(1) GENERAL INFORMATION
SCHOOL OF ECONOMIC AND MANAGEMENT STUDIES
DEPARTMENT OF BUSINESS ADMINISTRATIONS
LEVEL OF STUDY (BSc/MSc)
APPLICATIONS OF BUSINESS ANALYTICS
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 use of a data analysis language to solve key business problems.
• Successfully handle basic R language data structures
• Use statistical tools to successfully handle data analysis models to solve business problems.
• Design and implement simple data analysis models using the R language.
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
Programming knowledge and experience not only improves students' employability, but also teaches them analytical skills, such as breaking down a problem into smaller parts and identifying and reusing previously solved problems. The purpose of this course is to equip students with the knowledge and skills to write structured programs for solving Business Analytics problems. Although these basic principles can be achieved using any high-level programming language, the module introduces R as the introductory language. Although the module does not require previous programming experience, its analytical orientation is best suited for students who are particularly interested in problem solving and have strong analytical skills.
Introduction to the R language, basic concepts of the language such as variables, repetition structures, use of R for descriptive statistics, common solutions (with R code) of machine learning problems, complete solution of a Business Analytical problem using R code.
(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.
Big Data Analytics with R, Simon Walkowiak, Packt Publishing, 2021
R for Data Science, Hadley Wickham & Garrett Grolemund, O’ Reilly, 2017
Machine Learning with R, Third Edition, Brett Lantz, Packt Publishing, 2021
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