Introduction to mining of massive data sets. The MapReduce programming model. Finding similar items. Mining data streams. Link analysis. Finding frequent itemsets. Clustering of massive data sets. Recommendation systems. Mining social-networks graphs. Advertising on the Web. Dimensionality reduction. Large-scale machine learning.
Learning Outcomes
 

•     Recognize and understand why certain problem belongs to Big Data category
•     Apply the MapReduce programming model when faced with certain problems in practice
•     design and evaluate system for finding similar items in a massive data set
•     design and evaluate system for finding frequent itemsets in a massive data set
•     design and evaluate system for node rank among graph represented massive data set
•     design and evaluate recommendation system
•     apply the appropriate clustering algorithms in order to identify clusters in a massive data set
•     apply the appropriate algorithms for processing data streams

Type of methodology: Combination of lecture and hands-on

Paid training activity for participants: Yes, for some only

Participants prerequisite knowledge: No prerequisite knowledge

Language: English and Croatian