Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Outlier removal,Data science,Cognitive automation,Computer science,Visualization,Model building,Raw data,Automation,Artificial intelligence,Missing value imputation,Combinatorial explosion,Machine learning |
DocType | Citations | PageRank |
Conference | 5 | 0.55 |
References | Authors | |
1 | 13 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alain Biem | 1 | 288 | 18.64 |
| Maria Butrico | 2 | 5 | 0.55 |
| Mark Feblowitz | 3 | 5 | 0.55 |
| Tim Klinger | 4 | 177 | 15.36 |
| Yuri Malitsky | 5 | 278 | 17.79 |
| Kenney Ng | 6 | 141 | 13.20 |
| Adam Perer | 7 | 1187 | 68.74 |
| C. Reddy | 8 | 19 | 2.22 |
| Anton Riabov | 9 | 496 | 37.69 |
| Horst Samulowitz | 10 | 316 | 26.05 |
| Daby M. Sow | 11 | 131 | 17.69 |
| Gerald J. Tesauro | 12 | 3130 | 1048.34 |
| Deepak S. Turaga | 13 | 564 | 48.11 |