This program is designed for students who have a strong interest in data analysis, both from a theoretical and practical point of view, and who want to develop their skills in using methods and tools that play an essential role in various scientific and engineering fields, and are in great demand in many industrial sectors.
The interpretation of "data science" in this program is that of an interdisciplinary track, merging contributions from computer science and statistics, and addressing numerous applied problems. In addition to its importance in scientific research and in many industries, the study of data analysis comes with its own challenges, such as the development of methods, algorithms and ultimately computer programs for making reliable inferences from vast amounts of highdimensionaland heterogeneous data. As a consequence, the Data Science and Engineering program is centered around statistics and machine learning, the disciplines to develop and understand data analysis algorithms, and the systems that allow storing and processing data.
Through the program, students will learn basic theoretical frameworks and apply statistics and
machine learning methods to many problems of interest, as well as develop the computer science skills required to understand, operate and extend data management and large scale distributed systems. Theoretical lectures are intertwined with many practical laboratory sessions, using sophisticated and unique tools such as the Eurecom Cloud Computing platform, and many different modern parallel processing and storage systems and software, such as Hadoop MapReduce, Apache Spark, Apache Spark MLLib, R, scikit, and many more.
Ultimately, students will also develop "domain knowledge" by following lectures in which examples of data analysis problems include analyzing massive quantities of text and images, modeling computer systems threats and evaluating the efficacy of countermeasures, forecasting human behavior when using mobile.
