04133nam a22005655i 4500 978-3-031-48956-3 DE-He213 20251030111815.0 cr nn 008mamaa 240412s2024 sz | s |||| 0|eng d 9783031489563 978-3-031-48956-3 10.1007/978-3-031-48956-3 doi Q336 UN bicssc COM021000 bisacsh UN thema Igual, Laura. author. aut http://id.loc.gov/vocabulary/relators/aut 75064 Introduction to Data Science [electronic resource] : A Python Approach to Concepts, Techniques and Applications / by Laura Igual, Santi Seguí. 2nd ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XIV, 246 p. 82 illus., 78 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Undergraduate Topics in Computer Science, 2197-1781 1. Introduction to Data Science -- 2. Toolboxes for Data Scientists -- 3. Descriptive statistics -- 4. Statistical Inference -- 5. Supervised Learning -- 6. Regression Analysis -- 7. Unsupervised Learning -- 8. Network Analysis -- 9. Recommender Systems -- 10. Statistical Natural Language Processing for Sentiment Analysis -- 11. Parallel Computing. This textbook presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or 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 concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science 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 This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Associate 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. Artificial intelligence Data processing. Data mining. 6941 Python (Computer program language). 64608 Artificial intelligence. 58441 Data Science. Data Mining and Knowledge Discovery. Python. 47553 Artificial Intelligence. 58441 Seguí, Santi. author. aut http://id.loc.gov/vocabulary/relators/aut 75065 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031489556 Printed edition: 9783031489570 Undergraduate Topics in Computer Science, 2197-1781 66494 https://doi.org/10.1007/978-3-031-48956-3 ZDB-2-SCS ZDB-2-SXCS 59088 59088