Course Name | Digital Era and Retailing |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
---|---|---|---|---|---|
LOG 330 | Fall/Spring | 3 | 0 | 3 | 5 |
Prerequisites | None | |||||
Course Language | English | |||||
Course Type | Elective | |||||
Course Level | First Cycle | |||||
Mode of Delivery | Blended | |||||
Teaching Methods and Techniques of the Course | Lecture / Presentation | |||||
Course Coordinator | - | |||||
Course Lecturer(s) | ||||||
Assistant(s) |
Course Objectives | The course aims to analyze the big data associated with changing structure of the retail industry. The course aims to enable students to observe, construct and manage big data in different areas of online retailing. |
Learning Outcomes | The students who succeeded in this course;
|
Course Description | The course includes the digitization process, review of business models, dynamics of the retail industry, changing consumer behavior due to technology, big data related to online retailing and real-life big data analysis applications. In order to apply big data analysis in online retailing, course content includes basic building blocks of Python programming and specialized programming. Topics covered are discussed through case studies, classroom practices and discussions. |
Related Sustainable Development Goals | |
| Core Courses | |
Major Area Courses | X | |
Supportive Courses | ||
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
1 | Introduction to course | Lecture Notes |
2 | Changing business models: Distribution channel strategies Omni-channel strategy E-commerce and channel structures | Lecture Notes |
3 | Retail industry and its interaction with technology: Web and Mobile applications Generated data Telling stories with data Examples of handling customer reviews and services | Lecture Notes |
4 | Introduction to big data I: Sources and cost of data, Data types (associational, relational, geographical), Quality of data | Lecture Notes |
5 | Introduction to big data II: Storage and flow of data, ICT systems, Data transfer protocols and standards, SQL, SQL data retrieval and transfer | Lecture Notes |
6 | How to read and analyze big data: Introduction to Python I: Syntax, variables, algorithm building, Loops, functions | Lecture Notes |
7 | How to read and analyze big data: Introduction to Python II: Syntax, variables, algorithm building, Loops, functions | Lecture Notes |
8 | Midterm | |
9 | How to read and analyze big data: Data structures Python: Python data structure; files, lists, dictionaries, tuples | Lecture Notes |
10 | How to read and analyze big data: Processing Data: File access, input/output processing, String processing; parse, split, search strings, Regular expressions | Lecture Notes |
11 | How to read and analyze big data: Access web data, Network, socket, Webservices and API, Web crawling | Lecture Notes |
12 | How to read and analyze big data: Database management, XML, Json, REST architecture, Managing and mining database | Lecture Notes |
13 | Advanced Analysis with Big Data: sentiment analysis using python I:What is text mining? Sentiment analysis and methods, Sentiment analysis; data crawling, parsing, editing, | Lecture Notes |
14 | Advanced Analysis with Big Data: sentiment analysis using python II:Sentiment analysis cnt.; Categorizing sentiment, sentiment polarity, Applications in retailing | Lecture Notes |
15 | Review of the semester | |
16 | Final exam |
Course Notes/Textbooks |
|
Suggested Readings/Materials |
Semester Activities | Number | Weigthing |
Participation | 1 | 10 |
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | 20 | |
Presentation / Jury | ||
Project | ||
Seminar / Workshop | ||
Oral Exam | ||
Midterm | 1 | 30 |
Final Exam | 1 | 40 |
Total |
Weighting of Semester Activities on the Final Grade | 3 | 60 |
Weighting of End-of-Semester Activities on the Final Grade | 1 | 40 |
Total |
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
Study Hours Out of Class | 15 | 3 | 45 |
Field Work | |||
Quizzes / Studio Critiques | |||
Portfolio | |||
Homework / Assignments | 2 | 15 | |
Presentation / Jury | |||
Project | |||
Seminar / Workshop | |||
Oral Exam | |||
Midterms | 1 | 25 | |
Final Exams | 1 | 30 | |
Total | 178 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To be able to analyze complex problems in the field of logistics and supply chains | X | ||||
2 | To be able to have good knowledge of sector related market leaders, professional organizations, and contemporary developments in the logistics sector and supply chains | X | ||||
3 | To be able to participate in the sector-related communication networks and improve professional competencies within the business sector | |||||
4 | To be able to use necessary software, information and communication technologies in the fields of logistics management and supply chain | X | ||||
5 | To be able to understand and utilize the coordination mechanisms and supply chain integration | X | ||||
6 | To be able to analyze the logistics and supply chain processes using the management science perspective and analytical approaches | X | ||||
7 | To be able to design, plan and model in order to contribute to decision making within the scope of logistics and supply chains | X | ||||
8 | To be able to interpret and evaluate the classical and contemporary theories in the field of logistics and supply chains | X | ||||
9 | To be able to conduct projects and participate in teamwork in the field of logistics and supply chains | |||||
10 | To be able to have an ethical perspective and social responsiveness when making and evaluating decisions. | |||||
11 | To be able to collect data in the area of logistics and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1). | |||||
12 | To be able to speak a second foreign at a medium level of fluency efficiently. | |||||
13 | To be able to relate the knowledge accumulated throughout human history to their field of expertise. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest