COURSE UNIT TITLE

: COMPUTATIONAL STATISTICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5103 COMPUTATIONAL STATISTICS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

This course is about modern, computationally-intensive methods in statistics. The objective of the course is to introduce the students to the scientific computer programming languages (R, Python, etc.) which have powerful facilities for statistical computing.

Learning Outcomes of the Course Unit

1   An understanding of fundamental ideas of computational statistics
2   Use Monte Carlo techniques with statistical computation tools
3   Produce graphical displays with the statistical programming
4   An understanding of advanced structure of statistical programming
5   Develop students' analytical abilities and ability to present and criticize arguments

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to the statistical computation tools, Installing R, R environment, R Editors, R Project, CRAN
2 R syntax: variables, operators, expressions, assignment, statements. Data types, vector. Conditional execution
3 Data types, matrix, data.frame, lists. Data import / export
4 Fundamental graphical methods in computational statistics
5 Iterations and Convergence, Control structures, conditional statements, loops
6 Monte Carlo Methods for Statistical Inference, Generation of Random Numbers, Monte Carlo Estimation
7 Monte Carlo Methods for Statistical Inference, Random Sampling from Data
8 Monte Carlo Methods for Statistical Inference, Simulation methods
9 Monte Carlo Methods for Statistical Inference, Reporting Simulation Experiments
10 Subsetting and reshaping data, Splitting train end test data sets, simple validation, cross validation
11 Resampling, Jackknife and Bootstrap
12 Resampling, Jackknife and Bootstrap
13 R applications of various numerical method algorithms
14 Student presentations

Recomended or Required Reading

*Suggested Sources for the Course:
Textbook(s): :
1. Rizzo, M. L. (2007). Statistical computing with R. Chapman and Hall/CRC.
2. Gentle, J. E. (2009). Computational Statistics. Springer
Supplementary Book(s):
1. Braun, W. J., & Murdoch, D. J. (2016). A first course in statistical programming with R. Cambridge University Press.
2. Matloff, N. (2011). The art of R programming: A tour of statistical software design. No Starch Press.
Materials: Lecture Slides, R Manuals

Planned Learning Activities and Teaching Methods

The course consists of lecture and projects.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRS PRESENTATION
3 FCG FINAL COURSE GRADE ASG * 0.50 + PRS * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homeworks, presentation and report.

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the graduate policy at http://www.fbe.deu.edu.tr/

Contact Details for the Lecturer(s)

Dr. Engin YILDIZTEPE
DEU Faculty of Science Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
phone: +90 232 301 86 04

Office Hours

It will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 1 14
Preparation for final exam 1 60 60
Preparing assignments 1 25 25
Preparing presentations 1 35 35
Other activities within the scope of the atelier pratices 1 25 25
Final 1 2 2
TOTAL WORKLOAD (hours) 203

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1443353343
LO.2553343
LO.35334443
LO.4533454
LO.55555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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