STAT564 ADVANCED STATISTICAL DATA ANALYSIS
| Course Code: | 2460564 |
| 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. ZEYNEP IŞIL KALAYLIOĞLU AKYILDIZ |
| Offered Semester: | Fall or Spring Semesters. |
Course Objectives
This course aims to:
- Introduce fundamental methods for analyzing experimental and observational data.
- Teach effective techniques for univariate and multivariate data visualization.
- Develop skills in exploratory data analysis, including data transformation and outlier detection.
- Cover diagnostic methods such as examining residuals and applying resistant lines and robust estimation.
- Present approaches for handling missing data and regularization techniques.
- Explore the analysis of categorical data and basic data mining concepts.
Course Content
Introduction to methods for analyzing experimental and observational data. Useful display of univariate and multivariate data. Exploratory data analysis. Transforming data. Detecting and handling outliers. Examining residuals. Resistant lines. Robust estimation. Approaches to handling missing data. Analysis of categorical data. Data mining.
Course Learning Outcomes
By the end of this course, students will be able to:
- Apply fundamental methods for analyzing experimental and observational data.
- Use effective visualization techniques for univariate and multivariate data.
- Perform exploratory data analysis, including data transformation and outlier detection.
- Conduct diagnostic checks using residual analysis and robust estimation methods.
- Handle missing data and apply regularization techniques such as LASSO, Adaptive LASSO, SCAD, Elastic net to improve models.
- Analyze categorical data using basic and advanced methods.
- Understand and apply basic data mining concepts to real datasets
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