COURSE DESCRIPTION AND APPLICATION INFORMATION
| Course Name | Code | Semester | T+A+L (hour/week) | Type (C / O) | Local Credit | ECTS |
|---|---|---|---|---|---|---|
| Big Data and Insights | NMD 214 | Spring | 02+02+00 | Elective | 3 | 5 |
| Academic Unit: | Faculty of Communication |
| Mode of Delivery: | Face to face |
| Prerequisites: | None |
| Language of Instruction: | English |
| Level of Course Unit: | Undergraduate |
| Course Coordinator: | - - |
| Course Lecturer(s): | Pınar Dağ |
| Course Objectives: | The students are expected to:
• Have basic insights about Big Data and comprehend their influences on business dynamics and business culture in the ever developing world. |
| Course Contents: | This course aims to introduce the students to the Big Data Concept. The students who would like to work either as a white-collar employee or as an entrepreneur, will have an overall understanding of Big Data, its definition and evolution, the terminology of Big Data and methodologies, strategy development with Big Data and its dark sides. |
| Learning Outcomes of the Course Unit (LO): |
|
| Planned Learning Activities and Teaching Methods: | The course consists of 3 modules. These are: 1. Introduction to Big Data 2. Data Analytical Thinking 3. Data Mining The assessment is divided into three modules. The first module’s assessment is in the form of an individual reflection paper (25%). The second module’s assessment is comprised of a group (in pairs) in-class presentation (25%). In the third module, the students are required to develop a Big Data driven New Media campaign (40%). Active participation in class discussion are also required (10%). |
WEEKLY SUBJECTS AND RELATED PREPARATIONS
| Week | Subjects | Related Preperation |
|---|---|---|
| 1 | Orientation Week (Introduction and course plan) | |
| 2 | Module (I): Introduction to Big Data: What is big data? | Individual research, class discussion, receiving the brief of the reflection paper |
| 3 | Module (I): Introduction to Big Data: Evolution from data to big data | Individual research, class discussion, outlining ideas for the reflection paper |
| 4 | Module (I): Introduction to Big Data: Understand big data business implications | Bringing examples to class, class discussion, developing the reflection paper |
| 5 | Module (I): Introduction to Big Data: Understand big data use cases | Class discussion, consulting with mentors on the reflection papers |
| 6 | Module (II): Data Analytical Thinking: The culture of big data | Individual research |
| 7 | Module (II): Data Analytical Thinking: Developing a big data strategy | Forum discussion, pairs working on group presentation |
| 8 | Module (II): Data Analytical Thinking: Big data for digital entrepreneurs | Class discussion, discussing and receiving feedback about the presentation from the mentor(s) |
| 9 | Module (II): Data Analytical Thinking: Predictive analysis | Workshop with industry partners |
| 10 | Presentations | Pairs presenting their projects in class |
| 11 | Module (III): Data for New Media Campaigns: Social network analysis | Consulting mentor(s) for project ideas |
| 12 | Module (III): Data for New Media Campaigns: Business value from big data | Receiving feedback on project drafts from mentor(s) |
| 13 | Module (III): Data for New Media Campaigns: Integrating big data to your own campaign | Finalizing project |
| 14 | Review Week |
At Kadir Has University, a Semester is 14 weeks; The weeks 15 and 16 are reserved for final exams.
REQUIRED AND RECOMMENDED READING
| Davenport T., Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, Harvard Business School Publishing, 2014. O’Reilly Inc., Big Data Now; Current Perspectives from O’Reilly Media, 2012. Provost, F. Data Science for Business: What you need to know about data mining and data-analytic thinking, O’Reilly Media Publishing 2013. Dumbill E., Planning for Big Data, 2012. Barlow, M. The Culture of Big Data, O’Reilly, 2013 |
OTHER COURSE RESOURCES
ASSESSMENT METHODS AND CRITERIA
| Semester Requirements | Number | Percentage of Grade (%) |
|---|---|---|
| Attendance / Participation | 12 | 10 |
| Project | 1 | 40 |
| Homework Assignments | 1 | 25 |
| Presentation / Jury | 1 | 25 |
| Total: | 15 | 100 |
WORKLOAD
| Events | Count | Duration (Hours) | Total Workload (hour) |
|---|---|---|---|
| Course Hours | 14 | 3 | 42 |
| Project | 1 | 42 | 42 |
| Homework Assigments | 1 | 25 | 25 |
| Preparation for Presentation / Jury | 1 | 16 | 16 |
| Total Workload (hour): | 125 | ||
1 ECTS = 25 Hours Workload
THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)
| # | PQ1 | PQ2 | PQ3 | PQ4 | PQ5 | PQ6 | PQ7 | PQ8 | PQ9 |
| LO1 | |||||||||
| LO2 | |||||||||
| LO3 | |||||||||
| LO4 | |||||||||
| LO5 | |||||||||
| LO6 |
Contribution: 1 Low, 2 Average, 3 High