Titre :	The data science handbook
Type de document :	texte imprimé
Auteurs :	Field Cady
Editeur :	New York : John Wiley & Sons
Année de publication :	2017
Importance :	1 vol. (396 p.)
Présentation :	illustrations
Format :	25 cm
ISBN/ISSN/EAN :	978-1-119-09294-0
Langues :	Anglais (eng)
Catégories :	Informatique
Mots-clés :	Informatique
Index. décimale :	005.74 Architecture et conception des bases de données, fichiers et systèmes de gestion de bases de données (aspects informatiques des systèmes de recherche et stockage de l'information, bases et fichiers de données, ouvrages généraux sur les bases de données, programmation et programmes pour la gestion interne des bases et des fichiers de données, programmes informatiques permettant l'utilisation des bases ou des fichiers de données, traitement des fichiers de données, validation des données dans le traitement des fichiers)
Résumé :	Finding a good data scientist has been likened to hunting for a unicorn. The required combination of software engineering skills, mathematical fluency, and business savvy are simply very hard to find in one person. On top of that, good data science is not just rote application of trainable skillsets, but rather requires the ability to think critically in all these areas. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The author describes the classic machine learning algorithms, including the mathematics needed to understand what's really going on. Classical statistics is taught so that readers learn to think critically about the interpretation of data and its common pitfalls. In addition, basic software engineering and computer science skillsets often lacking in data scientists are given a central place in the book. Visualization tools are reviewed, and their central importance in data science is highlighted. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter. All of these are topics explained in the context of solving real-world modern data problems. Chapter coverage includes: Introduction: Becoming a Unicorn; Data Science Programming Languages; Visualizations; Software Engineering Concepts; Data Formats; Mathematical Foundations; Classical Statistics; Machine Learning; Computer Science Concepts; Software Packages; Big Data Tools; Common Domains of Application; and Communicating Results.
Côte titre :	Fs/25035

The data science handbook [texte imprimé] / Field Cady . - New York : John Wiley & Sons, 2017 . - 1 vol. (396 p.) : illustrations ; 25 cm.
ISBN : 978-1-119-09294-0
Langues : Anglais (eng)

Catégories :	Informatique
Mots-clés :	Informatique
Index. décimale :	005.74 Architecture et conception des bases de données, fichiers et systèmes de gestion de bases de données (aspects informatiques des systèmes de recherche et stockage de l'information, bases et fichiers de données, ouvrages généraux sur les bases de données, programmation et programmes pour la gestion interne des bases et des fichiers de données, programmes informatiques permettant l'utilisation des bases ou des fichiers de données, traitement des fichiers de données, validation des données dans le traitement des fichiers)
Résumé :	Finding a good data scientist has been likened to hunting for a unicorn. The required combination of software engineering skills, mathematical fluency, and business savvy are simply very hard to find in one person. On top of that, good data science is not just rote application of trainable skillsets, but rather requires the ability to think critically in all these areas. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The author describes the classic machine learning algorithms, including the mathematics needed to understand what's really going on. Classical statistics is taught so that readers learn to think critically about the interpretation of data and its common pitfalls. In addition, basic software engineering and computer science skillsets often lacking in data scientists are given a central place in the book. Visualization tools are reviewed, and their central importance in data science is highlighted. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter. All of these are topics explained in the context of solving real-world modern data problems. Chapter coverage includes: Introduction: Becoming a Unicorn; Data Science Programming Languages; Visualizations; Software Engineering Concepts; Data Formats; Mathematical Foundations; Classical Statistics; Machine Learning; Computer Science Concepts; Software Packages; Big Data Tools; Common Domains of Application; and Communicating Results.
Côte titre :	Fs/25035