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71588853
Probability and Statistics for Computer Science, (Paperback)
Item #:
71588853
BTN 7110
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Probability and Statistics for Computer Science provides a comprehensive background in qualitative and quantitative data analysis, essential for budding computer scientists.
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What Stands Out
Comprehensive Coverage
This textbook provides in-depth coverage of probability and statistics tailored specifically for computer science applications, ensuring students grasp foundational concepts crucial for data analysis and algorithm design.
Practical Applications
Includes real-world examples and exercises focused on computer science, enabling students to apply statistical methods to solve complex problems in programming, machine learning, and data science.
Accessible Learning
Designed with clarity in mind, this book simplifies complex statistical topics, making it ideal for both beginners and advanced learners, fostering a deeper understanding of concepts through intuitive explanations.
Product Details
- Textbook aimed at computer science undergraduates late in sophomore or early in junior year
- Provides comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods
- Includes practical treatment of simulation, point inference strategies, classification, regression, principal components analysis, and clustering
- Illustrated with worked examples and pedagogical elements
- Contains problems and programming exercises at the end of each chapter
- Author: David Forsyth, ISBN: 9783319877884, Format: Paperback, Publication Date: 2019-06-04
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | June, 2019 |
| Pages | 367 |
| Subgenre | Mathematical & Statistical Software |
| Series title | No Series |
| Edition | softcover reprint of the original 1st 2018 Edition |
| Publisher | Springer International Publishing |
| Original languages | English |
| Language | English |
| Edu focus | Mathematics |
| Educational level | College |
| Awards won | IEEE Computer Society's Technical Achievement Award (2005), Marr Prize, Prize for best paper in cognitive computer vision (ECCV 2002) |
| Is collectible | N |
| Occasion | Graduation |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 8.25 x 0.81 x 11.00 in (21 x 2.1 x 27.9 cm) |
| Assembled product weight | 1.94 lb (880 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Computer Science Students
Ideal for students pursuing computer science who need a solid foundation in probability and statistics principles.
-
Data Scientists
Useful for aspiring data scientists wanting to deepen their understanding of statistical methods and probability theory.
-
Educators and Instructors
A great resource for educators teaching probability and statistics in computer science-related courses.
-
Beginner Statisticians
Not suitable for complete beginners without prior knowledge of statistics or mathematical concepts.
Product Description
Probability and Statistics for Computer Science, (Paperback)
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David Forsyth All Books Editorial Review
Probability and Statistics for Computer Science (Paperback) is a comprehensive exploration geared towards college students in the field of computing and internet. This non-fiction book, published by Springer International Publishing, spans 367 pages and dives deep into the mathematical and statistical software aspects relevant to computer science. The softcover edition, released in June 2019, is backed by notable accolades such as the IEEE Computer Society's Technical Achievement Award, indicating its value and relevance in the academic community. Readers have noted its clear explanations and structured approach, making complex concepts more accessible. This book is essential for those pursuing advanced studies in mathematics and statistics within computer science.
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Pros
- Comprehensive coverage of statistical concepts
- Suitable for college-level education
- Includes practical applications for computer science
- Award-winning author and recognized work
- Well-structured for easy comprehension
Cons
- Some might find the content complex for beginners
Product Price History
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BTN 7110
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Features & Benefits
- Designed for computer science undergraduates in their sophomore or junior year.
- Covers key topics such as probability, random variables, and statistical methods.
- Includes practical treatments of simulation, inference strategies, and hypothesis testing.
- Introduces machine learning techniques including classification and regression.
- Features numerous worked examples and pedagogical elements for enhanced learning.
- Comes with instructor resources and model solutions for easier teaching.
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