Subject: Subject Sylbus: Statistical Theory for Data Analysis - 097414 (Current)

Statistical Theory for Data Analysis - 097414
Credit
Points
3.0
Given In
Semester
b
Lecture Exercise Laboratory Project or
Seminar
House
Work
Weekly
Hours
2 1 1 4

Determination of the grade according to progress during the semester and a final examination.


Prerequisites: ( Introductio to Data Science 094201
and Introduction to Statistics 094423 )
or ( Introduction to Statistics 094423
and Introduction to Computer Science N 234221 )
or ( Introduction to Statistics 094423
and Introduction to Data Science and Engin. 094700 )


The Course Will Cover a Variety of Statistical Theory Topics Combined with Data Analysis. the Course Topics Include Multivariate Regression, Generalized Regression, Non-Parametric Statistics and Re-Sampling Methods, Decision Theory, Bayesian Statistics and and Methods of Handling Missing Data. at the End of the Course, the Students Will Be:
1. Able to Use Different Regression Methods
2. to Perform Statistical Inference on Datasets of Various Types Including Handling Missing Data.




Times and places of examinations 02/2020 2020/2021 Spring Semester
examination timedaydateSeason
Friday30.07.2021
Tuesday12.10.2021

Timetable to semester 02/2020 2020/2021 Spring Semester
RoomBuildingHourdayLecturerExercise
Lecture
no.Registering
Group
21608:30-10:30WednesdayAssociate Professor Goldberg YairLecture1011
Project11
15310:30-11:30WednesdayMr. Madmon OronExercise11
21608:30-10:30WednesdayAssociate Professor Goldberg YairLecture1012
Project12
21414:30-15:30MondayMr. Madmon OronExercise12


Textbooks
PublishedPublisherAuthorsBook
2004springerlarry wassermanall of statistics a concise course in statistical inference
2002wileyroderick j.a. little, donald b. rubinstatistical analysis with missing data

Created in 22/04/2021 Time 00:07:54