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Learning Ipython for Interactive Computing and Data Visualization - Second Edition
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Learning Ipython for Interactive Computing and Data Visualization - Second Edition
Get started with Python for data analysis and numerical computing in the Jupyter notebook
Learning Ipython for Interactive Computing and Data Visualization - Second Edition
Item #: 86750251

Learning Ipython for Interactive Computing and Data Visualization - Second Edition

86% of respondents would recommend this to a friend

Item #: 86750251

€ 28

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What Stands Out

Interactive Learning
Offers an engaging, hands-on approach to mastering IPython and Jupyter Notebook, making it ideal for data analysts eager to enhance their skills in real-time computing.
Comprehensive Coverage
Provides in-depth tutorials on data visualization and numerical computing, ensuring learners gain a thorough understanding crucial for effective data analysis.
Updated Techniques
Features the latest practices and tools in interactive computing, empowering users to leverage current technology trends for optimized data manipulation and visualization.

Product Details

Shop Learning Ipython for Interactive Computing and Data Visualization - Second Edition online at a best price in Greece. 1783986980
Publisher Packt Publishing
Publication date October 1, 2015
Edition 2nd
Language English
Print length 175 pages
ISBN-10 1783986980
ISBN-13 978-1783986989
Item Weight 12.8 ounces (362.88 grams)
Dimensions 7.5 x 0.46 x 9.25 inches (19.1 x 1.2 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Analysts

    Ideal for data analysts seeking practical skills in data visualization and interactive computing with Python.

  • Beginners

    Great for programming novices wanting to learn Python and data analysis using Jupyter Notebook effectively.

  • Educators

    Useful for teachers wishing to incorporate interactive computing and data visualization in their curriculum using IPython.

Not Suitable For
  • Advanced Users

    May not suit experienced programmers who already possess advanced skills in Python and data visualization.

Product Description

Learning Ipython for Interactive Computing and Data Visualization - Second Edition

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Customer Questions & Answers

  • Question: What is 'Learning IPython for Interactive Computing and Data Visualization' about?

    Answer: This book serves as a comprehensive guide for those interested in utilizing IPython for interactive computing and data visualization. It introduces readers to Python programming, focusing on data analysis and numerical computing in the Jupyter environment. By integrating practical examples, the book helps readers enhance their computational skills and visualize data more effectively, making it a valued resource for beginners and experienced users alike.
  • Question: Do I need prior programming knowledge to understand this book?

    Answer: No, prior programming knowledge is not mandatory. The book is designed for individuals at various levels of expertise, including beginners. It provides foundational knowledge of Python, guiding readers through essential concepts step-by-step. Whether you're just starting your coding journey or looking to improve your data analysis skills, this resource is structured to support your learning effectively.
  • Question: What are some key features of IPython covered in the book?

    Answer: The book explores several key features of IPython, including its interactive shell, integration with Jupyter notebooks, and capabilities for real-time code execution and data visualization. Additionally, it emphasizes the importance of presenting data interactively, which enhances analytical workflows and makes data insights more accessible and easier to interpret, benefiting data scientists and analysts.
  • Question: How does this book support data visualization techniques?

    Answer: Learning IPython is rich in techniques for data visualization, providing readers with practical examples using libraries like Matplotlib and Seaborn. The focus is on creating compelling figures and charts that convey data insights effectively. By following the guides in this book, users can improve their ability to communicate findings visually, which is essential in data-driven decision-making.
  • Question: Is this book suitable for data analysts and scientists?

    Answer: Absolutely! This book is specifically tailored for data analysts and scientists who wish to leverage IPython and Jupyter for enhanced data processing and visualization. It equips users with necessary tools and techniques to analyze complex datasets, making it a vital asset for professionals seeking to elevate their analytical skills and capabilities in real-world applications.
  • Question: Does the book include practical examples?

    Answer: Yes, the book is loaded with practical examples illustrating how to apply Python in various scenarios using IPython. Through hands-on projects, readers can see how to manipulate data, deploy analytical methods, and visualize outcomes interactively. This incorporation of examples enables learners to grasp concepts better and apply them to their specific projects or interests.
  • Question: Can this book help in learning machine learning concepts?

    Answer: While the primary focus is on data analysis and visualization using IPython and Jupyter, the foundational knowledge gained from this book can pave the way to understanding machine learning concepts. The skills you develop for processing and visualizing data are essential when diving into machine learning, making this book a good stepping stone for those interested in advancing to that field.
  • Question: How does the Jupyter Notebook enhance learning in this context?

    Answer: Jupyter Notebook is a critical tool highlighted in this book, acting as an interactive interface that allows users to write and execute Python code in real time. This environment supports instant feedback and encourages experimentation, making it particularly useful for learning complex concepts in interactive computing and data analysis. It significantly enriches the educational experience and understanding.
  • Question: What skills will I gain from this book?

    Answer: By engaging with this book, you'll acquire a variety of skills, including data manipulation, critical analysis, and proficiency in data visualization using IPython and Jupyter. These skills are vital for making data-informed decisions in fields such as finance, healthcare, or technology. You'll also develop an understanding of how to present your findings effectively using visual formats.
  • Question: Where can I buy 'Learning IPython for Interactive Computing and Data Visualization' in Greece?

    Answer: You can purchase 'Learning IPython for Interactive Computing and Data Visualization: Started With Python for Data Analysis and Numerical Computing in the Jupyter' from Ubuy. They offer a user-friendly shopping experience, allowing you to explore various editions and formats of the book while providing strong customer support for your purchasing journey.

Python Editorial Review

This book on IPython for interactive computing and data visualization has received mixed reviews from customers. While some appreciate it as a good primer for learning Jupyter, IPython, and data science, others find it too short and basic, wanting more in-depth coverage of topics like pandas, numpy, and matplotlib. The book is described as a decent introduction, with concise examples and a tutorial approach, but some customers feel that later chapters are not as strong as the initial ones. Overall, opinions are divided on whether the book meets expectations, with some readers looking for a more comprehensive resource. **

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Pros

  • Good introduction to Jupyter, IPython, and data science
  • Covers a variety of topics
  • Includes a tutorial with a data set to follow along

Cons

  • Some chapters are not as strong

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Features & Benefits

  • students, teachers, researchers, engineers, analysts, journalists, hobbyists, data enthusiasts
  • Python basics in Jupyter Notebook
  • Data analysis and visualization with pandas, NumPy, matplotlib, seaborn
  • Numerical computations with Numba, Cython, ipyparallel
  • Newcomers to programming and data analysis, those familiar with other languages or analysis software, advanced readers with technical topics
  • No prior experience necessary, book covers all essential knowledge

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