| TYPE OF COURSE UNIT | Elective Course |
| LEVEL OF COURSE UNIT | Master's Degree Without Thesis |
| YEAR OF STUDY | - |
| SEMESTER | - |
| NUMBER OF ECTS CREDITS ALLOCATED | 10 |
| NAME OF LECTURER(S) | Professor Nizami Gasilov
Assistant Professor Elmas Burcu Mamak Ekinci
|
| LEARNING OUTCOMES OF THE COURSE UNIT |
At the end of this course, the students;
1) Select data collecting technique, prepare raw data for analysis.
2) Study with numeric data, use statistical data analysis tools.
3) Use any data analysis tool and/or a statistical packaged software.
4) Define statistical analysis results and explanation of executive effects of it.
5) Make reliability analysis of results.
6) Report analysis results.
|
| MODE OF DELIVERY | Face to face |
| PRE-REQUISITES OF THE COURSE | No |
| RECOMMENDED OPTIONAL PROGRAMME COMPONENT | None |
| COURSE DEFINITION | Preparing the data and making it ready for analysis. To be able to determine the appropriate analysis methods and to apply these methods. To gain the ability to use at least one programming language or package program and report the results. |
| COURSE CONTENTS | | WEEK | TOPICS |
|---|
| 1st Week | Course introduction. Basic definitions and concepts. | | 2nd Week | Introduction to R and R Studio. Data structures and data input in R. | | 3rd Week | Data structures in R: Vectors, matrices, arrays. Data frame and lists. Operations on matrices and vectors | | 4th Week | Descriptive statistics: Calculating frequency tables and descriptive statistics with R | | 5th Week | Data visualization | | 6th Week | Some discrete and continuous distributions. R applications | | 7th Week | Analysis of normality and evaluation of assumptions and application of R | | 8th Week | Mid-term exam. | | 9th Week | Parametric tests (one and two samples). R applications | | 10th Week | Analysis of variance. R applications | | 11th Week | Non-Parametric tests. R applications | | 12th Week | Chi-square independence test - R applications. | | 13th Week | Correlation analysis-R applications | | 14th Week | Linear Regression analysis-R applications. |
|
| RECOMENDED OR REQUIRED READING | (1) Cotton, R. (2013). Learning R. O?Reilly Media, Inc.
(2) Fischetti, T. (2015). Data Analysis with R. Packt Publishing.
(3) Demir, İ (Editör). (2017). R ile Uygulamalı İstatistik. Papatya Bilim.
(4) Peter Daalgard (2008). Introductory Statistics with R, Springer.
(5) Gareth J., Witten D., Hastie T., Tibshirani R. (2013). An Introduction to Statistical Learning with Applications in R. Springer. |
| PLANNED LEARNING ACTIVITIES AND TEACHING METHODS | Lecture,Questions/Answers,Problem Solving,Practice |
| ASSESSMENT METHODS AND CRITERIA | | | Quantity | Percentage(%) |
|---|
| Mid-term | 1 | 30 | | Assignment | 2 | 10 | | Project | 1 | 20 | | Total(%) | | 60 | | Contribution of In-term Studies to Overall Grade(%) | | 60 | | Contribution of Final Examination to Overall Grade(%) | | 40 | | Total(%) | | 100 |
|
| ECTS WORKLOAD |
| Activities |
Number |
Hours |
Workload |
| Midterm exam | 1 | 2 | 2 | | Preparation for Quiz | | | | | Individual or group work | | | | | Preparation for Final exam | 1 | 70 | 70 | | Course hours | 14 | 3 | 42 | | Preparation for Midterm exam | 1 | 40 | 40 | | Laboratory (including preparation) | | | | | Final exam | 1 | 2 | 2 | | Homework | 2 | 15 | 30 | | Project | 1 | 120 | 120 | | Total Workload | | | 306 |
|---|
| Total Workload / 30 | | | 10,2 |
|---|
| ECTS Credits of the Course | | | 10 |
|---|
|
| LANGUAGE OF INSTRUCTION | Turkish |
| WORK PLACEMENT(S) | No |
| | |
