Analysis of massive data sets
- 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
Forms of Teaching
Lecturer-driven classroom presentations with live demonstrations of how to implement theoretical concepts in softwareExams
Two written examsLaboratory Work
Several programming assignments that cover the topic of the course. Students individually implement given assignments in recommended language or tool, and periodically demonstrate the progress to teaching assistants.Consultations
Individual office hours with lecturers and assistants are organized on student's request.
|Type||Threshold||Percent of Grade||Threshold||Percent of Grade|
|Laboratory Exercises||50 %||35 %||0 %||0 %|
|Attendance||0 %||5 %||0 %||0 %|
|Mid Term Exam: Written||0 %||30 %||0 %|
|Final Exam: Written||0 %||30 %|
|Exam: Written||50 %||100 %|
Continuous Assessment: Min (Mid Term Exam: Written + Final Exam: Written + Lecture attendance and oral examination in classroom) = 50 %
Week by Week Schedule
- Introduction to Analysis of Massive Data Sets.
- The MapReduce Programming Model.
- Finding Similar Items in a Massive Data Set.
- Finding Frequent Itemsets in a Massive Data Set.
- Mining Data Streams.
- Computing NodeRank in a Massive Data Set Represented as Graph.
- Detecting Communities in Social Network graphs.
- Midterm exam.
- Finding clusters of similar entities in massive data sets.
- Recommendation Systems.
- Advanced topics in Recommendation Systems.
- Advertising on the Web.
- Dimensionality Reduction.
- Large-scale Machine Learning.
- Final exam.