Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications 1st ed. 2017

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.Introduction to Data ScienceJordi VitriàToolboxes for Data ScientistsEloi Puertas and Francesc DantíDescriptive statisticsPetia Radeva and Laura IgualStatistical InferenceJordi Vitrià and Sergio EscaleraSupervised LearningOriol Pujol and Petia RadevaRegression AnalysisLaura Igual and Jordi VitriàUnsupervised LearningPetia Radeva and Oriol PujolNetwork AnalysisLaura Igual and Santi SeguíRecommender SystemsSanti Seguí and Eloi PuertasStatistical Natural Language Processing for Sentiment AnalysisSergio Escalera and Santi SeguíParallel ComputingFrancesc Dantí and Lluís Garrido