Python Data Science Essentials: Become an efficient data science practitioner by thoroughly understanding the key concepts of Python
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Python Data Science Essentials: Become an efficient data science practitioner by thoroughly understanding the key concepts of Python
Become an efficient data science practitioner by thoroughly understanding the key concepts of Python.
Python Data Science Essentials: Become an efficient data science practitioner by thoroughly understanding the key concepts of Python
Numéro d'article: 101481038

Python Data Science Essentials: Become an efficient data science practitioner by thoroughly understanding the key concepts of Python

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Numéro d'article: 101481038

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Ce qui se démarque

Comprehensive Learning
Covers essential Python concepts in data science, ensuring a well-rounded understanding for beginners and practitioners alike.
Practical Applications
Includes real-world examples and projects that allow users to apply Python skills directly to data science challenges.
Accessible Format
Available in Kindle edition, making it easy to read anytime, anywhere, enhancing the learning experience for busy professionals.

Détails du produit

Shop Python Data Science Essentials: Become an efficient data science practitioner by thoroughly understanding the key concepts of Python online at a best price in Haiti. 1785280422
  • Key FeaturesQuickly get familiar with data science using PythonSave time - and effort - with all the essential tools explainedCreate effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experienceBook DescriptionThe book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results.In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.What you will learnSet up your data science toolbox using a Python scientific environment on Windows, Mac, and LinuxGet data ready for your data science projectManipulate, fix, and explore data in order to solve data science problemsSet up an experimental pipeline to test your data science hypothesisChoose the most effective and scalable learning algorithm for your data science tasksOptimize your machine learning models to get the best performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataAbout the AuthorsAlberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges involving natural language processing (NLP), machine learning, and probabilistic graph models everyday.Luca Massaron is a data scientist and marketing research director who specializes in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience in solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms.Table of ContentsFirst StepsData MungingThe Data Science PipelineMachine LearningSocial Network AnalysisVisualization
Publisher Packt Publishing
Publication date April 30, 2015
Language English
Print length 216 pages
ISBN-10 1785280422
ISBN-13 978-1785280429
Item Weight 1.05 pounds (480 grams)
Dimensions 7.5 x 0.59 x 9.25 inches (19.1 x 1.5 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Beginner Data Scientists

    Those new to data science will find structured guidance on Python essential concepts beneficial for foundational learning.

  • Professionals Switching Careers

    Individuals transitioning into data science will appreciate a focused, efficient approach to grasping Python's crucial aspects quickly.

  • Self-learners

    Independent learners aiming to acquire practical Python skills will benefit from the comprehensive, clear explanations and examples.

Not Suitable For
  • Experienced Developers

    Advanced programmers may find the content too basic and lacking depth in complex data science topics or techniques.

DESCRIPTION DU PRODUIT

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What prior knowledge do I need to start this book?

    répondre: Basic understanding of programming concepts in Python will be helpful.
  • question: Is this book suitable for beginners in data science?

    répondre: Yes, it is designed for both beginners and those looking to enhance their skills.
  • question: What operating systems does this book cover for setting up the toolbox?

    répondre: The book covers setup instructions for Windows, Mac, and Linux.

Python Editorial Review

**** "Python Data Science Essentials" has garnered a mixed reception among customers, showcasing the complexity of catering to both novices and experienced practitioners within the data science domain. Several reviewers highlight the book's effectiveness in providing a practical guide for those looking to quickly familiarize themselves with Python's capabilities in data analysis. For experienced engineers transitioning to data science, the book serves as a helpful reference, offering a streamlined approach to handling common tasks and clarifying the essentials of the Python ecosystem. Many find the writing style easy to digest, emphasizing a logical flow that mirrors real-world applications. The instructional nature allows readers to navigate from installations to advanced topics like machine learning algorithms with relative ease. The author’s focus on essential Python packages like pandas and scikit-learn is praised for its applicability in today's data challenges, especially with insights into data loading techniques and model performance evaluation. However, the book is not without its drawbacks. Some readers have encountered programming errors early on, which can detract from the overall learning experience. The lack of updates to reflect changes in tools such as Jupyter Notebooks has also raised concerns. While some critiques point to its limited depth compared to more comprehensive texts, others appreciate this as a concise refresher rather than a thorough introduction. In summary, "Python Data Science Essentials" strikes a chord for its practical approach and clarity, making it suitable for readers familiar with Python, though improvements in example accuracy and updates could significantly enhance its value. **

Avis et évaluations clients

9 évaluations des clients
  • 5 étoile
    45%
  • 4 étoile
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  • 3 étoile
    28%
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Avantages

  • Concise and practical guide to essential data science concepts in Python.
  • Easy-to-understand writing style suitable for both novices and experienced users.
  • Logical structure mirroring real-world applications.
  • Covers crucial packages like pandas and scikit-learn effectively.
  • Helpful for quick learning and refreshing knowledge.

Les inconvénients

  • Presence of programming errors in early chapters may hinder learning.

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Caractéristiques et avantages

  • Gain a strong foundation in data science with Python.
  • Quickly learn essential tools to save time and effort.
  • Create effective data science projects with practical examples.
  • Master data munging and preprocessing techniques.
  • Explore key machine learning algorithms and visualization tools.
  • Develop skills for optimizing models and analyzing data effectively.

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