It's a very fast compiled language and compile faster than most of its competitors.

I supports interactive programming with rdmd and can work as a kernel inside Jupyter notebook through Jupyter's wire protocol[1].

It has friendly python vibe to it due to default to GC based ecosystem (but you choose to let go the GC where appropriate).

With the introduction of DPP, its FFI capability to C/C++ is second to none if the needs arise to interface with the existing libraries in those languages and it can also easily interface seamlessly with python and R libraries! [2]

It also has a growing library for big data analysis [3] and one of the main users of D is WekaIO, one of the prominent big data companies with the claim of the world's fastest file system for data storage [4].

If you insist on multiple dispatch style like Julia, you can emulate it as well in D [5].

If you want to see the glimpse of what D can offer for data science domain please check this "D is for Data Science" article [6].

Finally if you want to see D in action against Julia and Chapel for kernel matrix calculations that's common in AI type of applications please check this post [7].

[1] https://github.com/symmetryinvestments/jupyter-wire

[2] https://dlang.org/blog/2020/01/27/d-for-data-science-calling...

[3] https://dlang.org/blog/2018/12/04/interview-liran-zvibel-of-...

[4] http://docs.algorithm.dlang.io/latest/mir_ndslice.html

[5] https://en.wikipedia.org/wiki/Multiple_dispatch#D

[6] https://tech.nextroll.com/blog/data/2014/11/17/d-is-for-data...

[7] https://dlang.org/blog/2020/06/03/a-look-at-chapel-d-and-jul...