Here is my data science stack https://github.com/mratsim/arch-Data-Science

Let's go over it:

- Is there a mature tensor/multidimensional array library in Clojure? Is there a Dataframe library?

- Are they compatible with common formats: .npy, .csv, .hdf5 and sqlite databases

- Is there a fast PCA and DBSCAN algorithm?

- Can you pass those arrays to XGBoost or LightGBM for Gradient Boosting based classification or regression.

- Do they support out-of-core processing (i.e. memory-mapped arrays) for processing that takes more than your RAM (happens often with geolocalisation)

- Can you use PyTorch through Clojure? Can you use Tensorflow? What's the GPU computing story

- Does your ML library support things like One-Hot-Encoding, cross-validation? Or more advanced like Target Encoding?

- NLP: stanfordNLP wrapper? spacy? Huggingfaces? Rasa-nlu? sentencepieces?

- Deep learning: Do you support image augmentation like Nvidia DALi or Albumentations? Mixed precision training like Nvidia APEx

- Vision: How is the OpenCV support? Does it interface with Clojure multidimensional arrays? Do you have morphological operations like erosion or dilation?

- Visualization: is there a dataviz library like plotly, ggplot2 or Altair/Vega?

And more importantly

- Are there tutorials and courses in Clojure from beginner to advanced concept? Even Julia is struggling with that.