Lambar abu:
184481678
Python for Data Science: A Hands-On Introduction
87% of respondents would recommend this to a friend
Lambar abu:
184481678
XAF 36104
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Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities.
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What Stands Out
Hands-On Learning
This book emphasizes practical experience, allowing readers to apply Python concepts directly to real data science problems, enhancing comprehension and retention.
Comprehensive Coverage
It covers a wide range of data science topics, including data manipulation, visualization, and machine learning, making it a well-rounded resource for beginners and intermediates alike.
User-Friendly Approach
Designed for readers with varying skill levels, it simplifies complex concepts with clear explanations and examples, ensuring accessibility for anyone looking to delve into data science.
Bayanin samfur
- Comprehensive guide to using Python for data science
- Covers essential concepts and hands-on techniques
- Provides practical examples and exercises
- Written in a clear and accessible language
- Suitable for beginners and those with prior Python knowledge
- Ideal for individuals and professionals seeking to delve into data science
| Yayıncı | No Starch Press |
| Yayınlanma Tarihi | 2 Ağustos 2022 |
| Baskı | 1. |
| Dil | İngilizce |
| Baskı Uzunluğu | 240 sayfa |
| ISBN-10 | 1718502206 |
| ISBN-13 | 978-1718502208 |
| Boyutlar | 17.9 x 1.8 x 23.5 cm |
Who Should Buy?
-
Beginner Programmers
Ideal for individuals new to programming who want to learn Python in a practical, hands-on manner.
-
Data Science Enthusiasts
Perfect for those interested in data science concepts and wishing to apply Python to data analysis tasks.
-
Students in STEM
Great for students pursuing STEM fields, providing foundational knowledge in Python for future coursework.
-
Advanced Developers
Not suitable for seasoned programmers looking for advanced Python techniques or specialized data science methods.
KWATANCEN ABUN
Kuna da wata tambaya? Yi taɗi da mu
Tambayoyi & Amsoshi Abokin Ciniki
-
Tambaya:
What is Python?
Amsa: Python is an interpreted, high-level, general-purpose programming language. -
Tambaya:
Is Python good for data science?
Amsa: Yes, Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. -
Tambaya:
What are some real-world data applications covered?
Amsa: Real-world data applications include using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
İngilizce Baskı Yuli Vasiliev Format: Kağıt Kapak Editorial Review
Python for Data Science: A Hands-On Introduction is a comprehensive guide published by No Starch Press that was released on August 2, 2022. Spanning 240 pages, this book is written in English and aims to provide practical knowledge for effectively utilizing Python in data science applications. With a focus on hands-on learning, readers can expect to engage with practical examples that enhance their understanding of the subject. This book is suitable for beginners who wish to dive into data science as it takes a straightforward approach, making complex concepts accessible and manageable.
Customer Reviews & Ratings
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Ribobi
- Hands-on approach enhances practical learning
- Suitable for beginners in data science
- Comprehensive coverage of Python basics
- Published by a reputable publisher
- Clear examples simplify complex concepts
Fursunoni
- No advanced topics covered for experienced users
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XAF 36104
Yi Oda yanzu, a samu nan da Sunday, Satumba 13
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Fasaloli & Fa'idoji
- Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds.
- Write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques
- Discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science
- Load data in various formats, streamline, group, and aggregate data sets, and create charts, maps, and visualizations
- Real-world data applications including Taxi service, market basket analysis, and machine learning to predict stock prices
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