists . Deep Learning . Deep Learning Hype . Fiebre Deep Learning . IA . Inteligencia Artificial . Machine Learning . Neural Networks . Redes Neuronales .

In the era of Big Data a day does not pass by without us reading some news about Artificial Intelligence, Machine Learning or Deep Learning, never knowing what they refer to. The "experts" of the sector mix and exchange the terms with all naturalness, and only contribute to their hype. The simple fact of mentioning them catches the attention of investors and convinces them that these techniques have an almost magical power. Machine Learning is a scientific discipline coming from Artificial Intelligence, that studies how systems can be programmed to learn and improve with experience without human intervention. To address this problem, new paradigms emerge daily that allow the discovery of knowledge based on specific data deriving from solid statistical…

06
Oct
ADRIÁN SUÁREZ ARMAS, TAMER OF DATA
  • Synergic Partners
  • 1936 Views
  • 0 Comment
  • Analítica de Datos . Big Data . Big Data Consulting . Consultoría Big Data . Data Scienc . Synergic Partners .

 PUBLISHED INTERVIEW BY EL DIARIO LAS PALMAS | LA PROVINCIA, SUNDAY SEPTEMBER 3 2017 Adrian Suárez Armas, from the municipality Arucas of Gran Canary and 'Team Leader' at the technological consultancy firm Synergic Partners, has 27 years and billions of data to offer through one of the leading companies in Europe specializing in Big Data. The future that is almost here and by which companies’ approach with surgical precision to the needs of consumers. Industrial Engineer specialized in Construction Mechanics by the University of Las Palmas de Gran Canaria, Suárez Armas faces a field so avant-garde that it goes ahead of the European Union's own regulation and demands the necessity of an ethics in the treatment of data that does not…

20
Sep
«They say that being a Data Scientist is the sexiest profession of the century»
  • Synergic Partners
  • 2055 Views
  • 0 Comment
  • Big Data . Carrera Profesional . Data Science . Data Scientist . Synergic Partners .

PUBLISHED INTERVIEW BY THE DIARY INFORMACIÓN ON TUESDAY, 12th SEPTEMBER 2017 Lourdes Pascual graduated with a degree in Mathematics from the University of Alicante in 2011 and later she finished a Master’s in Mathematical Engineering at Complutense in Madrid. She currently works as a Data Scientist for a consultancy firm within the Telefonica, and yesterday, she shared her experience with teachers and students at UA. P. As a Data Scientist, what do you do? I work at Synergic Partners, a technologic consultancy firm that is part of Grupo Telefonica and that is dedicated to exploiting data and extracting value from it through Machine Learning techniques. P. And what does that look like? There is an immense amount of data that needs to…

16
Aug
Anticipating Customer Churn through Levers of Retention
  • Rubén Granados
  • 2539 Views
  • 0 Comment
  • Big Data . Churn Prediction . Data Science . Fuga de Clientes . Machine Learning . Palancas de Retencion .

Identification and analysis of signs of customer churn Knowing which customers might not continue as such has been the objective of classic customer churn models, which final output consisted of the probability of loss associated with each customer over a specific temporary time range. However, once we know the customer is going to leave, we must ask ourselves: What can we do to keep them? And taking it even further: What can we do to keep the customer from even considering leaving? Preventive Measures for Customer Churn in the Banking Sector At Synergic Partners, we have carried out a project for one of the largest banking entities in Spain responding to these questions with a methodology based on the combination…

30
May
Improving Effectiveness at a Call Center with Machine Learning
  • Víctor Gonzalez
  • 3367 Views
  • 0 Comment
  • Algorithms . Big Data . Data Science . Machine Learning . Procesamiento de Lenguaje Natural .

Understanding our clients and giving the best response to meeting their needs in the shortest amount of time possible is key to improving satisfaction and engagement with the company. The problem begins when the number of clients is high and we are receiving hundreds or thousands of messages per day. In this situation, we have two problems to solve. First, we have to prioritize which messages we are going to respond first and, second, we have to understand what it is that they are saying. It is clear that some messages will be more important or more urgent than others and that it will not always be easy to prioritize. To make things easier, we can use Machine Learning techniques…

18
May
Predicting crime: fact or fiction?
  • Santiago González
  • 3471 Views
  • 0 Comment
  • Big Data . Crime Predicction . Innovation . Predicción de crímenes .

According to the OMS, one of the main worries of global society today is violence and crime, making avoiding crime and creating centers of intelligence to do so the main objective for police and judicial systems. In this sense, as director of innovation and influenced by science fiction, I set out to analyze this situation. Using the traditional scientific method, the first step was to ask questions and hypothesize: Is it possible to predict the place, date and time of a crime? And if so, to what extent, in what kind of detail? Do we have experience with this? Who can offer us information? Has anyone done this before and, if so, what were the results? Thanks to our previous…

17
Sep
Warnings about normalizing data
  • Santiago Morante
  • 3862 Views
  • 2 Comments
  • No tags

For many machine learning algorithms, normalizing the data of analysis is a must. A supervised example would be neural networks. It is known that normalizing the input data to the networks improve the results. If you don't believe me it's ok (no offense taken), but you may prefer to believe Yann Le Cunn (Director of AI Research in Facebook and founding father of convolutional networks) by checking section 4.3 of this paper. You can catch up the idea with the first sentence of the section: Convergence [of backprop] is usually faster if the average of each input variable over the training set is close to zero. Among other things, one reason is that when the neural network tries to correct…

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