Workshop 1

Title: Introduction to Data Analysis and Modeling

Workshop organizer: M-r Filip Nikolovski, Prof. D-r Irena Stojkovska

Target audience:

  • Primary: university-level mathematics lecturers, university students
  • Secondary: high school and elementary school mathematics teachers, high school students, enthusiasts

Content. The following broad topics are covered during the workshop:

  • Basics of Python programming (data types and structures, conditionals, loops; libraries and modules; user-defined functions)
  • Data description and visualization
  • Models and modeling (unsupervised and supervised learning)

Objectives. The following list contains the main objectives of the workshop:

  • Familiarization with the fundamentals of the Python programming language
  • Presenting data using appropriate tools and means
  • Choose an appropriate model in a practical setting

Skills. After finishing the workshop, the participants will be able to:

  • Write short Python programs to solve a problem
  • Load, analyze and visualize data in Python using dedicated libraries and modules
  • Given some data, construct a model which can be used in applications
  • Compare several models based on their performance and choose the optimal one


  • General
    • Active Gmail account for the participants (or Anaconda installed on their computers)
  • Software
    • Browser (e.g. Google Chrome or Mozilla Firefox)
    • MS Excel or similar (optional)


Data analysis and data engineering, along with machine learning are becoming an integral part of the life of researchers and scientists. Originally restricted to the applied sciences, nowadays it is increasingly becoming an integral part of everyday life. This makes understanding data and working with it very important and very relevant. The goal of this workshop is to present the capabilities of the programming language Python in terms of data analysis (loading, displaying, visualizing, and manipulating data) and model construction and evaluation (building, evaluating, and choosing an appropriate model). No previous knowledge of programming is required.