FACULTY OF BUSINESS
Department of Political Science and International Relations
SOCIAL MEDIA
BUS 210 | Course Introduction and Application Information
| Course Name |
Data Literacy for Business and Social Sciences
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
BUS 210
|
Fall
|
2
|
2
|
3
|
5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Required
|
|||||
| Course Level |
First Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | DiscussionCase StudyApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | ||||||
| Course Objectives | This course aims to prepare students in the fields of business and social sciences for the data skills needed to perform their professional and research tasks in today’s data driven environments. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | Data can be about anything. This course is about the data itself. Through this applied course students develop a critical perspective to identify data sources relevant to a problem in hand, learn how to: describe technologies and data management processes in contemporary corporate systems; combine and convert data across various sources, formats and standard; assess and improve data quality; articulate insights into a business or social science problem by visualizing and interpreting features of data and basic data analysis. The course consists of three modules: 1. Data and Life (4 weeks): Identifying sources of data in business and social sciences and what it represents. Translating theories and hypothesis to data. Sources and costs related to data. Data liabilities, ethics, security and theft, privacy concerns. Associational, relational, and geographic data; 2. Telling stories with data (5 weeks): Communicating analytics, using simple (Excel, Kaggle) plots in reports, infographics; 3. Managing data in the real world (5 weeks):SQL, RDBMS, data cleaning issues, unstructured data, the need for NoSQL databases in cloud and big data. Corporate ICT systems: storage and flow of data and information on-site and in cloud. |
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | MODULE 1: Data and Life The basics of scientific inquiry in social sciences. Populations, samples, and data. Theory and hypotheses formation in data terms. Data tables as basic data form. | “Data Literacy”, Ch 1 “Fundamentals of Analysis”, Ch. 1 |
| 2 | Identifying sources of data. Sources, open sources, and costs of obtaining data. Data liabilities, privacy, gender and ethics issues. | “Fundamentals of Analysis”, Ch. 2 |
| 3 | Structure of associational (i.e. co-occurrence), relational (e.g. social networks), and geographic (e.g. location based) data | “Fundamentals of Analysis”, Ch. 3 |
| 4 | Adding value with data. Statistical learning approaches. | |
| 5 | MODULE 2: Telling stories with data Communication beyond oral and written visual communication and role of graphics and infographics. Visualizations: the good, the bad, and the too much, focusing on the story. | “Data Literacy”, Ch 2 |
| 6 | Narrative patterns about co-occurrence and causality. Types of data visualizations for narrative patterns. Preferred tools for producing data plots. | “Data Literacy”, Ch 3 |
| 7 | Univariate and bivariate exploratory statistics and data plots with preferred tools. | “Data Literacy”, Ch 4 |
| 8 | Case exercise with univariate and bivariate statistics | “Data Literacy”, Ch 5 |
| 9 | Combining office and spreadsheet tools for story building. | “Data Literacy”, Ch 6 |
| 10 | MODULE 3: Managing data in the real world Structure and quality of data in relation to its sources. Aging of data and its structure. Beyond tables: Relational Data Base Management Systems. Understanding basic design patterns. | "Fundamentals of Analysis”, Ch. 4 |
| 11 | Organizational and inter-organizational ICT systems. Storage and flow of information between people, organizations, and locations. ICT standards. The need for a Standard Query Language(SQL) and ODBC standards | |
| 12 | SQL data retrieval and transfer. Basic join operations and table exporting from RDBMS. | “Fundamentals of Analysis”, Ch. 5 |
| 13 | SQL and ODBC usage in practice. Usage patterns. | “Fundamentals of Analysis”, Ch. 6 |
| 14 | Big data storage and processing problems. NoSQL databases. Cloud storage alternatives. | |
| 15 | Semester Review | |
| 16 | Semester Review |
| Course Notes/Textbooks | Herzog, D. (2015). Data literacy: a user's guide. SAGE Publications. DOI: https://dx.doi.org/10.4135/9781483399966 ISBN: 978-1483333465 Fundamentals of Analysis, a web book by Matt David and Dave Fowler: https://dataschool.com/fundamentals-of-analysis/ |
| Suggested Readings/Materials |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation |
1
|
10
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
30
|
| Presentation / Jury |
1
|
40
|
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
20
|
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade |
4
|
100
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
2
|
32
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
2
|
32
|
| Study Hours Out of Class |
16
|
3
|
48
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
3
|
11
|
33
|
| Presentation / Jury |
1
|
2
|
2
|
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
1
|
1
|
| Final Exam |
0
|
||
| Total |
148
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to use the theoretical and practical knowledge acquired in the areas of Political Science and International Relations. |
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| 2 | To be able to have the basic knowledge of, and make use of other disciplines which contribute to the areas of Political Science and International Relations. |
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| 3 | To be able to distinguish the differences between classical and contemporary theories and to assess their relationship. |
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| 4 | To be able to recognize regional and global issues, and develop solutions based on research. |
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| 5 | To be able to assess the acquired knowledge and skills in the areas of Political Science and International Relations critically. |
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| 6 | To be able to transfer ideas and proposals on issues in the areas of Political Science and International Relations to other people and institutions verbally and in writing. |
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| 7 | To be able to identify the historical continuity and changes observed in the relations between the actors and institutions of national and international politics. |
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| 8 | To be able to examine concepts, theories, and developments with scientific methods in the areas of Political Science and International Relations. |
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| 9 | To be able to take responsibility as an individual and as a team member. |
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| 10 | To be able to act in accordance with the scientific and ethical values in studies related to Political Science and International Relations. |
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| 11 | To be able to collect data in the areas of Political Science and International Relations and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1). |
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| 12 | To be able to speak a second foreign at a medium level of fluency efficiently. |
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| 13 | To be able to relate the knowledge accumulated throughout human history to their field of experience. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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