Buch, Englisch, 282 Seiten, Format (B × H): 156 mm x 234 mm
Buch, Englisch, 282 Seiten, Format (B × H): 156 mm x 234 mm
Reihe: Chapman & Hall/CRC Data Science Series
ISBN: 978-1-03-210034-0
Verlag: Taylor & Francis Ltd
This book’s first section explores how to build data science projects that deploy to production with no frills or fuss. Its second section covers the rudiments of administering a server, including Linux, application, and network administration before concluding with a demystification of the concerns of enterprise IT/Administration in its final section, making it possible for data scientists to communicate and collaborate with their organization’s security, networking, and administration teams.
Key Features:
• Start-to-finish labs take readers through creating projects that meet DevOps best practices and creating a server-based environment to work on and deploy them.
• Provides an appendix of cheatsheets so that readers will never be without the reference they need to remember a Git, Docker, or Command Line command.
• Distills what a data scientist needs to know about Docker, APIs, CI/CD, Linux, DNS, SSL, HTTP, Auth, and more.
• Written specifically to address the concern of a data scientist who wants to take their Python or R work to production.
There are countless books on creating data science work that is correct. This book, on the otherhand, aims to go beyond this, targeted at data scientists who want their work to be than merely accurate and deliver work that matters.
Zielgruppe
Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
- Mathematik | Informatik EDV | Informatik Informatik Rechnerarchitektur
- Mathematik | Informatik EDV | Informatik Technische Informatik Externe Speicher & Peripheriegeräte
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
- Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Datenbankprogrammierung
Weitere Infos & Material
What is the role of Data Science in orgs? What is the open data science platform: A centralized location to manage data science dev environment, as well as test and deploy content to end-users. Components. Determining requirements for your platform. IT Basics for Data Scientists. Servers and the Cloud. Networking. Security. Logging into different services. Scaling, Common Hardware Configurations. Platform Architecture and Management. Environments. Data Storage and Access. Package Management. Scaling. Appendix.