| 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 | |||||
| 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. |
| Related Sustainable Development Goals |   |
|
| Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 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 |
| 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 Exam | ||
| 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 |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 2 | 32 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | 2 | |
| Study Hours Out of Class | 16 | 3 | 48 |
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | 3 | 11 | |
| Presentation / Jury | 1 | 2 | |
| Project | |||
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 1 | |
| Final Exams | |||
| Total | 148 |
| # | 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 | |||||
| 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 | |||||
| 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 | |||||
| 5 | To be able to understand and utilize the coordination mechanisms and supply chain integration | |||||
| 6 | To be able to analyze the logistics and supply chain processes using the management science perspective and analytical approaches | |||||
| 7 | To be able to design, plan and model in order to contribute to decision making within the scope of logistics and supply chains | |||||
| 8 | To be able to interpret and evaluate the classical and contemporary theories in the field of logistics and supply chains | |||||
| 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
