STAT291 STATISTICAL PROGRAMMING
| Course Code: | 2460291 |
| METU Credit (Theoretical-Laboratory hours/week): | 4 (3.00 - 2.00) |
| ECTS Credit: | 6.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Prof.Dr. ÖZLEM İLK DAĞ |
| Offered Semester: | Fall and Spring Semesters. |
Course Objectives
In this course, we aim to provide an introduction to R and offer insights into its functionality.
In an era of expanding data size and diversity, the importance of employing an effective programming language for data management and analysis has grown significantly. R stands out as one of the most widely used statistical programming tools among statisticians and data scientists. It is a free, open-source system, renowned for its robust capabilities in data visualization, statistical analysis, and modeling. R is compatible with Windows, Mac OS X, and Linux operating systems.
This course is designed to cover fundamental topics, including software installation, interface navigation, data importation, and data management for subsequent analysis. Throughout the course, we will illustrate each subject with practical examples, allowing you to explore the program's functionality. It's important to note that no prior computational experience is required to excel in this course.
An introduction to MATLAB is also provided in the last couple of weeks. The dept and length of MATLAB topics depend on the availability of lab computer's capacity.
Course Content
Introduction to statistical techniques in statistical software available in the department or on the campus. Managing and analyzing data using statistical database packages like R. Introduction to MATLAB with applications to matrix algebra.
Course Learning Outcomes
At the end of this course, the learner is expected to learn R and apply these software to solve the problems with the previously learnt statistical methods
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Applying the knowledge of statistics, mathematics and computer to statistical problems and developing analytical solutions. | ✔ | |||
| 2 | Defining, modeling and solving real life problems that involve uncertainty, and interpreting results. | ✔ | |||
| 3 | To decide on the data collection technique, and apply it through experiment, observation, questionnaire or simulation. | ✔ | |||
| 4 | Analysing small and big volumes of data and interpreting results. | ✔ | |||
| 5 | Utilizing up-to-date techniques, computer hardware and software required for statistical applications; developing software programs and numerical solutions for specific problems when necessary. | ✔ | |||
| 6 | Taking part in intradisciplinary and interdisciplinary teamwork, using time efficiently, taking leadership responsibilities and being entrepreneurial. | ✔ | |||
| 7 | Taking responsibility in individual work and offering authentic solutions. | ✔ | |||
| 8 | Following contemporary developments and publications in statistical science, conducting research, being open to novelty and thinking critically. | ✔ | |||
| 9 | Efficiently communicating in Turkish and English to define and analyze statistical problems and to interpret the results. | ✔ | |||
| 10 | Having a professional and ethical sense of responsibility. | ✔ | |||
| 11 | Developing computational solutions to statistical problems that cannot be solved analytically. | ✔ | |||
| 12 | Having theoretical background and developing new theories in statistics, building relations between theoretical and practical knowledge. | ✔ | |||
| 13 | Serving the society with the expertise in the field. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution