The Data Science Design Manual /
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and...
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| Main Author: | |
|---|---|
| Format: | Specialized reference book |
| Language: | English |
| Published: |
Germany :
Springer,
2017.
|
| Edition: | 1st ed. |
| Series: | Texts in Computer Science,
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| Subjects: | |
| Online Access: | https://dlib.phenikaa-uni.edu.vn/handle/PNK/1910 |
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| 040 | |a Phenikaa Uni |b eng |e pn |e aacr2 |c Phenikaa Uni | ||
| 041 | |a eng | ||
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| 100 | 1 | |a Skiena, Steven S, |e author. | |
| 245 | 1 | 4 | |a The Data Science Design Manual / |c by Steven S. Skiena. |
| 250 | |a 1st ed. | ||
| 260 | |a Germany : |b Springer, |c 2017. | ||
| 300 | |a 426 tr. ; |c 24cm. | ||
| 490 | 1 | |a Texts in Computer Science, | |
| 520 | |a This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 0 | |a Pattern recognition. | |
| 650 | 0 | |a Statistics. | |
| 650 | 0 | |a Visualization. | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |0 https://scigraph.springernature.com/ontologies/product-market-codes/I18030 |
| 650 | 2 | 4 | |a Big Data/Analytics. |0 https://scigraph.springernature.com/ontologies/product-market-codes/522070 |
| 650 | 2 | 4 | |a Pattern Recognition. |0 https://scigraph.springernature.com/ontologies/product-market-codes/I2203X |
| 650 | 2 | 4 | |a Statistics and Computing/Statistics Programs. |0 https://scigraph.springernature.com/ontologies/product-market-codes/S12008 |
| 650 | 2 | 4 | |a Visualization. |0 https://scigraph.springernature.com/ontologies/product-market-codes/M14034 |
| 856 | |u https://dlib.phenikaa-uni.edu.vn/handle/PNK/1910 | ||
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