STAT635 Advanced Computational Statistics
| Course Code: | 2460635 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| 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 aims to provide students with a rigorous foundation in computational statistics, combining theory with applied research practice. It covers key methods such as MCMC, EM, bootstrap, importance sampling, optimization, and simulation techniques, emphasizing both their theoretical basis and practical implementation for complex statistical challenges. The course also trains students to assess algorithm efficiency and scalability in high-dimensional or large-data contexts, and to apply these tools in an independent, project-based research setting that culminates in clear and professional scholarly communication.
Course Content
Exploring multidimensional data. Discovering structure in data. Bootstrapping basics and dependent data. Data partitioning. Statistical pattern recognition: classifiers and clustering. Bivariate and multivariate smoothing. Nonparametric regression models. Advanced topics in Markov Chain Monte Carlo (MCMC).
Course Learning Outcomes
By the end of this course, students will be able to explain and apply advanced computational algorithms (e.g., MCMC, EM, importance sampling, bootstrap, optimization, numerical integration) to complex statistical models; implement and evaluate statistical computing methods using appropriate programming languages (e.g., R, Python, or C++), ensuring both accuracy and efficiency; critically analyze algorithm performance in terms of convergence, stability, scalability, and suitability for high-dimensional or large-scale data problems; compare and justify methodological choices by linking algorithmic properties with theoretical foundations in statistical inference; design and conduct simulation studies to assess the robustness and reliability of computational methods in applied contexts; collaborate on interdisciplinary research projects, applying computational statistics to real-world datasets from diverse domains; and lastly, communicate statistical findings effectively.
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