Welcome to the Moodle's page of Big Data Computing 2019-20!

Class Schedule
- Tuesday from 8:00AM to 10:00AM (Room Alfa, Via Salaria 113)
- Wednesday from 3:00PM to 6:00PM (Room Alfa, Via Salaria 113)

Course website:Click here

Description and Goals
The amount, variety, and rate at which data is being generated nowadays both by humans and machines are unprecedented. This opens up a number of challenges on how to deal with those data, as traditional computing paradigms are not conceived to operate at such a scale.

"Big Data" is the umbrella term that has rapidly become popular to describe methodologies and tools specifically designed for collecting, storing, and processing very large or complex data sets. In addition to addressing foundational computer science problems, such as searching and sorting, big data computing mainly focuses on extracting knowledge - thereby value - from large-scale data sets using advanced data analysis techniques, such as machine learning.

This course is intended to provide graduate-level students with a deep understanding of programming models and tools that are suitable for the large-scale analysis of data distributed across clusters of computers. More specifically, the course will give students the ability to proficiently develop big data/machine learning solutions on top of industry standard frameworks, such as Hadoop and Spark, to tackle real-world problems faced by the so-called "Big Five" tech companies (i.e., Apple, Amazon, Google, Microsoft, and Facebook): text/graph analysis, classification/regression, and recommendation, just to name a few.

Prerequisites
The course assumes that students are familiar with the basics of data analysis and machine learning, properly supported by a strong knowledge of foundational concepts of calculus, linear algebra, and probability and statistics. In addition, students must have non-trivial computer programming skills (preferably using Python programming language). Previous experience with Hadoop, Spark, or distributed computing is not required.

Exams
Students must prove their level of comprehension of the subject by developing a software project, leveraging the set of methodologies and tools introduced during classes. Projects must of course refer to typical Big Data tasks: e.g., clustering, prediction, recommendation using very-large datasets in any application domain of interest. The topic of the project must anyway be agreed with the professor in advance; references where to select interesting projects from will be however suggested throughout the course (e.g., Kaggle).
Projects can be done either individually or in group of at most 2 students, and they should be accompanied by a brief presentation written in english (e.g., a few PowerPoint slides). Finally, there will be an oral exam where submitted projects will be discussed in english; other questions on any topic addressed during the course may also be asked, but those can be answered either in english or in italian, as the student prefers.