Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization
Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization
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Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization
Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization
Unlock the power of data with practical Python programming for scientific computing, data analysis, and visualization.
Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization
Nº de artículo: 45560432

Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization

Nº de artículo: 45560432

CRC 22880

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What Stands Out

Comprehensive Curriculum
Offers a thorough grounding in scientific computing, data analysis, and visualization, making it ideal for beginners and experienced learners seeking to deepen their Python skillset effectively.
Hands-On Approach
Emphasizes practical applications through exercises and projects, allowing users to immediately apply concepts and gain real-world experience in data science.
Up-to-Date Resources
Incorporates the latest tools and techniques in Python data science, ensuring learners are equipped with relevant skills needed in today’s fast-evolving job market.

Detalles de producto

Shop Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization online at a best price in Costa Rica. 1735241008
  • In this book, readers learn about:• Programming with the Python language• Scientific computing, data analysis, and data visualization with Python• Data structure in Python• NumPy library and NumPy arrays• Statistical functions• Pandas library and Pandas DataFrames• Time-series in Python• Matplotlib library and data visualization• SciPy library• Interpolation, curve fitting, root finding, and numerical integration• Signal processing and digital filtering• Reading and writing data filesPlease note that this book focuses on scientific computing and data analysis and DOES NOT cover machine learning or artificial intelligence topics.
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?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners seeking foundational knowledge in data science using Python for practical applications and theoretical insights.

  • Students and Educators

    Perfect for academic settings, providing structured lessons and exercises in data analysis and visualization techniques.

  • Professionals Transitioning Fields

    Beneficial for individuals switching to data-centric careers, offering essential skills applicable in various industries.

Not Suitable For
  • Advanced Data Scientists

    Not suitable for experts as it may lack advanced techniques and depth required for seasoned professionals.

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Preguntas y respuestas de los clientes

  • 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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CRC 22880

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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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