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Pythonデータサイエンスハンドブック 第2版
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MOP 369
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A must-have practical reference for mastering Python data science tools.
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產品詳情
- Pythonのデータサイエンス用のツールを使いこなすための実用的な情報が詰め込まれたリファレンスの待望の改訂版です。IPythonとJupyter、NumPy、pandas、Matplotlib、scikit-learnを利用し、データの操作、可視化、行列計算、時系列解析、統計分析、機械学習、データモデルの構築、複雑な科学計算まで、幅広いトピックをカバー。それぞれのトピックについて、押さえておくべき基本、tips、便利なコマンドなどを紹介します。Pythonでデータ処理を行う人にとってはいつも手元に置いておきたい「使える」一冊です。
| Publisher | オライリー・ジャパン |
| 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?
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Data Science Beginners
Ideal for those new to data science, offering comprehensive introductions to essential libraries and concepts.
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Experienced Programmers
Select programming professionals will appreciate advanced techniques for applying Python in data analysis and machine learning.
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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.
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Jake VanderPlas Programming Editorial Review
Python Data Science Handbook 2nd Edition is a comprehensive guide published on February 17, 2024, designed for those looking to delve into data analysis using tools like Jupyter, NumPy, pandas, and Matplotlib. This handbook spans 576 pages, offering extensive insights into scikit-learn for machine learning, making it an invaluable resource for practitioners and learners alike. Written in Japanese, it serves both novice and experienced data scientists aiming to enhance their skills in data manipulation and visualization. The book’s hands-on approach is frequently highlighted, making complex concepts accessible, which greatly aids in mastering data science techniques.
Customer Reviews & Ratings
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優點
- Comprehensive coverage of key data science tools
- Practical insights into machine learning applications
- Hands-on approach enhances learning experience
- Suitable for both beginners and advanced users
- Well-organized layout for easy navigation
缺點
- Some readers may prefer an English version
Product Price History
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MOP 369
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特色和優勢
- 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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