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Item #:
20694326
Data Science with Python and Dask
89% of respondents would recommend this to a friend
Item #:
20694326
AUD 59
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Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets.
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What Stands Out
Scalable Computing
Dask enables seamless scaling of computations, handling large datasets efficiently across multiple cores and clusters, ensuring optimal performance for data-intensive tasks.
Integrated Ecosystem
Integrates with popular Python libraries like Pandas and NumPy, allowing users to leverage familiar tools while enhancing capabilities for big data processing and analysis.
Practical Insights
Offers hands-on examples and real-world applications, guiding users through data science concepts effectively, ensuring they gain practical skills for immediate application in their projects.
Product Details
- Learn to use Dask, a native parallel analytics tool for Python
- Dask integrates with libraries like Pandas, NumPy, and Scikit-Learn for working with big datasets
- Includes a free eBook in PDF, Kindle, and ePub formats
- Dask enables parallel computing and intuitive workflows for analyzing large datasets
- Teaches building scalable projects, machine learning models using Dask-ML, visualization, and deploying Dask apps
- Targeted at data scientists and developers experienced with Python and the PyData stack
| Publisher | Manning |
| Publication date | July 30, 2019 |
| Edition | First Edition |
| Language | English |
| Print length | 296 pages |
| ISBN-10 | 1617295604 |
| ISBN-13 | 978-1617295607 |
| Item Weight | 1.2 pounds (540 grams) |
| Dimensions | 7.38 x 0.6 x 9.25 inches (18.7 x 1.5 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners wanting to learn data analysis using Python and Dask for efficient computation.
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Python Developers
Useful for developers familiar with Python looking to leverage Dask for scalable data processing tasks.
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Data Analysts
Great for analysts needing to handle large datasets and perform complex calculations without resource bottlenecks.
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Complete Beginners
Not suitable for those with no programming experience; prior knowledge of Python is essential for understanding.
Product Description
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Customer Questions & Answers
-
Question:
What is 'Data Science with Python and Dask First Edition' about?
Answer: 'Data Science with Python and Dask First Edition' focuses on leveraging Python for data science tasks by utilizing Dask, a parallel computing library that enables efficient handling of large datasets. The book covers various data manipulation, analysis, and machine learning techniques to help practitioners process data more effectively. This resource is beneficial for both beginners and experienced data scientists looking to enhance their skills with scalable data processing workflows. -
Question:
Who is the target audience for this book?
Answer: This book caters to data scientists, analysts, and Python developers who want to dive into scalable data science practices. It's suited for readers with a basic understanding of Python who wish to learn how to perform data analysis on large datasets using Dask. Whether you are in academia or industry, this book provides practical insights and examples to aid in real-world applications of data science. -
Question:
What are the key features of Dask that are highlighted in the book?
Answer: 'Data Science with Python and Dask First Edition' highlights Dask's ability to parallelize computations across multiple cores and distributed systems. Key features include its compatibility with existing Python libraries like NumPy and Pandas, lazy evaluation for improved performance, and dynamic task scheduling. These features allow data scientists to handle datasets that exceed memory capacity and perform computations efficiently, making it essential for modern data-driven projects. -
Question:
What topics are covered in the book?
Answer: The book covers a broad range of topics including data manipulation with Dask DataFrames, working with large arrays, parallel algorithms, and integrating Dask with machine learning pipelines. Additionally, it discusses performance optimization techniques and best practices for building scalable data workflows. This diverse coverage ensures that readers can tackle various data science challenges and understand how to apply Dask effectively. -
Question:
Is prior knowledge of data science required to understand the book?
Answer: While prior knowledge of data science is beneficial, 'Data Science with Python and Dask First Edition' is designed to be accessible to readers who have a foundational understanding of Python programming. The book starts with the basics of data manipulation and gradually introduces advanced concepts, making it a valuable resource for learners at various levels. Thus, it offers a great entry point for those looking to enter the field of data science. -
Question:
Can Dask be used for real-time data processing?
Answer: Yes, Dask can be effectively used for real-time data processing as it supports dynamic task scheduling and can handle streaming data scenarios. This feature allows data scientists to manage and analyze data as it is generated, making it ideal for applications that require immediate insights, such as monitoring systems and online recommendation engines. The book explores such use cases, guiding readers on how to implement these capabilities in their projects. -
Question:
How does Dask compare to other data processing libraries?
Answer: Dask is often compared to libraries like Apache Spark and large-scale tools but stands out for its seamless integration with familiar Python data libraries like Pandas and NumPy. It allows users to write code in a more Pythonic way while still enabling scalable performance. This makes it an attractive option for Python users looking to scale up their data processing without learning a new ecosystem. The book provides insights into benchmarking Dask against other libraries to understand when to use it. -
Question:
What skills will I learn from reading this book?
Answer: Readers of 'Data Science with Python and Dask First Edition' will gain skills in scalable data manipulation and analysis using Dask. Practical scenarios illustrate how to implement parallel computing, optimize tasks, and manage large datasets effectively. By the end of the book, you'll have a strong foundation in building data pipelines that efficiently process large volumes of information, enhancing your preparedness for real-world data scenarios. -
Question:
Are there any code examples provided in the book?
Answer: Absolutely, the book includes numerous code examples that demonstrate how to use Dask for various data science tasks. These examples are practical and designed to give readers hands-on experience with Dask's capabilities. Additionally, the authors provide explanations of the code, allowing readers to understand the reasoning behind each step, which is invaluable for learning and applying the concepts discussed throughout the book. -
Question:
Where can I buy 'Data Science with Python and Dask First Edition'?
Answer: 'Data Science with Python and Dask First Edition' is available for purchase at Ubuy. This online platform offers a user-friendly shopping experience, with a range of options to access the book conveniently within Kiribati. Ubuy is well-known for its extensive collection of titles on programming and data science, making it a reliable place to find this publication and more.
Data Modeling & Design Editorial Review
"Data Science with Python and Dask First Edition" is a comprehensive and practical guide for anyone interested in leveraging Dask for data science projects. The book provides clear explanations, relevant examples, and up-to-date information on the topic. It is well-organized and caters to both beginners and experienced practitioners in the field. The hands-on approach and case studies make it an invaluable resource for learning and implementing Dask in real-world scenarios.
Customer Reviews & Ratings
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Pros
- Clear explanations and practical examples
- Up-to-date information on data science with Python and Dask
- Hands-on approach with case studies
Cons
- Some readers may prefer more in-depth technical discussions
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Features & Benefits
- Learn to use Dask for data projects without changing the way you work
- Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas and Scikit-learn
- Teaches you to create machine learning models using Dask-ML
- Build interactive visualizations and clusters using AWS and Docker
- Ideal for data scientists and developers with experience using Python and the PyData stack
- Scala your code from a single laptop to a cluster of hundreds of machines with ease
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