Added Data Pack directory

python/speckle-data-pack/README.md

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+# Speckle Data Pack
+Speckle Data Pack is a comprehensive content pack that has been designed to inspire curiosity and demonstrate a practical way to gain insights from your BIM data stored in Speckle. The aim of the content pack is to provide an intermediate-level resource for data-minded architects, engineers, and BIM managers who are looking to take their next step beyond Excel.
+
+This training set provides a convenient way to work with sample BIM data. It is a playground for testing and experimenting, and for becoming familiar with Speckle schema. The pack includes a Speckle project (stream) with 30 Revit models and [Jupyter](https://jupyter.org/) notebook examples showing different ways of working with a large data set containing the whole project portfolio. Set of Revit models in the Speckle Data Pack project (stream) is a collection of simple buildings that vary in size and contain basic Revit types which makes it easy to play with and get results quickly.
+
+You will learn how to load data from Speckle with [specklepy](https://github.com/specklesystems/specklepy) (our Python SDK), then how to convert it into tables and leverage [Pandas](https://pandas.pydata.org/) to analyse and manipulate BIM data. Bonus steps with [PandasAI](https://pandas-ai.readthedocs.io/en/latest/) show you how to integrate AI into you data workflows. OpenAI’s ChatGPT is used in examples but PandasAI offers more options for LLM so feel free to experiment!
+
+Basic Python skills are required and some knowledge of Pandas is beneficial. Instructions how to set up Jupyter Notebooks in Visual Studio Code are [here](https://code.visualstudio.com/docs/datascience/jupyter-notebooks).
+## Speckle Project
+
+[Speckle Data Pack I](https://speckle.xyz/streams/729cb7c74b) 
+
+It contains 30 simple buildings from Revit 2022, 2023 and 2024. Models are stored either as
+
+- versions (commits) of a single model (branch) called `all_in_one`
+- individual models (branches) named after the Revit files (SB2201, …)
+
+## Code Examples
+
+- SDP_1_room data from model.ipynb
+- SDP_2_room data from project.ipynb
+- SDP_3_room data with pandas.ipynb
+- SDP_4_material data with pandas.ipnyb
+
+## Files
+
+Download all files [here](https://drive.google.com/file/d/1no_R9lgh5MP9SmSsHdVDXeFkqjWcri_Z/view?usp=sharing), in case you want to set up this content pack on your own server. 
+
+- 30 Revit models
+- Grasshopper definition that generated the BIM data set

speckle-examples.code-workspace

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 {
 	"folders": [
+    {
+      "name": "speckle data pack",
+      "path": "./python/speckle-data-pack"
+    },
     {
       "name": "jupyter",
       "path": "./python/speckle-jupyter-notebooks"