Introduction to Data Science

A Python Approach to Concepts, Techniques and Applications
1. Auflage 2017
ISBN: 978-3-319-50016-4
Verlag: Springer

Buch, Englisch, 218 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 3999 g

Reihe: Undergraduate Topics in Computer Science

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Igual, Laura

Seguí, Santi

Weitere Mitwirkende

Vitrià, Jordi

Puertas, Eloi

Radeva, Petia

Pujol, Oriol

Escalera, Sergio

Dantí, Francesc

Garrido, Lluís

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Introduction to Data Science
Jordi Vitrià

Toolboxes for Data Scientists
Eloi Puertas and Francesc Dantí

Descriptive statistics
Petia Radeva and Laura Igual

Statistical Inference
Jordi Vitrià and Sergio Escalera

Supervised Learning
Oriol Pujol and Petia Radeva

Regression Analysis
Laura Igual and Jordi Vitrià

Unsupervised Learning
Petia Radeva and Oriol Pujol

Network Analysis
Laura Igual and Santi Seguí

Recommender Systems
Santi Seguí and Eloi Puertas

Statistical Natural Language Processing for Sentiment Analysis
Sergio Escalera and Santi Seguí

Parallel Computing
Francesc Dantí and Lluís Garrido

Über Autor(innen)

Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution.

The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dantí and Lluís Garrido.