Data-Science-Workshop

This project contains jupyter notebook files, that were shown during the workshop and that can be used as a good starting point for own projects.

Introductional Part

Topic	Notebookfile
Python - Introduction	Basic Python Concepts
Introduction to Numpys	Numpys
Introduction to Pandas and Graphical Representation	Pandas and Graphical Representation
Hints on Data Preprocessing	Data Preprocessing
Markup	An overview of markup commands to format text in notebooks can be found here:

To run the notebooks the following tools and libs need to be installed:

conda install -c conda-forge cufflinks-py
pip install plotly==3.10.0
pip install python-highcharts

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Basic Python Concepts.ipynb		Basic Python Concepts.ipynb
Data Preprocessing.ipynb		Data Preprocessing.ipynb
Data.csv		Data.csv
Data.xlsx		Data.xlsx
Decission Trees.ipynb		Decission Trees.ipynb
Gaussian Mixture Model.ipynb		Gaussian Mixture Model.ipynb
Gradient_Boosting_(LightGBM).ipynb		Gradient_Boosting_(LightGBM).ipynb
K Nearest Neighbours.ipynb		K Nearest Neighbours.ipynb
K-Means Clustering.ipynb		K-Means Clustering.ipynb
Logistic Regression.ipynb		Logistic Regression.ipynb
Multiple Linear Regression.ipynb		Multiple Linear Regression.ipynb
Numpys.ipynb		Numpys.ipynb
Pandas and Graphical Representation.ipynb		Pandas and Graphical Representation.ipynb
README.md		README.md
Random Forest.ipynb		Random Forest.ipynb
Simple Linear Rgression.ipynb		Simple Linear Rgression.ipynb
Support Vector Machine.ipynb		Support Vector Machine.ipynb
WORKSHOP.ipynb		WORKSHOP.ipynb