BUS 210 | Course Introduction and Application Information - Department of Economics

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)	Dr. Öğr. Üyesi Sinem Tokçaer Öğr.Gör.Dr. Emre CAN 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	The students who succeeded in this course; Assess the quality of a data source. Describe technologies that enable data storage and retrieval. Correct problems with data sets to facilitate analysis. Locate sources of data relevant to their field of study. Combine data sets from different sources. 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.
Related Sustainable Development Goals

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

Week	Subjects	Related Preparation
1	MODULE 1: Data Essentials Introduction. Essential data concepts: data, information. The basics of inquiry in social sciences: statistical inference, theory and hypotheses formation in data terms.. Populations, samples, and data. Data quality. Data file formats and processing software. Data tables and variable data types. Spreadsheet functions and referencing.	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: “Fundamentals of Analysis”, Section. 1, p1-12.
2	Data sources, liabilities, and quality control. Identifying sources and costs of obtaining data. Data liabilities.Data privacy, ethical issues, and regulatory laws. Descriptive statistics and summary visualizations and their use for data quality control	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: Section III and IV, p65-142.
3	Exploring change with data. Data aggregation and exploration of change among groups with pivot tables. Exploring change over time with time series visualization.	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: Section V , p143-186.
4	Exploring causal relations in data. Exploring covariation between different types of variables using corresponding plot types. Correlation and simple linear regression. Quality measures, quality improvement, and dealing with curvilinearity in linear regression models.	Cetinkaya-Rundel, M., Diez, D., & Barr, C. (2019). OpenIntro Statistics. (Fourth Edition ed.) OpenIntro: chapter 8, p303-340
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.	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: Chapter 2, p15-28.
6	Narrative patterns about co-occurrence and causality. Types of data visualizations for narrative patterns. Preferred tools for producing data plots.	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: Chapter 3, p29-50
7	Univariate and bivariate exploratory statistics and data plots with preferred tools.	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: Chapter 4, p51-64
8	Case exercise with univariate and bivariate statistics	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: Chapter 5, p67-74.
9	MIDTERM WEEK	MIDTERM WEEK
10	Combining office and spreadsheet tools for story building.	Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications: Chapter 6, p75-94
11	MODULE 3: Managing data in the real world Structure and quality of data Data Base Management Systems and uses of DBMS	Harrington, J. L. (2010). SQL clearly explained (3rd ed.). Morgan Kaufmann: Chapter 3, p65-74
12	Relational Data Base Management Systems Basic concepts and relations in databases	Harrington, J. L. (2010). SQL clearly explained (3rd ed.). Morgan Kaufmann: Chapter 3-4, p65-105
13	SQL basics Data retrieval and transfer using SQL DAta editing using Query in google sheets	Harrington, J. L. (2010). SQL clearly explained (3rd ed.). Morgan Kaufmann: Chapter 4, p77-105
14	Big data storage and processing problems. NoSQL databases. Cloud storage alternatives.	Harrington, J. L. (2010). SQL clearly explained (3rd ed.). Morgan Kaufmann: Chapter 7, p161-196
15	Basic join operations and table exporting from RDBMS	Harrington, J. L. (2010). SQL clearly explained (3rd ed.). Morgan Kaufmann: Chapter 5, p107-130
16	FINAL EXAM

Course Notes/Textbooks

Herzog, D. (2015). (All resources are either publicly available or available as an electronic resource at the IEU library)

Data literacy: a user's guide. Herzog, D. (2015). Data literacy: A user's guide. SAGE Publications. Freely available at DOI: https://dx.doi.org/10.4135/9781483399966

Cetinkaya-Rundel, M., Diez, D., & Barr, C. (2019). OpenIntro Statistics. (Fourth Edition ed.) OpenIntro. https://leanpub.com/os

Fundamentals of Analysis, a web book by Matt David and Dave Fowler: https://dataschool.com/fundamentals-of-analysis/

Harrington, J. L. (2010). SQL clearly explained (3rd ed.). Morgan Kaufmann, ISBN: 9780123756978

Suggested Readings/Materials

https://ourworldindata.org/coronavirus

https://www.data-to-viz.com/

https://flourish.studio/examples/

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	2	40
Portfolio
Homework / Assignments
Presentation / Jury	1	30
Project
Seminar / Workshop
Oral Exams
Midterm
Final Exam	1	30
Total

Weighting of Semester Activities on the Final Grade	6	70
Weighting of End-of-Semester Activities on the Final Grade	1	30
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	2	3	6
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	2	2
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam	1	2	2
		Total	146

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to acquire a sound knowledge of fundamental concepts, theories, principles and methods of investigation specific to the economic field.	-	X	-	-	-
2	To be able to apply adequate mathematical, econometric, statistical and data analysis models to process economic data and to implement scientific research for development of economic policies.	-	-	-	X	-
3	To be able to participate in academic, professional, regional, and global networks and to utilize these networks efficiently.	-	-	-	-	-
4	To be able to have adequate social responsibility with regards to the needs of the society and to organize the activities to influence social dynamics in line with social goals.	-	-	-	-	-
5	To be able to integrate the knowledge and training acquired during the university education with personal education and produce a synthesis of knowledge one requires.	-	-	X	-	-
6	To be able to evaluate his/her advance level educational needs and do necessary planning to fulfill those needs through the acquired capability to think analytically and critically.	-	-	-	-	-
7	To be able to acquire necessary skills to integrate social dynamics into economic process both as an input and an output.	-	-	-	-	-
8	To be able to link accumulated knowledge acquired during the university education with historical and cultural qualities of the society and be able to convey it to different strata of society.	-	-	-	-	-
9	To be able to take the responsibility as an individual and as a team member.	-	-	-	-	-
10	To be able to attain social, scientific and ethical values at the data collection, interpretation and dissemination stages of economic analysis.	-	X	-	-	-
11	To be able to collect data in economics and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1)	-	-	-	-	-
12	To be able to speak a second foreign language at a medium level of fluency efficiently.	-	-	-	-	-
13	To be able to relate the knowledge accumulated throughout human history to their field of economics.	-	-	-	-	-