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- Python Data Science Essentials: Become an eff...
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Nº de artículo:
101481038
Python Data Science Essentials: Become an efficient data science practitioner by thoroughly understanding the key concepts of Python
75% of respondents would recommend this to a friend
Nº de artículo:
101481038
COP 171536
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Become an efficient data science practitioner by thoroughly understanding the key concepts of Python.
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What Stands Out
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.
Detalles de producto
| Publisher | Packt Publishing |
| Publication date | April 30, 2015 |
| Language | English |
| File size | 3.7 MB |
| Screen Reader | Supported |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 260 pages |
| ISBN-13 | 978-1785287893 |
| Page Flip | Enabled |
| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
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Beginner Data Scientists
Those new to data science will find structured guidance on Python essential concepts beneficial for foundational learning.
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Professionals Switching Careers
Individuals transitioning into data science will appreciate a focused, efficient approach to grasping Python's crucial aspects quickly.
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Self-learners
Independent learners aiming to acquire practical Python skills will benefit from the comprehensive, clear explanations and examples.
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Experienced Developers
Advanced programmers may find the content too basic and lacking depth in complex data science topics or techniques.
DESCRIPCIÓN DEL PRODUCTO
Python Data Science Essentials: Become an efficient data science practitioner by thoroughly understanding the key concepts of Python
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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. **
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ventajas
- 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.
Contras
- Presence of programming errors in early chapters may hinder learning.
Product Price History
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características y beneficios
- 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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