Information Package / Course Catalogue
| Business Administration | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Econ., Admin. and Social Sciences | ||||
| Course Code | ECON 337 | ||||
| Course Title in English | R Programming for Social Sciences | ||||
| Course Title in Turkish | Sosyal Bilimler için R programlama | ||||
| Language of Instruction | EN | ||||
| Type of Course | Laboratory Work | ||||
| Level of Course | Seçiniz | ||||
| Semester | Fall | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 133 hours per semester | ||||
| Number of Credits | 5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | Basic knowledge of statistics | ||||
| Co-requisites | None | ||||
| Registration Restrictions | None | ||||
| Overall Educational Objective | To familiarize learners with the basics of R programming language and basic data-handling procedures. | ||||
| Course Description | The course covers practical issues in statistical analysis which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. In addition, you will work with real data to investigate real policy questions such as inequality, financial instability, the future of work, environmental degradation, wealth creation and innovation. | ||||
| Course Description in Turkish | Bu ders R programlama, R’a veri aktarımı, R paketlerine ulaşma, R fonksiyonlarını kullanma, hata ayıklama ve bir R kodunu organize etme ve yorumlama gibi temel pratik istatistiki konuları içerir. İstatistiksel analiz konuları dersin örneklerini oluşturacaktır. Bunun yanı sıra, gerçek veri kullanarak eşitsizlik, finansal istikrar, iş güvenliği, çevre kirliliği, servet yaratımı ve inovasyon gibi gerçek politika soruları hakkında araştırma yapılacaktır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Understand the basic concepts such as data type and index in R 2) Conceptualize and create loops to solve different types of problems 3) Create their own customized functions 4) Construct tables and figures for descriptive statistics 5) Learn to understand new data sets and functions |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1) Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | |||||
| 2) Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | |||||
| 3) Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | |||||
| 4) Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | |||||
| 5) Demonstrates individual and professional ethical behavior and social responsibility | |||||
| 6) Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | |||||
| 7) Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | |||||
| 8) Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | |||||
| 9) Displays computer proficiency to support problem solving and decision-making | |||||
| 10) Demonstrates teamwork, leadership, and entrepreneurial skills | |||||
| 11) Displays learning skills necessary for further study with a high degree of autonomy |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | S | |
| 2) | Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | N | |
| 3) | Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | N | |
| 4) | Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | N | |
| 5) | Demonstrates individual and professional ethical behavior and social responsibility | S | |
| 6) | Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | N | |
| 7) | Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | H | |
| 8) | Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | S | |
| 9) | Displays computer proficiency to support problem solving and decision-making | S | |
| 10) | Demonstrates teamwork, leadership, and entrepreneurial skills | S | |
| 11) | Displays learning skills necessary for further study with a high degree of autonomy | S |
| Prepared by and Date | NAROD ERKOL , December 2023 |
| Course Coordinator | NAROD ERKOL |
| Semester | Fall |
| Name of Instructor | Asst. Prof. Dr. NAROD ERKOL |
Course Contents
| Week | Subject |
| 1) | Syllabus, Installing R, Installing R Studio, Datacamp Platform, Registering to Datacamp |
| 2) | Basics, vectors, matrices |
| 3) | Factors, dataframes, lists |
| 4) | Importing data in R |
| 5) | Importing data in R |
| 6) | Importing data in R |
| 7) | Loops |
| 8) | Functions |
| 9) | Apply family and Utilities |
| 10) | Projects |
| 11) | Projects |
| 12) | Projects |
| 13) | Projects |
| 14) | Projects |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | Through out the course, we will use two online resources: Datacamp platform and “Doing economics” by CoreEcon project. Links are given below: Datacamp Courses: https://www.datacamp.com/courses/free-introduction-to-r https://www.datacamp.com/courses/importing-data-in-r-part-1 https://www.datacamp.com/courses/importing-data-in-r-part-2 https://www.datacamp.com/courses/intermediate-r https://www.datacamp.com/courses/intermediate-r https://www.datacamp.com/courses/intermediate-r-practice Doing Economics: https://www.core-econ.org/doing-economics/book/text/0-3-contents.html | |||||||||||||||
| Teaching Methods | Active learning Flipped learning | |||||||||||||||
| Homework and Projects | Pre-lecture and In-lecture assignments and two projects | |||||||||||||||
| Laboratory Work | The course is a lab-based. | |||||||||||||||
| Computer Use | Yes | |||||||||||||||
| Other Activities | ||||||||||||||||
| Assessment Methods |
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| Course Administration |
erkoln@mef.edu.tr 02123953670 Course Instructor: Asst. Prof. Narod Erkol (erkoln@mef.edu.tr) Attendance/participation: Students are expected to prepare for the lecture via assigned Datacamp lectures and reading materials. Students are responsible to follow the announcements, course materials available on Blackboard system. Formal use of e-mails: Students are expected to use their @mef accounts for email traffic. The instructor is only responsible for the information sent/received through Blackboard system and emails using @mef account. The course instructor assumes that any information sent through email will be received in 24 hours, unless a system problem occurs. Grading and evaluation: Evaluation will be based on the student learning outcomes. It is strongly recommended to complete all the work in a timely fashion. Late submissions will not be accepted. Missing projects: No make up unless a legitimate proof of absence is presented. Missing final exam: Faculty regulations. Academic integrity: All students of MEF University are expected to be honest and comply with academic integrity. Students are expected to do their own work and neither give nor receive unauthorized assistance. Disciplinary action will be taken in case of suspicion. Improper behavior, academic dishonesty and plagiarism: Law on Higher Education Article 54. Important: If the learner cannot collect at least 30 points from the activities other than the final exam, they can not take the final exam and will get an FZ grade. |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Laboratory | 14 | 3 | 2 | 70 | |||
| Homework Assignments | 3 | 0 | 12 | 36 | |||
| Final Examination | 1 | 25 | 2 | 27 | |||
| Total Workload | 133 | ||||||
| Total Workload/25 | 5.3 | ||||||
| ECTS | 5 | ||||||