Towards Cognitive Automation of Data Science
Abstract
A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e.g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.
Cite
Text
Biem et al. "Towards Cognitive Automation of Data Science." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9281Markdown
[Biem et al. "Towards Cognitive Automation of Data Science." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/biem2015aaai-cognitive/) doi:10.1609/AAAI.V29I1.9281BibTeX
@inproceedings{biem2015aaai-cognitive,
title = {{Towards Cognitive Automation of Data Science}},
author = {Biem, Alain and Butrico, Maria and Feblowitz, Mark and Klinger, Tim and Malitsky, Yuri and Ng, Kenney and Perer, Adam and Reddy, Chandra and Riabov, Anton and Samulowitz, Horst and Sow, Daby M. and Tesauro, Gerald and Turaga, Deepak S.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {4268-4269},
doi = {10.1609/AAAI.V29I1.9281},
url = {https://mlanthology.org/aaai/2015/biem2015aaai-cognitive/}
}