STAT412 STATISTICAL DATA ANALYSIS
| Course Code: | 2460412 |
| METU Credit (Theoretical-Laboratory hours/week): | 4 (3.00 - 2.00) |
| ECTS Credit: | 8.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Prof.Dr. CEYLAN YOZGATLIGİL |
| Offered Semester: | Spring Semesters. |
Course Objectives
This is an applied course - applications of statistics in many different fields will be covered. The objectives of this course are to enable the students to understand how to think about data, to learn how to clean data and prepare for the analysis, to be able to handle graphical and methodological ways to highlight what is going on in data, summarize relationships in data using statistical models, and demonstrate the ability to highlight structure in data by doing so. Students will see many analyses of real data, and will spend lots of time doing their own statistical analyses of real data using the computer and learning to interpret the results of those analyses. In the recitation hours, the applications will be handled with R under Anaconda and Jupyter Notebook.
I strongly urge you to bring in to me examples of statistics, probabilistic reasoning, and so on, that you see in newspapers or magazines, whether they are directly relevant to material being discussed in class or not. Such material might very well then be incorporated into class discussion.
I strongly urge you to bring in to me examples of statistics, probabilistic reasoning, and so on, that you see in newspapers or magazines, whether they are directly relevant to material being discussed in class or not. Such material might very well then be incorporated into class discussion.
Course Content
Types of data. Graphical and tabular represantation of data. Approaches to finding the unexpected in data. Exploratory data analyses for large and high-dimensional data. Analysis of categorical data. Elements of robust estimation. Handling missing data. Smoothing methods. Classification and Clustering applications, Application of Principle Component Analysis and Factor analysis, Use of regression trees for claasification and prediction.
Course Learning Outcomes
- To develop quantitative reasoning
- To develop statistical reasoning and methodology provide the tools to become numerate.
- How to think about randomness, and about data
- Explain the role of data and models in the decision-making process and how they support (rather than determine) decisions
- Decide what tools are appropriate in di?erent data and decision-making settings
- Decide how to structure model so that the essential elements are included and that the model can be analyzed in a timely fashion
- Translate the results of the model into a statement about the data relationships and of the actions that could be taken with the new understanding.
- To understand how machine learning algorithms work for classification and prediction purposes.
- Running R and doing applications under Anaconda and Jupyter Notebook environment.
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