# 💻 Programming languages
> [!danger] 📊 Scientific programming, ML pipelines and data visualization
> *Fluent in:*
> - Julia
> - Python
> - R
> [!danger] Smart Contract Programming
> *Some experience:*
> - Solidity
> [!danger] Other programming languages
> *Fluent in:*
> - C++
>
> *Some experience, in decreasing order:*
> - SQL
> - C#
> - JavaScript
> - bash
> - CUDA
> - SAS
> - Java
# Technologies
> [!hint] Explored Python packages
>
> - `scipy`
> - `numpy`
> - `pandas`
> - `torch`
> - `marshall`
> - `sys`
> - `os`
>
> *Not a package, but good knowledge about:*
> - Python C API
> [!hint] Explored Julia packages
> - `Discord.jl`
> - `OpenAI.jl`
> - `Flux.jl`
> - `Zygote.jl`
>
> *Not a package, but some knowledge about:*
> - Julia dispatch
> - Julia metaprogramation (macros)
# 🤸♂️ Skills and understood concepts
> - *Statistics* (classical, asymptotic, bayesian, stochastic process, probabilistic graph models)
> - *Optimization* (linear, non-linear, convex, conditioned convexity)
> - *Topology, calculus and algebra* (complex calculus, differential geometry, manifolds, Banach space, Function space, Fourier transform, wavelet transform, etc)
> - *Linear Algebra* (rotation matrices, diagonalization, eigen-all-the-stuff)
>[!example] Machine learning and statistical inference
> - Supervised Learning
> - Unsupervised Learning
> - Distributed Learning and Statistical Inference
> - Deep Learning
> - Density Estimation, with or without measurement error (KDE, DKDE)
> - Hidden Markov Chain model
> - Kalman Filters
> - Other useful concepts for ML
> - Backward propagation
> - Automatic differentiation
> - Simpson's paradox
> [!example] Computer science
> - Graph algorithms
> - Algorithm correctness proofs
> - Algorithmic complexity proofs
> - Many data structures, such as hash maps, binary trees, Merkle trees, adjacency list or matrix, chained lists, etc.
> [!example] Development
> - Git
> - Markup languages (JSON, HTML, Markdown, XML)
> - OOP
> - UML graphs
> - REST
>[!example] Management and administration
>- Scrum