Big Scale Analytics
A.Y. 2018/2019
Learning objectives
The course aims at introducing the bases of data analysis techniques suited to process datasets whose dimension does not allow them to be stored on a single standard computer.
Expected learning outcomes
Students will acquire advanced skills allowing them to use distributed systems for storing and processing data, as well as to design and implement systems for analysis of massive datasets and to use algorithmic techniques suited for the problems of similar item identification, recommendation systems, link analysis, stream analysis, market-basked analysis, clustering, and machine learning.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Milan
Responsible
Lesson period
First semester
ATTENDING STUDENTS
Course syllabus
NON-ATTENDING STUDENTS
Large scale file systems
Map-Reduce algorithms
Hadoop and Spark frameworks
Schema-less data bases
Finding similar items
Mining data streams
Link analysis
Frequent itemsets
Clustering
Recommendation systems
Large-scale machine learning
Map-Reduce algorithms
Hadoop and Spark frameworks
Schema-less data bases
Finding similar items
Mining data streams
Link analysis
Frequent itemsets
Clustering
Recommendation systems
Large-scale machine learning
Course syllabus
Large scale file systems
Map-Reduce algorithms
Hadoop and Spark frameworks
Schema-less data bases
Finding similar items
Mining data streams
Link analysis
Frequent itemsets
Clustering
Recommendation systems
Large-scale machine learning
Map-Reduce algorithms
Hadoop and Spark frameworks
Schema-less data bases
Finding similar items
Mining data streams
Link analysis
Frequent itemsets
Clustering
Recommendation systems
Large-scale machine learning
Professor(s)