Introduction to Information Systems and Applications

Εισαγωγή στους Η/Υ και Εφαρμογές

Εισαγωγή στους Η/Υ και Εφαρμογές

Course ID: 
ECO_150
Semester: 
1st
Year of Study: 
1st Year
Category: 
Compulsory
For Erasmus Students: 
Yes
eclass URL

Learning Outcomes

The aim of the course is to develop basic competences in using computers and informations systems as the main tools for data processing. After successfully completing the course, students will be able to:

  • Describe the role and importance of computers in the field of Economics
  • Identify the basic elements of the computer’s architecture and their role in computations
  • Define the methods for data representation, especially for numerical data using different numerical systems
  • Recognize the role and importance of algorithms and use algorithmic thinking when solving statistical problems using computers.
  • Employ methods for statistical data processing suitable for computers
  • Utilize spreadsheets and the Python programming language for statistical data processing taks of open data
  • Comparing and assessing the different tools for statistical data processing and draw conclusions on their strengths and weaknesses.

Course Contents

  • Role and importance of computers in the field of Economics.
  • Evolution of computing machines and their architecture. Architecture of contemporary computing systems.
  • Methods of data representation in the context of computers. Number systems and their role in computing.
  • Methods and algorithms to address statistical problems using computers.
  • Techniques to address statistical problems using:
    1. Spreadsheets (Excel / OpenOffice Calc) and
    2. the programming language Python.
  • Using open data to apply and study statistical processing methods towards understanding the data using both tools.
  • Comparison and evaluation of statistical tools in the context of data processing problems.  

Teaching Activities

Lactures (3 hours per week) and Laboratory exercises (2 hours per week)

Teaching Organization

Lectures in the laboratory for the presentation of the basic concepts (3 hours per week). Laboratory exersises (2 hours per week) where students work by their own or in small groups. On-line weekly exercises where students work on small data sets in spreadsheets or programming with python. Students also work in groups (3-4 students) for two large projects that learn to process open large datasets: one project using spreadsheet applications and the second one using the Python programming language.

Activity

Semester workload

Lectures

39 hours

Lab exercises

26 hours

Team Projects

52 hours

Individual quizzes and Self-study

33 hours

Total number of hours for the Course (25 hours of work-load per ECTS credit)

150 hours (total student work-load)

Assessment

The final grade of this module is derived by the following components:

  1. Two (2) Team Projects on using software to perform statistical data processing and analysis: 30% [Project in spreadsheet (15%), Project in Python (15%)]
  2. Final exam (Short and problem-solving questions: 70%)

The evaluation criteria are available to students at eclass here.

Use of ICT

  • Slides and notes to support lectures
  • Spreadsheet software and the Python programming language for demonstration and practice 
  • Use of the E-Learning platform eclass in order to:
    • Organize the course material (slides, notes, examples, code snippets etc)
    • Perform weekly online quizzes to evaluate the understanding of the related course material
    • Hand in homeworks
    • Communicate with the students and the class
  • Open courses and open educational material

Course Info

Teaching Hours: 
5 hours per week
ECTS Credits: 
6.00
Teaching Credits: 
3.00
Weight: 
1.50
Type: 
Special background,
Specialised general knowledge,
Skills development
Language: 
Greek,
Greek
Teaching Method: 
Face-to-Face
General Competences: 
Search for, analysis and synthesis of data and information, with the use of the necessary technology,
Team work
Required for: 
Issues on Digital Economy
Database Systems
Data Analysis
Computer assisted Statistics

Current Tutors

Instructor: 

Tzagarakis Manolis

Associate Professor
Tzagarakis Manolis
Field of Expertise: 
Information and Knowledge Management
Organic Unit / Lab: 
Laboratory of Quantitative Economics and Information Systems
E-mail: 
Telephone: 
+30 2610 962588
Fax: 
+30 2610 997622
Office Hours: 
Tuesday 12:00 -13:00
Thursday 11:00 -13:00
or after appointment
Personal Page

Daskalou Victoria

Teaching Staff
Victoria Daskalou
Field of Expertise: 
Internet Information Systems
Organic Unit / Lab: 
Laboratory of Quantitative Economics and Information Systems
E-mail: 
Telephone: 
+30 2610 997788
Office Hours: 
Thursday 11:30-13:30
Personal Page

Reading List

eclass URL: 
e-class
Reading Recommendations: 
- Β, Βερύκιος, Σ. Κωτσιαντής, Η. Σταυρόπουλος, Μ. Τζαγκαράκης, Η Επιστήμη των Δεδομένων, 1η/2018, Εκδόσεις Νέων Τεχνολογιών ΙΚΕ, https://service.eudoxus.gr/search/#a/id:77120638/0
- Αβούρης, Ν., Κουκιάς, Μ., Παλιουράς, Β., Σγάρμπας, Κ.: Python – Εισαγωγή στους υπολογιστές, ΙΔΡΥΜΑ ΤΕΧΝΟΛΟΓΙΑΣ & ΕΡΕΥΝΑΣ-ΠΑΝΕΠΙΣΤΗΜΙΑΚΕΣ ΕΚΔΟΣΕΙΣ ΚΡΗΤΗΣ, 4η Έκδοση, 2018, ISBN: 978-960-524-529-0, https://service.eudoxus.gr/search/#a/id:77117677/0
- Βογιατζής Ι., Αντωνοπούλου Ήρ., Υλικό, Λογισμικό και Επικοινωνίες Υπολογιστών - 4η Έκδοση, Εκδόσεις Νέων Τεχνολογιών ΙΚΕ. https://service.eudoxus.gr/search/#a/id:102075306/0
- A Byte of Python, Creative Commons Attribution-ShareAlike 4.0 International License, Διαθέσιμο από http://dide.flo.sch.gr/Plinet/Meetings/Meeting23/A_Byte_of_Python-el.pdf

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