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87953333
Python Data Science Handbook 2nd Edition ―Jupyter, NumPy, pandas, Matplotlib, data analysis using scikit-learn, machine learning
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Item #:
87953333
SAR 165
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A must-have practical reference for mastering Python data science tools.
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What Stands Out
Comprehensive Coverage
Covers essential tools like Jupyter, NumPy, and pandas, providing a thorough grounding in data science methods and applications for both beginners and experienced practitioners.
Hands-On Approach
Offers practical examples and exercises, allowing readers to apply concepts in real-time, enhancing learning and reinforcing data analysis skills effectively.
Updated Content
Second edition features updated libraries and techniques ensuring users stay relevant in the fast-evolving field of data science and machine learning.
Product Details
| Publisher | u30aau30e9u30a4u30eau30fcu30fbu30b8u30e3u30d1u30f3 |
| Publication date | February 17, 2024 |
| Language | Japanese |
| Print length | 576 pages |
| ISBN-10 | 4814400632 |
| ISBN-13 | 978-4814400638 |
| Item Weight | 1 g |
| Dimensions | 9.45 x 7.48 x 1.38 inches (24 x 19 x 3.5 cm) |
Who Should Buy?
-
Data Science Beginners
Ideal for those new to data science, offering comprehensive introductions to essential libraries and concepts.
-
Experienced Programmers
Select programming professionals will appreciate advanced techniques for applying Python in data analysis and machine learning.
-
Academic Researchers
Researchers seeking a practical guide for implementing data analysis tools and methodologies in their studies will benefit.
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Absolute Beginners
Individuals with no programming background may find the concepts and technical language challenging and difficult to comprehend.
Product Description
Python Data Science Handbook 2nd Edition ―Jupyter, NumPy, pandas, Matplotlib, data analysis using scikit-learn, machine learning
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Features & Benefits
- Comprehensive guide covering essential Python data science tools.
- Covers Jupyter, NumPy, pandas, Matplotlib, and scikit-learn.
- Includes tips and convenient commands for efficient data analysis.
- Ideal for handling data manipulation, visualization, and machine learning.
- Great for beginners and experienced data scientists alike.
- Always keep this invaluable resource at hand for your data projects.
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