BUS 210 | Course Introduction and Application Information - Department of Political Science and International Relations

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	Discussion Case Study Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Prof. Dr. Mehmet Gençer
Course Lecturer(s)	Doç. Dr. Gül ERTAN ÖZGÜZER Dr. Öğr. Üyesi Sinem Tokçaer Prof. Dr. Mehmet Gençer
Assistant(s)	Araş. Gör. Hasan GÜLOĞLU

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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	Assess the quality of a data source.
2	Describe technologies that enable data storage and retrieval.
3	Correct problems with data sets to facilitate analysis.
4	Locate sources of data relevant to their field of study.
5	Combine data sets from different sources.
6	Convey meaningful insights from a data analysis through visualizations and inferences.

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.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
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	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	6	30
Portfolio
Homework / Assignments
Presentation / Jury	1	30
Project
Seminar / Workshop
Oral Exams
Midterm	2	40
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
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	4.5	72
Field Work			0
Quizzes / Studio Critiques	6	1	6
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	2	2
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	2	2	4
Final Exam			0
		Total	148

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	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.		-	-	-	-	-
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.		-	-	-	-	-
3	To be able to distinguish the differences between classical and contemporary theories and to assess their relationship.		-	-	-	-	-
4	To be able to recognize regional and global issues, and develop solutions based on research.		-	-	-	-	-
5	To be able to assess the acquired knowledge and skills in the areas of Political Science and International Relations critically.		-	-	-	-	-
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.		-	-	-	-	-
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.		-	-	-	-	-
8	To be able to examine concepts, theories, and developments with scientific methods in the areas of Political Science and International Relations.		-	-	-	-	-
9	To be able to take responsibility as an individual and as a team member.		-	-	-	-	-
10	To be able to act in accordance with the scientific and ethical values in studies related to Political Science and International Relations.		-	-	-	-	-
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).		-	-	-	-	-
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 experience.		-	-	-	-	-