(1) GENERAL INFORMATION
SCHOOL OF ECONOMIC AND MANAGEMENT STUDIES
DEPARTMENT OF BUSINESS ADMINISTRATIONS
LEVEL OF STUDY (BSc/MSc)
INDEPENDENT TEACHING ACTIVITIES
WEEKLY TEACHING HOURS
Add rows if needed. The teaching organization and teaching methods used are described in detail in (d).
general background, special background, general knowledge specialization, skills development
LANGUAGE OF TEACHING and EXAMS:
IS THE COURSE OFFERED TO ERASMUS STUDENTS
ELECTRONIC COURSE PAGE (URL)
(2) LEARNING OUTCOMES
The learning outcomes of this course, knowledge and skills that will be gained, and abilities of an appropriate level that students will acquire after the successful completion of the course.
Refer to Appendix A.
Upon successful completion of the course, students should:
• Know the terminology of data analysis as it is applied in the field of business.
• Distinguish the main categories of data analysis models and their special features.
• Use statistical tools to successfully handle data analysis models.
• Know the basic use of at least two software for data analysis.
Taking into account the general skills that the graduate must have acquired (as they are listed in the Diploma Supplement and are listed below) which of the following is the aim of the course ?
Search, analysis and synthesis of data and information, using the necessary technologies
Adaptation to new situations
Working in an international environment
Work in an interdisciplinary environment
Production of new research ideas
Project design and management
Respect for diversity and multiculturalism
Respect for the natural environment
Demonstration of social, professional and moral responsibility and sensitivity in gender issues
Exercise criticism and self-criticism
Promoting free, creative and inductive thinking
Promoting free, creative and inductive thinking.
Adaptation to new situations.
Search, analysis and synthesis of data and information, using the necessary technologies.
Project design and management.
(3) COURSE CONTENT
The course attempts to lay the foundations for Data Analysis in business. It introduces students to the basic concepts of data analysis, business intelligence, and machine learning, while encouraging a critical understanding of the hypotheses underpinning these methodologies and the ethical and legal implications of data analysis.
Introduction to Data Analysis, Data Analysis Models and BI / DA Tools, Ethical and Legal Dimensions of Data Analysis, Problematic Data, Introduction to Business Intelligence, Statistical Tools, Data Visualization, Introduction to Machine Learning, Regression Use, Discrimination and Trade-Discrimination / Overfitting, data analysis in Excel (Descriptive Statistics Toolbox, Vlookups / Match & Index, Pivot Tables, Regression Analysis, Using Excel Macros, VBA Scripting
(4) TEACHING AND LEARNING METHODS - EVALUATION
FACE TO FACE, LAB
USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES
USE OF ICT IN TEACHING, LABORATORY EDUCATION, ELECTRONIC COMMUNICATION WITH STUDENTS
The teaching methodologies are described in detail.
Lectures, Seminars, Laboratory Exercise, Field Exercise, Bibliography study & analysis, Tutoring, Internship (Placement), Clinical Exercise, Art Workshop, Interactive teaching, Study visits, Study work, artwork, creatio, etc
Indicate the student's study hours for each learning activity as well as the non-guided study hours according to the ECTS principles
Description of the evaluation process
Assessment Language, Assessment Methods, Formative or Concluding, Multiple Choice Test, Short Answer Questions, Essay Development Questions, Problem Solving, Written Assignment, Report / Report, Oral Examination, Public Presentation, Public Presentation, Others
Explicitly defined assessment criteria are stated and if and where they are accessible to students.
Written Examination (GA) with a weight of 70% at the end of the semester, in the Greek language, which includes multiple choice questions. The student can also implement an optional assignment (EP) with a weight of 30%. In order for the student's attendance to be considered successful, the Final Grade (TB) must be> = 5. TB is calculated as follows:
ΤΒ = ΜΑΧ (GA, 0.7xGA + 0.3xEP)
Data Science for Business, Foster Provost and Tom Faucett, greek translation, Kleidarithmos, 2019.
The Second Machine Age- Work, Progress, and Prosperity in a Time of Brilliant Technologies, BRYNJOLFSSONErik και McAFEEAndrew , greek translation, Kritiki, 2016.
Analytics Stories- Using Data to Make Good Things Happen, Wayne L. Winston, Wiley, 2021
The Data Detective- Ten Easy Rules to Make Sense of Statistics, Tim Harford, Riverhead Books, 2021