Introduction to Data Science : A Python Approach to Concepts, Techniques and Applications /
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 applic...
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Cham :
Springer International Publishing,
2024
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| Edition: | Second edition |
| Series: | Undergraduate Topics in Computer Science
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| Subjects: | |
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| Call Number : | QA 76.73.P98 |
MARC
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| 008 | 250710s20242024sz a b 001 0 eng d | ||
| 020 | |a 9783031489556 (pbk) | ||
| 020 | |z 9783031489563 (ebk) | ||
| 040 | |a DLC |b eng |c MY-KlNDU |e rda | ||
| 050 | 0 | 0 | |a QA 76.73.P98 |
| 090 | |a QA 76.73.P98 |b I38 2024 | ||
| 100 | 1 | |a Igual, Laura, |e author | |
| 245 | 1 | 0 | |a Introduction to Data Science : |b A Python Approach to Concepts, Techniques and Applications / |c Laura Igual, Santi Seguí |
| 250 | |a Second edition | ||
| 264 | 1 | |a Cham : |b Springer International Publishing, |c 2024 | |
| 264 | 4 | |c ©2024 | |
| 300 | |a xiv, 246 pages : |b illustrations ; |c 24 cm. | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a unmediated |2 rdamedia | ||
| 338 | |a volume |2 rdacarrier | ||
| 490 | 1 | |a Undergraduate Topics in Computer Science | |
| 504 | |a Includes bibliographical references and index | ||
| 520 | |a 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. 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 | ||
| 650 | 0 | |a Data mining | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Artificial intelligence | |
| 830 | 0 | |a Undergraduate Topics in Computer Science | |
| 942 | |c 1 |2 lcc | ||
| 999 | |c 106109 |d 106109 | ||