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Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization
BOB 296
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Unlock the power of data with practical Python programming for scientific computing, data analysis, and visualization.
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Detalles de producto
| Publisher | Arash Karimpour |
| Publication date | July 31, 2020 |
| Language | English |
| Print length | 544 pages |
| ISBN-10 | 1735241008 |
| ISBN-13 | 978-1735241005 |
| Item Weight | 2.53 pounds (1.15 kg) |
| Dimensions | 7.5 x 1.23 x 9.25 inches (19.1 x 3.1 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners seeking foundational knowledge in data science using Python for practical applications and theoretical insights.
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Students and Educators
Perfect for academic settings, providing structured lessons and exercises in data analysis and visualization techniques.
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Professionals Transitioning Fields
Beneficial for individuals switching to data-centric careers, offering essential skills applicable in various industries.
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Advanced Data Scientists
Not suitable for experts as it may lack advanced techniques and depth required for seasoned professionals.
DESCRIPCIÓN DEL PRODUCTO
Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization
Preguntas y respuestas de los clientes
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Pregunta:
What programming language does the book focus on?
Respuesta: The book focuses on programming with the Python language. -
Pregunta:
Does this book cover machine learning topics?
Respuesta: No, the book does not cover machine learning or artificial intelligence topics. -
Pregunta:
What libraries will I learn about in this book?
Respuesta: You will learn about libraries such as NumPy, Pandas, Matplotlib, and SciPy.
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BOB 296
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características y beneficios
- Learn Python programming for data science applications.
- Master scientific computing and data visualization techniques.
- Explore the functionalities of libraries like NumPy, Pandas, and Matplotlib.
- Understand statistical functions and time-series analysis.
- Gain hands-on experience with interpolation, curve fitting, and numerical integration.
- Focus on practical data manipulation without machine learning or AI content.
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