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.
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.
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
Informations sur le produit
Introduction to Data Science
A Python Approach to Concepts, Techniques and Applications