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Big Data Course: «Data Science, or how to deal with data-based truth and lies»

Big Data Course: «Data Science, or how to deal with data-based truth and lies»

From May 27th to May 28th, the ETSE will hold the Big Data course «Data Science, or how to deal with data-based truth and lies».

Due to the unprecedented growth of data during the last years, Data Science has become one of the critical disciplines of the 21st century. Although we have the tools to store and access huge amounts of information, the extraction of useful knowledge and its translation to optimized decisions is still a challenge that needs from a new generation of professionals that have been called Data Scientists. The course will be divided in two parts: a conference and a practical workshop.

The conference will present an overview of the Data Science field by analyzing its similarities with the scientific method. It will also discuss the connections of Data Science with Big Data. The last part of the talk will be devoted to identify some of the most frequent mistakes when performing data science and how to deal with this problem.

Regarding the workshop, the following tools for Data Science will be presented:

  1. Python tools for Data Science and Big Data: IPython, NumPy, Pandas, Blaze.

  2. Statistical Frequency Estimation: limitations and implications for the Big Data

  3. Bayesian Statistical Estimations

  4. Probabilistic Programming: PyMC

In order to take part of this workshop, participants should register following this link. Furthermore, every participant must bring his/her own laptop, with the Anaconda Python distribution previously installed on their systems.

Conference: Wednesday 27th, 12:00h, Assembly Hall, School of Engineering (ETSE)

Workshop: Thursday 28th, 16:00-19:30h, classroom A1, School of Engineering (ETSE)

Dr. Jordi Vitrià is a senior researcher and full professor at the Universitat de Barcelona. He received his Ph.D. degree from the Universitat Autònoma de Barcelona in 1990. Dr. Jordi Vitrià has more than 20 years of experience in working on Computer Vision and its applications to several fields. His research, when personal computers had 128KB of memory, was originally oriented towards digital image analysis and how to extract quantitative information from them, but soon evolved towards computer vision problems. After a postdoctoral year at the University of California at Berkeley in 1993, he focused on Bayesian methods for computer vision methods.

Now, he is the head of a research group working in visual object understanding. In 2007, he joined the Applied Mathematics and Analysis Department at the University of Barcelona (UB) as Full Professor, where he teaches an introductory course on Algorithms and advanced courses on Collective Intelligence, Computer Vision and Data Science. From April 2011 He is serving as Head of the Applied Mathematics and Analysis Department, UB.