README.md

Data Science Notebooks

This repository contains my data science lab work, demos, and exam preparation notebooks. The focus is on statistics, probability, data cleaning, preprocessing, regression, and time series analysis.

Contents

• Data cleaning and preprocessing

  • Data_Cleaning_Demo.ipynb
  • Data_Cleaning_DemoPascal.ipynb
  • Labo_09_Data_Preprocessing_*.ipynb

• Descriptive statistics

  • Labo_01_centrummaten_test.ipynb
  • Demo_week2_Spreidingmaten_Oplossing.ipynb
  • Labo_02_Spreidingsmaten_*.ipynb
  • centrummaten_demo_oplossingen.ipynb

• Probability distributions

  • Labo_03_Discrete_Kansverdelingen_Opdracht.ipynb
  • Labo_04_Continue_Kansverdelingen_Opdracht.ipynb

• Correlation and linear regression

  • Labo_05_Correlatie_Lineaire_Regressie_*.ipynb

• Time series

  • Labo_06_Stationaire_tijdsreeksen_*.ipynb
  • Labo_08_Niet_stationaire_tijdsreeksen_*.ipynb

• Python for data science

  • Labo_07_python_voor_data_science_*.ipynb

• Review and exam preparation

  • Labo_10_Herhalingslabo_Opgave.ipynb
  • Pascal_MusabyimanaCTAIGroep20DS_21_05_2025.ipynb

How to Use

Open the notebooks with Jupyter Notebook, JupyterLab, VS Code, or Google Colab.

If you run them locally, create a Python environment and install the common data-science packages:

pip install notebook jupyter pandas numpy matplotlib seaborn scipy scikit-learn statsmodels

Then start Jupyter:

jupyter notebook