After such long learning journey of evolutionally growing Big Data technologies and Data Science techniques and applications; the objective of the group project is to put all what students have learned during the 4 courses of the diploma into a real-life end to end customer-like engagement to strengthen the expertise they have gained and acquired through their contribution over the two semesters

The capability of collecting and storing huge amounts of versatile data necessitate the development and use of new techniques and methodologies for processing and analyzing big data. This course provides a comprehensive covering of a number of technologies that are at the foundation of the Big Data movement. The Hadoop architecture and ecosystem of tools will be of special focus to this course

This course provides an introduction to machine learning and statistical data analysis. The first part of the course covers topics such as parameter estimation, hypothesis testing and regression analysis. The second part includes machine learning topics such as supervised learning; unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance

This course acts as an applied course where students can develop on their combined knowledge of BigData technologies (e.g. Hadoop, Spark, etc.) and Data Science (e.g. Statistics, Machine Learning, etc.) and understand how such combination is used to solve real-world applications. In addition to this main goal, the course has the additional goal of familiarizing students with the latest

This introductory course focuses on building the requisite understanding and skills necessary to start applying Deep Learning models. Special emphasis will be convolutional architectures, recurrent neural network and autoencoders. We will attempt varied applications areas, from image classification, to text generation, to signal processing.

Data Sciences is a fast evolving practice that apply several sciences, theories and techniques in solving complex data-related problems and develop applications that support transforming the way organizations do their activities. Machine learning techniques are at the core of data sciences and have proven great value in the context of the practical applications of Big Data solutions. In this

The course is divided into two parts. The first part provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning; unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); Neural Networks, Decision Trees, Local Models, Model selection, Combining