STAT102 INTRODUCTION TO STATISTICS AND DATA SCIENCE II
| Course Code: | 2460102 |
| 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. CEYLAN YOZGATLIGİL |
| Offered Semester: | Fall and Spring Semesters. |
Course Objectives
The course aims at introducing the students with inferential statistics in the data science and its applications to decision making in distinct data types.
Course Content
Basic statistical analyses in different types of data. Sampling distributions of distinct data sources. Inferential statistics in the data science. Estimation, confidence intervals and hypothesis testing under various data types. Distribution fitting and analysis of variance for one factor design in a given dataset. Linear regression and association between two categoric variables. Basic nonparametric procedures under various data types.
Course Learning Outcomes
After successful completion of this course, students will be able to:
- Describe basic procedure to construct confidence interval and perform a hypothesis test.
- Evaluate the difference between two population means and proportions by constructing a confidence interval and conducting hypothesis test.
- Evaluate the difference between more than two population means by performing an analysis of variance (ANOVA) and multiple comparisons of means.
- Complete a simple linear regression analysis based on two numerical variables.
- Complete a chi-square test based on one and two categorical variables.
- Complete rank tests in nonparametric Statistics.
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