|
|
|
|
| LEADER |
02221nmm a2200421 u 4500 |
| 001 |
EB001931471 |
| 003 |
EBX01000000000000001094373 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 050 |
|
4 |
|a HG106
|
| 100 |
1 |
|
|a Chin, Andrew
|
| 245 |
0 |
0 |
|a Leveraging data science in asset management
|c Andrew Chin, Celia Chen
|
| 260 |
|
|
|a [Place of publication not identified]
|b O'Reilly
|c 2019
|
| 300 |
|
|
|a 1 streaming video file (42 min., 14 sec.)
|
| 653 |
|
|
|a Finance / Econometric models / fast / (OCoLC)fst00924377
|
| 653 |
|
|
|a Investments / fast / (OCoLC)fst00978234
|
| 653 |
|
|
|a Econometrics / fast / (OCoLC)fst00901574
|
| 653 |
|
|
|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
|
| 653 |
|
|
|a Investments / http://id.loc.gov/authorities/subjects/sh85067715
|
| 653 |
|
|
|a Finances / Modèles économétriques
|
| 653 |
|
|
|a Finance / Econometric models
|
| 653 |
|
|
|a Investissements
|
| 653 |
|
|
|a Économétrie
|
| 653 |
|
|
|a Apprentissage automatique
|
| 653 |
|
|
|a Econometrics / http://id.loc.gov/authorities/subjects/sh85040763
|
| 653 |
|
|
|a Investments
|
| 653 |
|
|
|a Machine learning / fast / (OCoLC)fst01004795
|
| 700 |
1 |
|
|a Chen, Celia
|e author
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 500 |
|
|
|a Title from title screen (viewed November 14, 2019). - Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York
|
| 776 |
|
|
|z 0636920339496
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/0636920339519/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 000
|
| 082 |
0 |
|
|a 332.6
|
| 082 |
0 |
|
|a 332
|
| 520 |
|
|
|a "Asset managers of all stripes are ramping up their data science capabilities. Andrew Chin and Celia Chen (AllianceBernstein) offer an overview of data science applications within the asset management industry, covering case studies spanning different functions within an asset management organization. Along the way, they look at an investment research project that used a new "big dataset" as well as a machine algorithm to create a more powerful prediction model and detail a machine learning model they developed to understand their clients better."--Resource description page
|