Things To Know
While working, use this tab as a dictionary because the NumPy Glossary provides clear definitions for terms you'll encounter in tutorials and error messages.
You'll define the fundamental components:
dtype (the type of values stored),
shape and axis (the size of the array and the direction in which you are working),
ndarray (the main array object). It explains practical concepts like...
- ufuncs (fast element-wise functions) and
- broadcasting (how different shapes can still cooperate),
- as well as performance terms like contiguous memory and strides.
- It also discusses...
- array memory order (C vs. Fortran),
- boolean masks and advanced indexing (methods to choose data),
- views vs. copies (why a slice may alter the original).
The fastest way to convert vocabulary into comprehension is to keep the glossary open while you read the documents.
For more specific details please click on the following link
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15 Math Concepts Every Data Scientist Should Know
by
ISBN: 9781837634187Publication Date: 2024-08-30Data science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers.
Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you’ll learn why data varies and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts, such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems.
By the end of the book, you’ll have the confidence to apply key mathematical concepts to your data science challenges.
