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HU Credits:
2
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:
Wednesday 16-17
Teaching Staff:
Dr. Or Zuk
Course/Module description:
The course teaches principles of data analysis and computation-based statistical inference, with a secondary goal of teaching the R statistical computing language.
Students will learn visualization, data wrangling, sampling / simulation of probability models, and computer-based inference.
Course/Module aims:
The goals of the course:
1. Introduce and practice principles of data-analysis and statistical computing.
2. Develop independence of the students as data analysts and R users.
Learning outcomes - On successful completion of this module, students should be able to:
- Prepare, summarize and present data files in the R environment to answer research questions
- Study probabilistic models using simulations in R
- Use computer experiments to evaluate statistical methods.
Attendance requirements(%):
0
Teaching arrangement and method of instruction:
The course comprises of lectures, individual exercises, Labs, and a final home exam.
Course/Module Content:
1.Introduction to interactive and reproducible research with R-markdown and github
2. Data manipulation
3. Table manipulation
4. Summaries and visuals for a single file
5. GGplot environment and Data-viz principles
6. The regression line and transformations
7. Files and strings
8. Sampling in R
9. Monte carlo (complex probability models)
10. Computer-assisted Inference
Required Reading:
None
Additional Reading Material:
Can be used as reference:
http://www.john-ros.com/Rcourse/
Course/Module evaluation:
End of year written/oral examination 35 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 0 %
Assignments 50 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 15 %
weekly exercises (magen)
Additional information:
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