| GASTRONOMY (TURKISH) | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE5022 | Applied Statistics | Fall | 3 | 0 | 3 | 9 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
| Language of instruction: | English |
| Type of course: | Non-Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assoc. Prof. SAEID KARAMZADEH |
| Course Lecturer(s): |
Prof. Dr. SELİM ZAİM |
| Recommended Optional Program Components: | none.......... |
| Course Objectives: | The course introduces fundamental topics in statistics and implements its applications to industrial, medical, financial, energy and similar type very large-size datasets to infer meaninful statistical results. The course is for gradute students with no significant background on this subject. Implementations will be performed on the open source statistical software R. Introduction to R programming will be given. |
|
The students who have succeeded in this course; I. Identify basic terms in statistics. II. Gain ability to use and apply basic methods and programming tools used in statistics over various engineering disciplines. III. Ability to explore data and its relationships. IV. Ability to perform hypothesis testing for statistical problems. V. Perform statistical inference over statistical data. |
| Topics include: Introduction to R programming, Sampling, Data Exploration, Exploring Relationships, Probability, Random Variables and Probability Distributions, Estimation, Hypothesis Testing, Statistical Inference, Multiple Testing Correction, ANOVA, Analysis of Categorical Variables, Regression Analysis, Bayesian Analysis, Survival Analysis, Over Representation Analysis, Meta Analysis. |
| Week | Subject | Related Preparation |
| 1) | Introduction | |
| 2) | Introduction to R statistical programming | |
| 3) | Term Project | |
| 4) | Data Exploration with R | |
| 5) | Visualizing and Summarizing Relationships | |
| 6) | Probability and Random Variables | |
| 7) | Estimation in datasets | |
| 8) | Hypothesis Testing for various engineering applications | |
| 9) | Statistical Inference over various large datasets | |
| 10) | ANOVA | |
| 11) | Analysis of Categorical Variables | |
| 12) | Regression and Bayesian Analysis | |
| 13) | Survival analysis | |
| 14) | Over Representation Analysis |
| Course Notes / Textbooks: | Principles of Applied Statistics (Paperback), by D. R. Cox, Christl A. Donnelly 2011 ISBN-10: 1107644453 | ISBN-13: 978-1107644458 |
| References: | Introductory Statistics with R Peter Dalgaard 2011 ISBN 978-0-387-79053-4 |
| Semester Requirements | Number of Activities | Level of Contribution |
| Project | 1 | % 30 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 30 | |
| PERCENTAGE OF FINAL WORK | % 70 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 14 | 42 |
| Project | 1 | 30 |
| Midterms | 1 | 40 |
| Final | 1 | 50 |
| Total Workload | 204 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | - Possess advanced level theoretical and practical knowledge supported by textbooks with updated information, practice equipments and other resources. | |
| 2) | Use of advanced theoretical and practical knowledge within the field. -Interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the field. | |
| 3) | Inform people and institutions, transfer ideas and solution proposals to problems in written and orally on issues in the field. - Share the ideas and solution proposals to problems on issues in the field with professionals and non-professionals by the support of qualitative and quantitative data. -Organize and implement project and activities for social environment with a sense of social responsibility. -Monitor the developments in the field and communicate with peers by using a foreign language at least at a level of European Language Portfolio B1 General Level. -Use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge. | |
| 4) | Evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach. -Determine learning needs and direct the learning. -Develop positive attitude towards lifelong learning. | |
| 5) | Act in accordance with social, scientific, cultural and ethic values on the stages of gathering, implementation and release of the results of data related to the field. - Possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also, environmental protection, worker's health and security. | |
| 6) | Conduct studies at an advanced level in the field independently. - Take responsibility both as a team member and individually in order to solve unexpected complex problems faced within the implementations in the field. - Planning and managing activities towards the development of subordinates in the framework of a project |