Mat4treaT Summer School

Introduction to Basic Statistical Tools and Data Analysis in Research
Ioannina (Greece), June 27-29, 2018

Organizing committee:
V. Sakkas, C. Stalikas, N. Kourkoumelis, T. Albanis, P. Calza, E. Laurenti, G. Magnacca

Scientific board:
C. Stalikas, N. Kourkoumelis, V. Sakkas, T. Albanis, P. Calza, A. Bianco Prevot, C. Medana, M.C. Paganini, D. Scalarone, M. Sangermano, A. Arques, V. Boffa, M. Cerruti, L. Carlos, P.E. Mallon, H. Kadrispahic, D.Mainero, E. Laurenti, G. Magnacca

General information

  1. The target audience for the school is Master and PhD students in Chemistry, Material Science, Engineering, Medical Science, planning to employ statistical methods to process data and extracting valuable information. Other students interested in this topic are also welcome.
    It is a part of the activities of the Mat4treaT project.
  2. Lectures will be held at the Department of Chemistry (Room X2-090), University of Ioannina, Panepistimioupolis, 45110, Ioannina (Greece).
  3. The school fee is 250 €. Lunches, coffee-breaks are included. Accommodation is not included.
  4. Interested persons are requested to download the summerschool_registration_form.docx and send it by email to within April 30th, 2018.
  5. Please note that the number of participants is limited to 30 and the participation to the school will be guaranteed only on the basis of the first come-first served principle.
  6. Acceptance or rejection will be notified within May 11th. Students admitted will have to proceed with the payment of the fee within May 18th. Please, note that in case of missed payment the participation to the school will not be allowed.


(download the pdf file of the programme: Mat4Treat_Summer_School_Programme_final.pdf)

Wednesday 27/6 Thursday 28/6 Friday 29/6
Basic statistics and significance tests
(C. Stalikas)
Introduction to Experimental Design
(V. Sakkas)
Introduction to data science and machine learning with Python
(N. Kourkoumelis)
09.00-09.30 Registration
09.30-11.00 Distribution and types of data Introduction, basic terminology Python Crash Course
11.00-11.30 Coffee break Coffee break Coffee break
11.30-13.00 Significance testing Analysis of experiments by hand Data
Analysis – Pandas
13.00-14.30 Lunch Lunch Lunch
14.30-16.00 Non-parametric and robust statistics Analysis of experiments using computer software Data Visualization
16.00-16.20 Coffee break Coffee break Coffee break
16.20-17.50 Choosing the right statistical procedures Response surface methods (RSM) Machine
learning and statistical pattern recognition

For information requests, please send an email to