04974cam a2200649 i 4500 1430431939wcmSPRnew OCoLC 20251123081015.0 m o d cr un||||aucua 240418s2024 sz a ob 001 0 eng d GBC593582 bnb 300601819 Uk 1430322139wcmSPRnew 1443087681wcmSPRnew 1444215268wcmSPRnew 1492955720wcmSPRnew 1530779749wcmSPRnew 9783031489563 electronic bk. 10.1007/978-3-031-48956-3 doi CIPO000260734 (OCoLC)1430431939 (OCoLC)1430322139 (OCoLC)1443087681 (OCoLC)1444215268 (OCoLC)1492955720 (OCoLC)1530779749 GW5XE eng rda pn GW5XE EBLCP OCLCO YDX OCLCQ NEHVU OCLCQ UKAHL EMRUN OCLCQ SFB N$T OCLCQ OCLCL UKMGB OCLCO Q180.55.Q36 COM021000 bisacsh UN bicssc UN thema Igual, Laura, author. Introduction to data science : a Python approach to concepts, techniques and applications / Laura Igual, Santi Segu�i. Second edition. Cham : Springer, 2024. 1 online resource (xiv, 246 pages) : illustrations (some color) text txt rdacontent. computer c rdamedia. online resource cr rdacarrier. Undergraduate topics in computer science, 2197-1781. Includes bibliographical references and index. 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. Access limited to UNC Chapel Hill-authenticated users. Unlimited simultaneous users. 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 Matem�atiques i Inform�atica, Universitat de Barcelona, Spain. Dr. Santi Segu�i is an Associate Professor at the same institution. The authors wish to mention that some chapters were co-written by Jordi Vitri�a, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera. Online resource; title from PDF title page (SpringerLink, viewed April 18, 2024). Content provider: SpringerLink. Python (Computer program language) Quantitative research. Data mining fast. Mathematical statistics fast. Python (Computer program language) fast. Qualitative research fast. Segu�i, Santi, author. Springer e-books (online collection). Computer science. 2024. OCLC WorldShare Collection Manager managed collection. SPRnew. Original 3031489551 9783031489556 (OCoLC)1412623335. Print version : 9783031489556. Undergraduate topics in computer science, 2197-1781. https://login.libproxy.lib.unc.edu/login?url=https://link.springer.com/10.1007/978-3-031-48956-3 Full text available via the UNC-Chapel Hill Libraries Batch load history: 1294 wcm SPRnew records loaded 20251015, ka NOTAUTHO Batch load history: 657 wcm SPRnew records loaded 20250910, bj Batch load history: 835 wcm SPRnew records loaded 20240224, ka Batch load history: 2162 wcm SPRnew records loaded 20240808, ka 607958 607958