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| Course Description |
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| Course Name |
: |
Statistical Computing |
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| Course Code |
: |
ISB-561 |
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| Course Type |
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Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Assoc.Prof.Dr. ALİ İHSANGENÇ
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| Learning Outcomes of the Course |
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Perform statistical simulations of random variables on a computer.
Know Monte Carlo methods in statistical inference.
Know bootstrap and jackknife methods.
Know Monte Carlo integration and variance reduction.
Know Monte Carlo methods in statistical inference.
Know MCMC methods in Bayesian statistics.
Know how to find the maximum likelihood estimates of parameters and use EM algorithm.
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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 exploit statistical concepts in data analysis by using a software such as R. |
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| Course Contents |
: |
R basics, univariate and multivariate data plots, random variate generation, simulation, Monte Carlo integration, Monte Carlo methods in statistical inference, bootstrap and jackknife, MCMC, maximum likelihood method, method of moments, EM algorithm |
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| Language of Instruction |
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Turkish |
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| Work Place |
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Seminar room in the Department of Statisitics |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Introduction to R system, script files, packages, graphs |
Reading the references |
Lecture, discussion and computer application |
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2 |
Methods of generating random numbers, inverse transform method |
Reading the references |
Lecture, discussion and computer application |
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3 |
Accept-reject method, transformation method |
Reading the references |
Lecture, discussion and computer application |
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4 |
Multivariate data graphs |
Reading the references |
Lecture, discussion and computer application |
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5 |
Contour plots |
Reading the references |
Lecture, discussion and computer application |
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6 |
Monte Carlo integration |
Reading the references |
Lecture, discussion and computer application |
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7 |
Variance reduction |
Reading the references |
Lecture, discussion and computer application |
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8 |
Midterm exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
|
9 |
Monte Carlo methods in inference |
Reading the references |
Lecture, discussion |
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10 |
Bootstrap and jackknife |
Reading the references |
Lecture, discussion and computer application |
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11 |
Permutation tests |
Reading the references |
Lecture, discussion and computer application |
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12 |
MCMC methods |
Reading the references |
Lecture, discussion and computer application |
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13 |
Probability density function estimation |
Reading the references |
Lecture, discussion and computer application |
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14 |
Maximum likelihood method |
Reading the references |
Lecture, discussion and computer application |
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15 |
EM algorithm |
Reading the references |
Lecture, discussion and computer application |
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16/17 |
Final exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Maria L. Rizzo, Statistical Computing with R, Chapman & Hall/ CRC, 2008.
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| |
| Required Course Material(s) | |
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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 |
6 |
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
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Possess advanced level of theoretical and applicable knowledge in the field of Probability and Statistics. |
0 |
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2 |
Conduct scientific research on Mathematics, Probability and Statistics. |
0 |
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3 |
Possess information, skills and competencies necessary to pursue a PhD degree in the field of Statistics. |
4 |
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4 |
Possess comprehensive information on the analysis and modeling methods used in Statistics. |
5 |
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5 |
Present the methods used in analysis and modeling in the field of Statistics. |
5 |
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6 |
Discuss the problems in the field of Statistics. |
0 |
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7 |
Implement innovative methods for resolving problems in the field of Statistics. |
0 |
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8 |
Develop analytical modeling and experimental research designs to implement solutions. |
0 |
|
9 |
Gather data in order to complete a research. |
0 |
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10 |
Develop approaches for solving complex problems by taking responsibility. |
0 |
|
11 |
Take responsibility with self-confidence. |
0 |
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12 |
Have the awareness of new and emerging applications in the profession |
0 |
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13 |
Present the results of their studies at national and international environments clearly in oral or written form. |
0 |
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14 |
Oversee the scientific and ethical values during data collection, analysis, interpretation and announcment of the findings. |
0 |
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15 |
Update his/her knowledge and skills in statistics and related fields continously |
0 |
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16 |
Communicate effectively in oral and written form both in Turkish and English. |
0 |
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17 |
Use hardware and software required for statistical applications. |
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) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
6 |
5 |
30 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
20 |
20 |
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Total Workload: | 144 |
| Total Workload / 25 (h): | 5.76 |
| ECTS Credit: | 6 |
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