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Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
86% of respondents would recommend this to a friend
ARS 111904
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Master the math needed to excel in data science, machine learning, and statistics.
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Detalles de producto
- Master essential mathematical concepts like calculus, linear algebra, and statistics for data science, machine learning, and statistics
- Learn to use Python libraries such as SymPy, NumPy, and scikit-learn to explore mathematical concepts and techniques like linear regression, logistic regression, and neural networks
- Gain practical insights into the field of data science and learn to apply mathematical knowledge to maximize career opportunities
- Perform descriptive statistics, hypothesis testing, and manipulate vectors and matrices including matrix decomposition
- Integrate incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
- Navigate through a data science career, avoid common pitfalls and biases, and stand out in the job market through practical mathematical and statistical knowledge
| Publisher | O'Reilly Media |
| Publication date | July 5, 2022 |
| Edition | 1st |
| Language | English |
| Print length | 349 pages |
| ISBN-10 | 1098102932 |
| ISBN-13 | 978-1098102937 |
| Item Weight | 1.28 pounds (580 grams) |
| Dimensions | 7 x 0.75 x 9 inches (17.8 x 1.9 x 22.9 cm) |
Who Should Buy?
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Aspiring Data Scientists
Those new to data science will gain foundational knowledge essential for analyzing data effectively.
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Students of Mathematics
Mathematics students looking to apply linear algebra and statistics concepts in practical data science scenarios.
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Professionals Transitioning
Individuals shifting careers into data science will find useful resources to build necessary mathematical skills.
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Advanced Practitioners
Experienced data scientists seeking advanced methodologies may find the content too basic and lacking in depth.
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Linear Editorial Review
Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics breaks down the complex concepts of data science into accessible ideas. With clear explanations and practical examples, it's suitable for beginners and those looking to refresh their math skills. The book provides a comprehensible approach to essential mathematical principles, making it a valuable asset for anyone involved in data analysis. Many readers find it easy to read, though some note a certain density in parts. The integration of Python implementations throughout the chapters enriches the learning experience, culminating in a challenging yet rewarding final chapter on Neural Networks. This book is recommended for its thorough coverage on identifying bias and ensuring accurate conclusions in data analysis.
Customer Reviews & Ratings
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5 estrella
80%
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4 estrella
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3 estrella
5%
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2 estrella
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1 estrella
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ventajas
- Clear explanations of complex data science concepts
- Practical examples enhance understanding
- Suitable for beginners and skill refreshers
- Integration of Python examples is beneficial
- Methodical approach to math in data science
Contras
- Some sections may feel dense to new readers
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ARS 111904
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características y beneficios
- Learn and apply essential mathematical concepts like calculus, linear algebra, statistics, and machine learning in Python
- Understand regression models like linear regression, logistic regression, and neural networks
- Perform descriptive statistics and hypothesis testing on datasets and interpret p-values
- Gain practical insights into the state of data science and maximize your career potential
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