FACULTY OF BUSINESS

Department of Business Administration

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
Mode of Delivery -
Teaching Methods and Techniques of the Course -
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;
  • 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.

 



Course Category

Core Courses
X
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. Herzog, D. (2015). Data literacy: a user's guide. SAGE Publications., 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. Michael Yeomans, 2017, "A Manager’s Guide to Machine Learning and Automated Algorithms, in "HBR Guide to Data Analytics Basics for Managers", Harvard Business Review Press (ISBN: 978-1633694286)
5 MODULE 2: 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
6 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. Melton, J. (1996). Sql language summary. Acm Computing Surveys (CSUR), 28(1), 141-143.
7 SQL data retrieval and transfer. Basic join operations and table exporting from RDBMS. “Fundamentals of Analysis”, Ch. 5
8 SQL and ODBC usage in practice. Usage patterns. “Fundamentals of Analysis”, Ch. 6
9 Big data storage and processing problems. NoSQL databases. Cloud storage alternatives. Nayak, A., Poriya, A., & Poojary, D. (2013). Type of NOSQL databases and its comparison with relational databases. International Journal of Applied Information Systems, 5(4), 16-19.
10 MODULE 3: 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., Ch 2
11 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., Ch 3
12 Univariate and bivariate exploratory statistics and data plots with preferred tools. Herzog, D. (2015). Data literacy: a user's guide. SAGE Publications., Ch 4
13 Case exercise with univariate and bivariate statistics. Herzog, D. (2015). Data literacy: a user's guide. SAGE Publications., Ch 5
14 Combining office and spreadsheet tools for story building. Herzog, D. (2015). Data literacy: a user's guide. SAGE Publications., Ch 6
15 Semester Review
16 Final exam

 

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
60
Project
Seminar / Workshop
Oral Exams
Midterm
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
1.5
24
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
1
20
20
Presentation / Jury
1
40
40
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
0
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 solve problems with an analytical and holistic viewpoint in the field of business administration.

X
2

To be able to present the findings and solutions to the business problems in written and oral formats.

3

To be able to interpret the application of business and economic concepts, and philosophies at the national and international levels.

4

To be able to use innovative and creative approach for real-life business situations.

5

To be able to demonstrate leadership skills in different business situations.

6

To be able to interpret the reflections of new technologies and softwares to business dynamics.   

X
7

To be able to integrate knowledge gained in the five areas of business administration (marketing, production, management, accounting, and finance) through a strategic perspective.

8

To be able to act in accordance with the scientific and ethical values in studies related to business administration.

9

To be able to work efficiently and effectively as a team member.

X
10

To be able to have an ethical perspective and social responsiveness when making and evaluating business decisions.

11

To be able to collect data in the area of business administration and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1).

X
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 the human history to their field of expertise.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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