BUSINESS ANALYTICS

SCHOOL

SCHOOL OF ECONOMIC AND MANAGEMENT STUDIES

DEPARTMENT

DEPARTMENT OF BUSINESS ADMINISTRATIONS

LEVEL OF STUDY (BSc/MSc)

BSc

COURSE CODE

509

SEMESTER

5^th

COURSE TITLE

BUSINESS ANALYTICS

INDEPENDENT TEACHING ACTIVITIES
in case the credits are awarded in separate parts of the course e.g. Lectures, Laboratory Exercises, etc. If the credits are awarded uniformly for the whole course, indicate the weekly teaching hours and the total number of credits.

WEEKLY TEACHING HOURS

CREDIT UNITS

4

5

Add rows if needed. The teaching organization and teaching methods used are described in detail in (d).

COURSE TYPE

general background, special background, general knowledge specialization, skills development

ELECTIVE

PREREQUISITE COURSES:

NO

LANGUAGE OF TEACHING and EXAMS:

GREEK

IS THE COURSE OFFERED TO ERASMUS STUDENTS

NO

ELECTRONIC COURSE PAGE (URL)

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.

• Description of the Level of Learning Outcomes for each course of study according to the Qualifications Framework of the European Higher Education Area
• Descriptive Indicators of Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Annex B
• Summary Guide for writing Learning Outcomes

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.

General Skills

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

Decision making

Autonomous work

Teamwork

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

……

Other…

…….

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.

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

TEACHING METHOD
Face to face, distance learning, etc..

FACE TO FACE, LAB

USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES
Use of ICT in Teaching, in Laboratory Education, in Communication with students

USE OF ICT IN TEACHING, LABORATORY EDUCATION, ELECTRONIC COMMUNICATION WITH STUDENTS

TEACHING ORGANIZATION

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

STUDENT EVALUATION

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