TY  - BOOK
AU  - Igual,Laura
AU  - Segu�i,Santi
TI  - Introduction to data science: a Python approach to concepts, techniques and applications 
T2  - Undergraduate topics in computer science,
SN  - 9783031489563
AV  - Q180.55.Q36 
PY  - 2024///
CY  - Cham
PB  - Springer
KW  - Python (Computer program language)
KW  - Quantitative research
KW  - Data mining
KW  - fast
KW  - Mathematical statistics
KW  - Qualitative research
N1  - 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
N2  - 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
UR  - https://login.libproxy.lib.unc.edu/login?url=https://link.springer.com/10.1007/978-3-031-48956-3
ER  -