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HU Credits:
3
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Statistics
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Or Zuk
Coordinator Office Hours:
Wed. 16:15-17:15
Teaching Staff:
Dr. Or Zuk
Course/Module description:
We will learn methods for analyzing big datasets
Course/Module aims:
Acquiring statistical and computational tools for performing statistics on large-scale data
Learning outcomes - On successful completion of this module, students should be able to:
Analyze datasets with millions of records and thousands of variables. Use in an efficient manner programs with parallel/cloud computing. Extract data from the web.
Attendance requirements(%):
0
Teaching arrangement and method of instruction:
Lectures, hands-on examples on the computer
Course/Module Content:
Working remotely in a unix environment/cloud computing, SQL, acquire data from the web.
Finding similarities: Hash functions, nearest neighbours
Distributed computing in a cloud environment
Analyzing network data: finding communities, sampling large graphs
Streaming data: online algorithms, A/B testing
Required Reading:
None
Additional Reading Material:
Leskovec, Rajaraman&Ullman (2014). Mining of massive datasets, Cambridge University Press
Tan, Steinbach, Karpatne and Kumar (2005). Introduction to Data Mining. Pearson Addison Wesley
Liu (2011). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications). Springer
White (2015). Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale. O'Reilly Media
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 75 %
Assignments 25 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
There will be a mid-term project during the semester that will comprise 25% of the course grade.
After the end of the semester there will be given a final project that will comprise 75% of the course grade.
In addition, there will be a few exercises for self-practice (not for grade)
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