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HU Credits:
3
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Statistics
Semester:
1st Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Or Zuk
Coordinator Office Hours:
Monday 10:30-11:30
Teaching Staff:
Dr. Or Zuk
Course/Module description:
We will learn and apply 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 and analyze data from the web.
Attendance requirements(%):
70
Teaching arrangement and method of instruction:
Lectures, hands-on demonstrations on the computer
Course/Module Content:
Working remotely in a cluster environment and/or cloud computing.
Database (SQL), information extraction from the web.
Finding similarities: Hash functions, nearest neighbours
Distributed computing
Analyzing network data: finding communities, sampling large graphs
Streaming data: online algorithms
Additional subjects as time permits
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
Grading Scheme :
Essay / Project / Final Assignment / Home Exam / Referat 75 %
Submission assignments during the semester: Exercises / Essays / Audits / Reports / Forum / Simulation / others 25 %
Additional information:
There will be a mid-term project and a few short check-list assignments during the semester that will comprise together 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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