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| Course Description |
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| Course Name |
: |
Statistical Analysis and Programming in R |
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| Course Code |
: |
ZO-656 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Prof.Dr. ZEYNEL CEBECİ
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| Learning Outcomes of the Course |
: |
The students learn how to install and use R software and associated libraries,
learn simple and advanced level statistical analysis with R,
learn simple and advanced level graphical analysis and modelling with R,
write basic level software,
develop and deploy statistical packages in R.
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
This course aims to teach basic and advanced statistical analysis; basic and advanced graphical analysis, and statistical programming and application development in R.
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| Course Contents |
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This content of this course includes using R for statistical computing and graphics; and developing R programmes for statistical data analysis. |
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| Language of Instruction |
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Turkish |
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| Work Place |
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Classroom |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Introduction to R, and use of R interface |
Downloading and installing R Software |
Exercise |
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2 |
Data types, data input and data manipulation |
None |
None |
|
3 |
Arithmetic and logical expressions |
None |
None |
|
4 |
Simple statistical analysis |
Studying descriptive statistics in textbooks |
Reading |
|
5 |
Simple graphs |
Studying matrices in textbooks |
None |
|
6 |
Introduction to programming R |
Reading some tutorials and articles about programming in R |
Reading |
|
7 |
Functions and libraries |
None |
None |
|
8 |
Introduction to graphics programming |
None |
None |
|
9 |
Midterm exam |
None |
None |
|
10 |
Advanced statistical analysis |
None |
None |
|
11 |
Advanced graphs |
None |
None |
|
12 |
Developing a program - Hierachical Clustering analysis |
Studying clustering analysis in the textbooks |
Reading |
|
13 |
Developing a program - Power analysis |
Studying power analysis in the textbooks |
Reading |
|
14 |
Packaging programs and deployment |
None |
None |
|
15 |
Final exam preperation |
None |
None |
|
16/17 |
Final exam |
None |
None |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Dalgaard, P. (2011). Introductory Statistics with R. Springer, 2nd edition.
Matloff, N. (2009). The Art of R Programming. http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf
Lumley, T. (2006). R Fundamentals and Programming Techniques. http://faculty.washington.edu/tlumley/Rcourse/R-fundamentals.pdf
R Programming at Wikibooks - http://en.wikibooks.org/wiki/R_Programming
An Introduction to R - http://cran.r-project.org/doc/manuals/R-intro.html
Murison, R. (2005). R programming guide. http://www.unt.edu/rss/R_Programming_Notes.pdf
Kabacoff, R: I (2011). R in Action : Data Analysis and Graphics with R. Manning Publications Co.ISBN 9781935182399, 472p
R Tutorial ebook. http://www.r-tutor.com/
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| |
| Required Course Material(s) |
List of R Statistical Packages - http://en.wikipedia.org/wiki/List_of_statistical_packages
R Graphical Manual. http://rgm3.lab.nig.ac.jp/RGM/
R Seek. http://rseek.org/
R books - http://www.r-project.org/doc/bib/R-books.html
The R Project for Statistical Computing (http://www.r-project.org/)
Girke, T. (2012). R & Bioconductor Manual. http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual
Programming R - Beginner to advanced resources for the R programming language. http://www.programmingr.com/
R Tutorial. http://r-statistics.net/r-tutorial.html
NCEAS(2012). R Programming Resource Center. http://www.nceas.ucsb.edu/scicomp/software/r
Quick R - http://www.statmethods.net/
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
60 |
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Homeworks/Projects/Others |
4 |
40 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
At the end of this programme, the students improve and deepen their knowledge in the field of Animal Science by building on the knowledge and competence acquired at the undergraduate level and can employ interdisciplinary interaction in their field of study. |
2 |
|
2 |
The students interpret and generate new information and theories in specific fields related to Animal Science using the theoretical and practical knowledge at masters level. Also, they can reveal the cause-effect relationship regarding the problems in their field of study and employ scientific research methods to generate possible solutions. |
3 |
|
3 |
The students independently identify potential problems and carry out research studies aiming at solutions in the field of Animal Science. Also, they investigate and develop strategic approaches for potential problems that may arise related to the particular studies. |
3 |
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4 |
The students access and compile information about the latest developments and fundamental sources in the particular field and reach a new synthesis by evaluating and interpreting the existing research. They can make use of this acquired knowledge to practice the profession effectively and follow the improving implementations in the field. |
5 |
|
5 |
The students use information in the field of Animal Science, through compiling, interpreting and synthesising it, in order to make social contributions. They make evaluations by creating a plan and framework and taking specific total quality criteria into consideration. They use the skills and knowledge acquired in the field of Animal Science in joint projects with other disciplines. |
4 |
|
6 |
The students discuss and pass on the acquired knowledge based on their work in the field by making written and oral presentations. They have speaking and writing competence in at least one foreign language at a level that enables them to keep up with the requirements of the age. They express their ideas clearly using the tools of information and communication technologies. |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
13 |
4 |
52 |
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Out of Class Study (Preliminary Work, Practice) |
5 |
10 |
50 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
4 |
10 |
40 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
4 |
4 |
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Final Exam |
1 |
4 |
4 |
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Total Workload: | 150 |
| Total Workload / 25 (h): | 6 |
| ECTS Credit: | 6 |
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