Course Material

Lecture 1 – Introduction

• SciData_Lecture_01.pdf

Lecture 2 – Data visualization using interactive Jupyter Notebooks

• SciData_Lecture_02.pdf

• Hands-on

  • intro.ipynb

  • 2025_gdp_income_life_expect.csv

• Example workflows (you need to conda install a few packages if you'd like to run these notebooks):

  • 13_ExcitedStateAnalysis.ipynb

  • Cu3N.ipynb, Cu3N.cif

  • 01-basic-bioinfo.ipynb

Lecture 3 – Analysis and visualization of univariate datasets and one-variable functions

• SciData_Lecture_03.pdf

• Hands-on

  • xaboom.ipynb

  • c_data_xaboom.csv

Lecture 4 – Scientific images: how to convey information and best practices in data visualization

• SciData_Lecture_04.pdf

• VizualizationFactSheet.pdf

Lecture 5 – Visualization of two-variable functions

• SciData_Lecture_05.pdf

• two_var_fcts.ipynb

• trajectory.csv

• trajectory_newton_raphson.csv

Lecture 6 – Working with large datasets: Introduction to pandas

• SciData_Lecture_06.pdf

• titanic.ipynb

Lecture 7 – Time and space visualizations: maps and timelines

• SciData_Lecture_07.pdf

• gapminder.ipynb

Lecture 8 – How to prepare a good poster

• SciData_Lecture_08.pdf

Lecture 9 – Good poster & Isocurves, isosurfaces

• SciData_Lecture_09.pdf

• iso.ipynb

• Rysy mountain:

  • rysy_coordinates.txt

  • rysy_elevation.txt

• Electron density:

  • grid_x.npy

  • grid_y.npy

  • grid_z.npy

  • density_on_grid.npy

Lecture 11 – Principal component analysis

• SciData_Lecture_11.pdf

• abalone.ipynb

• abalone.data

Lecture 12 – Graphical abstracts

• SciData_Lecture_12.pdf