Introduction to Information Systems and Applications (ECON1242)

Emmanouil Tzagarakis, Victoria Daskalou


The aim of the course is to develop basic competences in using computers and informations systems as the main tools for data processing.

The course introduces the importance of computers in the field of Economics and analyzes methods and algorithms to address statistical problems using computers.

Data processing methods are presented by using:

  1. the spreadsheet software (MS excel or OpenOffice Calc),
  2. the Python programming language, and
  3. the R language and environment for statistical computing and graphics.

Open data are used to apply and study statistical processing methods and finally a comparison is made in order to evaluate statistical tools.

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Course Syllabus

The module covers the following topics:

  • Computers as mind expanding tools
  • Computer and data processing history
  • Modern computer systems and their architecture
  • Arithmetic systems and number representation. Conversion between different arithmetic systems.
  • Data processing using spreadsheet software (MS excel)
  • Data processing using programming languages (python).
Course Objectives/Goals

Upon completion of this course, students will be able to: 

  • Understand the importance of computer systems in Economics 
  • Describe the concepts of hardware, software and applications
  • Accomplish conversions and operations in different arithmetic systems 
  • Process data using spreadsheet software (MS Excel) 
  • Process data using programming languages (Python)
Instructional Methods

Lectures in the laboratory for the presentation of the basic concepts (2 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 an other one using the python programming language.

Assessment Methods

The final grade of this module is derived by the following components: Project in spreadsheet (15%), Project in Python (15%), Written Examination Grade (70%)

Prerequisites/Prior Knowledge

No need for prior knowledge


Manolis Tzagarakis, Victoria Daskalou


Data processing techniques using MS Excel spreadsheet software.



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