BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| COMPUTER PROGRAMMING (TURKISH) | |||||
| Associate | TR-NQF-HE: Level 5 | QF-EHEA: Short Cycle | EQF-LLL: Level 5 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE5022 | Applied Statistics | Spring Fall |
3 | 0 | 3 | 9 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | English |
| Type of course: | Non-Departmental Elective |
| Course Level: | Associate (Short 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. |
Learning Outcomes
|
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. |
Course Content
| 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. |
Weekly Detailed Course Contents
| 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 |
Sources
| 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 |
Evaluation System
| 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 | |
ECTS / Workload Table
| 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 | |
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students will have knowledge of fundemantals of mathemetaics and phsics | |
| 2) | Students will have konowledge of relevant software and hardware requirements in the office environment | |
| 3) | Having the ability to define and explain the fundemental concepts, principles and essentilas of software and having the ability to develop software. | |
| 4) | Having the ability to use tools, machines and having the ability to recognize and diagnose problems in the related computer fields. | |
| 5) | Having the ability to communicate efficiently in verbal and written Turkish, to know at least one foreign language in order to communicate with the colleagues and customers. | |
| 6) | Having the ability to setup,diagnose and maintanance of databases | |
| 7) | Having the ability to design graphical animations for desktop applications and internet programming. | |
| 8) | Having the ability to develop and control internet projects | |
| 9) | Having the ability to develop group projects, team work, software and hardware projects | |
| 10) | Having the ability to set up,diagnose operating systems and network systems |