BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| ECONOMICS | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| ECO2867 | Data Science with R | Fall | 3 | 0 | 3 | 6 |
| 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: | Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assoc. Prof. OZAN BAKIŞ |
| Course Objectives: | This course aims to teach basics of R programming language and how to use R for data analysis to students without any programming background. This involves not only an efficient use of the R statistical software but also an effective programming in the R language for data cleaning, data analysis and visualization. The goal of the course is to draw insights and information from raw data using R. |
Learning Outcomes
|
The students who have succeeded in this course; 1. How to install and customize R to their own PC 2. Basic knowledge and working of R language 3. How to find ans install ready-to-use R packages 4. How to find and download data from internet 5. How to manipulate and clear data to make it ready for statistical analysis 6. Write reproducible and dynamic reports with R |
Course Content
| After learning how to install the required programs (R and RStudio) for the programming language R, basic programming concepts will be taught. Then, using these concepts, students will learn how to clean and analyze the data. The course is based on "learning by doing" approach. For this reason, students will be encouraged to make mistakes and learn from their mistakes. To reinforce this learning process the final exam will take the following form: students will form groups of 2-3 people in the first 4 weeks and select a data and a topic with the professor of the course. The grading of the final exam will rely on this project that students have been working on throughout the semester. Students are required to, first, make an oral presentation (10 minutes maximum) where students present their findings. Then, they have to create a short paper (10 pages maximum) about the project that is fully reproducible and dynamically generated using R markdown. |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | Gettng started with R and Rstudio | R for Data Science, Ch. 1 & 6 |
| 2) | Importing data | R for Data Science, Ch. 11 |
| 3) | Transforming data | R for Data Science, Ch. 5 |
| 4) | Visualizing data | R for Data Science, Ch. 3 |
| 5) | Visualizing data | R for Data Science, Ch. 3 |
| 6) | Exploring data | R for Data Science, Ch. 7 |
| 7) | Exploring data | R for Data Science, Ch. 7 |
| 8) | Midterm exam | |
| 9) | Cleaning data | R for Data Science, Ch. 12 & 13 |
| 10) | Cleaning data | R for Data Science, Ch. 12 & 13 |
| 11) | Getting data from the web | |
| 12) | Getting data from the web | |
| 13) | R markdown | R for Data Science, Ch. 27 |
| 14) | Project presentations |
Sources
| Course Notes / Textbooks: | |
| References: | Wickham, H. and G. Grolemund (2017). R for Data Science, https://r4ds.had.co.nz/ Neth, H. (2022). Data Science for Psychologists, https://bookdown.org/hneth/ds4psy/ |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 14 | % 10 |
| Midterms | 1 | % 30 |
| Final | 1 | % 60 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Study Hours Out of Class | 14 | 2 | 28 |
| Project | 1 | 26 | 26 |
| Midterms | 1 | 26 | 26 |
| Final | 1 | 30 | 30 |
| Total Workload | 152 | ||
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) | As a world citizen, she is aware of global economic, political, social and ecological developments and trends. | 1 |
| 2) | He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning. | 5 |
| 3) | Absorbs basic economic principles and analysis methods and uses them to evaluate daily events. | 5 |
| 4) | Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders. | 5 |
| 5) | Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions. | 3 |
| 6) | Comprehends new ways of doing business using digital technologies. and new market structures. | 2 |
| 7) | Takes critical approach to economic and social problems and develops analytical solutions. | 1 |
| 8) | Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods. | 2 |
| 9) | In the works he/she contributes, observes individual and social welfare together and with an ethical perspective. | 1 |
| 10) | Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines. | 3 |
| 11) | Generates original and innovative ideas in the works she/he contributes as part of a team. | 4 |