STAT554 COMPUTATIONAL STATISTICS
| Course Code: | 2460554 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 7.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Prof.Dr. VİLDA PURUTÇUOĞLU |
| Offered Semester: | Fall and Spring Semesters. |
Course Objectives
This course provides master’s students with a rigorous understanding of computational methods in statistics, emphasizing both theoretical foundations and practical implementation. Students will learn to develop and apply computational algorithms—including numerical optimization, Monte Carlo simulation, randomization techniques, and graphical methods—to analyze complex datasets. The course equips students with the skills to implement these methods using programming tools such as R, MATLAB, C, or Python, and to interpret and communicate results effectively in applied research and data-driven decision-making contexts.
Course Content
Overview of statistical distributions, generating random variables, exploratory data analysis, Monte Carlo (MC) method for statistical inference, data partitioning, resampling, bootstrapping, nonparametric density estimation.
Prerequisite: Consent of Department.
Course Learning Outcomes
By the end of this course, students will be able to apply computational algorithms in statistics, including numerical optimization, Monte Carlo simulation, randomization techniques, and graphical methods; implement statistical methods using programming tools such as R, MATLAB, Python, or C to solve complex data analysis problems; evaluate algorithm performance in terms of efficiency, accuracy, and suitability for different types of datasets; analyze and interpret results from computational procedures, drawing valid statistical conclusions; communicate findings effectively, both in written reports and oral presentations, for research or applied data-driven contexts; design and conduct reproducible computational experiments for applied statistical problems; and lastly, critically compare and select computational approaches, considering trade-offs between theoretical rigor, computational cost, and practical applicability.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Ability for converting theoretical, methodological, and computational statistical knowledge into analytical solutions in researches requiring statistical analyses. | ✔ | |||
| 2 | Ability for specifiying problems in real life situations bearing uncertainty, forming hypotheses, modeling, application, and interpreting the results. | ✔ | |||
| 3 | Ability for using current technology, computer softwares for statistical applications, computer programming for specific problems when necessary, writing computer codes for speeding up statistical calculations, organizing and cleaning databases, and preparing them for statistical analyses, and data mining. | ✔ | |||
| 4 | Ability for taking part in intra/inter disciplinary team work, efficient use of time, taking responsibility as a team leader, and entrepreneurship. | ✔ | |||
| 5 | Ability for taking responsibility in solitary work and producing creative solutions. | ✔ | |||
| 6 | Ability for keeping up-to-date with current advancements in statistical sciences, doing research, being open-minded, and adopting critical thinking. | ✔ | |||
| 7 | Ability for effective communication both in Turkish and English in specification of statistical problems, analyes, and interpretation of findings. | ✔ | |||
| 8 | Ability for using the knowledge in the field of expertise for the welfare of the society. | ✔ | |||
| 9 | Ability for suggesting the researchers in a comprehensible way the appropriate statistical methods for problems in fields that use statistics such as economics, finance, industrial engineering, genetics, and medicine and apply if needed. | ✔ | |||
| 10 | Ability for catalyzing discussions and presentations, public speaking, making presentations, communicating topics of expertise to the audiance in a comprehensible way. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution