The aim of the course is to create basic competences for the use of computing systems and their applications 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 ways of statistical processing of open data using spreadsheets as well as and the programming languages Python and R
• Comparing and assessing the different tools for statistical data processing and draw conclusions on their strengths and weaknesses

Role and importance of computers and data processing in the field of Economics. Using spreadsheets for statistical processing of data. Functions for descriptive statistics. Handling random data. Charting and visualization of data. Introduction to the programming language Python. Data processing module Pandas. Data frames. Reading and handling of csv files. Slicing data frames. Descriptive statistics functions. Charts and plots. Introduction totthe programming language R. Data handling using data frames. Slicing data frames. Descriptive statistics functions. Charts and plots. 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.

Lectures (4 hours per week) and Laboratory exercises (2 hours per week)

1. Three(3) Team Projects on using software to perform statistical data processing and analysis: 30%
2. Final exam (Short and problem-solving questions: 70%

The evaluation criteria are available to students at eclass here.

• Slides and notes to support lectures
• Spreadsheet software and the programming languages Python and R 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