Jordi Warmenhoven
Senior SAP Consultant at Repona AB
Greater Helsingborg Metropolitan Area Sweden
Summary
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Rockstar🎓
Top SchoolJordi Warmenhoven is a Senior SAP Consultant with 11 years of experience, currently based in the Greater Helsingborg area and working at Repona AB. He blends enterprise SAP expertise with a strong background in data visualization, probabilistic programming and Bayesian statistics, often working "fairly Bayesian" and mostly in Python. Jordi is an active open-source contributor and QA-focused engineer, with notable work on popular projects like seaborn and educational repos that implement ISLR and Coursera machine learning examples. His contributions show attention to robust testing and clear visual communication of statistical analyses, as well as hands-on implementation of hierarchical Bayesian models. Comfortable operating between business systems and data science, he brings a pragmatic curiosity and a habit of improving code quality and reproducible analyses.
11 years of coding experience
Master's Degree, Master's Degree at Maastricht UniversityExchange student programme (Reims, France), Exchange student programme (Reims, France) at NEOMA Business School
Dutch, Swedish, English, French
Stackoverflow
193reputation
4kreached
4answers
3questions
Github Skills (25)
data-visualizations10
bayesian-statistics10
seaborn10
python10
matplotlib10
testing10
k-means-clustering10
scikit10
pandas10
statistics10
machine-learning10
data-visualisation10
statistic10
numpy10
linear-regression10
Programming languages (4)
Github contributions (5)
JWarmenhoven/Coursera-Machine-Learning
Dec 2015 - Aug 2018
Coursera Machine Learning - Python code
Role in this project:
Data Scientist
Data ScientistContributions:49 commits, 42 pushes, 1 branch in 2 years 8 months
Contributions summary:Jordi primarily contributed to programming exercises related to machine learning concepts covered in the Coursera course. Their work included implementing and modifying code within Jupyter Notebooks to explore topics such as linear regression, logistic regression, and K-means clustering. They demonstrated skills in data manipulation, statistical modeling, and applying machine learning techniques. The user's changes directly reflect a focus on practical application of machine learning algorithms.
python-codepythoncoursera-machine-learningclassification-algorithmlogistic-regression
JWarmenhoven/ISLR-python
Jun 2015 - Jun 2022
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Role in this project:Data Scientist
Data ScientistContributions:219 commits, 4 PRs, 203 pushes in 7 years 1 month
Contributions summary:Jordi implemented the code for Chapter 3 of "An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code". The user's contributions focused on building various visualizations and running regression analysis based on advertising datasets. The user was responsible for implementing various analyses and tests to visualize data and explore correlation in their dataset.
pythonprincipal-component-analysisregression-modelslinear-modelsanova
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