Projects and Repositories
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Introduction to Data Science with Python
This Repository provides an overview how to analyze data with commonly used
statistical methods in Python. Besides an general introduction to syntax and
basic operations, we will use packages like: pandas, numpy, scipy
and matplotlib. Useful cheatsheets can be downloaded:
datacamp cheatsheets
Statistical Learning/ Machine Learning with R
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis.
This repository contains exercises from a course in computational statistics, starting with simple Monte Carlo simulations to become familiar with this topic.
Chapter 01 of the script:
Chapter 02 of the script:
Chapter 03 of the script:
Chapter 04 of the script:
Chapter 05 of the script:
Chapter 06 of the script:
Deep Learning with Python
A subset of machine learning modeled loosely on the neural pathways. Deep refers to the multiple layers between the input and output layers.
This Repository provides an example how to implement deep learning algorithms in Python. We will first use data to train the neural network, while testing the forecasts afterwards.
Time Series with R
This Repository contains a basic introduction to Time Series analysis with R. We learn how to:implement dynamic regression models, model selection & correction, inference and produce forecasts.
Interactive Networks Visualisation with Python
Python, a versatile and widely-used programming language, offers numerous libraries and tools to create interactive network graph visualizations. In this Repository, we will explore the key components and popular Python libraries that enable the creation of interactive network graph visualizations.
Simulation Study with Python
The scope of this project is to replicate the major findings from the paper “Are Credit Markets Still Local? Evidence from Bank Branch Closings” by Hoai-Luu Q. Nguyen by using Python as Data Science tool. Additionally, I create a simulated dataset to show the robustness of the identification strategy.