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| BILB3017 | Basic Statistical Algorithms | 4+0+0 | ECTS:4 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Elective | | Department | COMPUTER SCIENCE | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Eda ÖZKUL | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to teach students how to develop the algorithms for basic statistical methods and to equip them with knowledge about statistical data, data summarization, and analysis. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Understand basic concepts of statistics and their applications. | 1,2 | 1,4, | | LO - 2 : | Learn how to develop and implement algorithms for basic statistical methods. | 1,2 | 1,4, | | LO - 3 : | Gain proficiency in data summarization techniques. | 1,2 | 1,4, | | LO - 4 : | Develop skills in statistical data analysis and interpretation. | 1,2 | 1,4, | | LO - 5 : | Apply statistical algorithms to real-world data sets. | 1,2 | 1,4, | | LO - 6 : | Gain knowledge in developing modules in Python. | 1,2 | 1,4, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Descriptive statistics, normality tests, parametric and non-parametric tests, chi-square tests, corelation analysis |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic statistical concepts | | | Week 2 | Descriptive Statistics | | | Week 3 | Introduction to hypothesis testing, Normality tests | | | Week 4 | One sample t-test, Sign test, Wilcoxon test | | | Week 5 | Independent samples t-test | | | Week 6 | Mann-Whitney U test | | | Week 7 | Paired t-test | | | Week 8 | Wilcoxon test | | | Week 9 | Midterm exam | | | Week 10 | One-way ANOVA | | | Week 11 | Kruskal Wallis test | | | Week 12 | Repeated measures ANOVA | | | Week 13 | Friedman test | | | Week 14 | Chi-square Tests | | | Week 15 | Pearson correlation coefficient, Spearman rank correlation coefficient | | | Week 16 | Final exam | | | |
| 1 | Sheskin, D. J. 2003; Handbook of parametric and nonparametric statistical procedures, CRC Press. | | | 2 | Haslwanter, T. 2022; An Introduction to Statistics with Python with Applications in the Life Sciences, Springer. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1,5 | 50 | | End-of-term exam | 16 | | 1,5 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav için hazırlık | 3 | 2 | 6 | | Arasınav | 1.5 | 1 | 1.5 | | Dönem sonu sınavı için hazırlık | 3 | 2 | 6 | | Dönem sonu sınavı | 1.5 | 1 | 1.5 | | Total work load | | | 113 |
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