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Ultimate Python Libraries for Data Analysis and Visualization: Leverage Pandas, NumPy, Matplotlib, Seaborn, Julius AI and No-Code Tools for Data ...
R$ 329
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Unlock the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI.
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Detalhes do produto
| Publisher | Orange Education Pvt Ltd |
| Publication date | 4 April 2024 |
| Language | English |
| Print length | 267 pages |
| ISBN-10 | 8197081913 |
| ISBN-13 | 978-8197081910 |
| Item weight | 463 g |
| Dimensions | 19.05 x 1.55 x 23.5 cm |
| Book 2 of 2 | Data Analyst (Python) u2014 Expert Micro Path |
Para quem é indicado?
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Data Analysts
Ideal for data analysts needing comprehensive tools for manipulating datasets and generating insightful visualizations.
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Data Science Students
Perfect for students learning data science who want practical guidance on applying popular Python libraries.
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No-Code Enthusiasts
Great for users interested in data analysis without coding experience, offering no-code tools and clear instructions.
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Absolute Beginners
Not suitable for complete beginners to programming who lack foundational knowledge of Python and libraries.
DESCRIÇÃO DO PRODUTO
Ultimate Python Libraries for Data Analysis and Visualization: Leverage Pandas, NumPy, Matplotlib, Seaborn, Julius AI and No-Code Tools for Data ... Data Analyst Python — Expert Micro Path Paperback – 4 April 2024
Perguntas e Respostas do cliente
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pergunta:
What programming language does this book focus on?
responda: The book primarily focuses on Python for data analysis and visualization. -
pergunta:
Does this book include practical examples?
responda: Yes, it includes hands-on projects and real-world datasets for practical application. -
pergunta:
Is there a reference guide included?
responda: Yes, a Python cheat sheet is included in the appendix for quick reference.
Data Mining Editorial Review
**** "Ultimate Python Libraries for Data Analysis and Visualization" is an insightful guide that promises to unlock the potential of Python's powerful libraries such as Pandas, NumPy, Matplotlib, Seaborn, and the innovative Julius AI, along with no-code tools. This book serves both beginners and those with some prior knowledge of Python, offering a balance between theoretical understanding and practical application. The book excels in its comprehensive coverage of essential libraries, making complex concepts accessible through clear and concise explanations paired with practical examples. This hands-on approach not only helps abstract concepts come to life but also allows readers to immediately apply what they learn to their own projects. The inclusion of statistical analysis is particularly noteworthy as it simplifies a typically daunting subject, integrating it seamlessly within the context of data analysis. However, some readers expressed frustrations with aspects of the execution. Critiques were directed at the quality of code provided via the GitHub repository, which some found poor and outdated. Additionally, the book's rapid pace can be overwhelming for novices seeking in-depth explanations, causing some to feel that it lacks sufficient detail. Despite these drawbacks, the book is recognized for its valuable insights and practical focus, making it an excellent resource for aspiring data scientists and professionals alike. Its unique inclusion of contemporary tools such as Julius AI sets it apart, appealing to those keen on enhancing their productivity within the evolving data landscape. In summary, "Ultimate Python Libraries for Data Analysis and Visualization" is a commendable toolkit for mastering data analysis with Python, balancing theory and practical application, but it may leave depth-seeking readers wanting more. **
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Prós
- Comprehensive coverage of essential Python libraries for data analysis and visualization.
- Clear explanations and practical examples make complex concepts accessible.
- Hands-on approach encourages immediate application to real-world projects.
- Inclusion of Julius AI and no-code tools enhances productivity and appeal.
- Suitable for beginners and experienced users alike, providing a strong foundation.
Contras
- Some code examples from the GitHub repo are poor quality and outdated.
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Características e benefícios
- Master data analysis and visualization with Python.
- Comprehensive guide covering popular libraries like Pandas and NumPy.
- Step-by-step tutorials for statistical analysis and time series forecasting.
- Hands-on projects for analyzing real-world datasets across various industries.
- Includes a Python cheat sheet for quick reference.
- Gain confidence to tackle any data analysis challenge.
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