What topics are covered in 'Data Science and Analytics with Python'?

This book provides an in-depth look into data science principles and analytics techniques using Python. Key topics include data manipulation, statistical analysis, machine learning, and data visualization. The book emphasizes practical approaches and includes real-world case studies, making it valuable for both beginners and seasoned practitioners. By combining theoretical foundations with hands-on coding examples, readers can apply the concepts directly to their projects, enhancing their data-driven decision-making skills.

Is this book suitable for beginners in data science?

Yes, 'Data Science and Analytics with Python' is quite suitable for beginners. It starts with fundamental concepts and gradually progresses to more complex topics, ensuring that readers build a strong foundation. The author presents clear explanations and illustrations that facilitate understanding. With practical examples and exercises, beginners can easily grasp essential data science techniques and start working on their own analytics projects confidently.

Which Python libraries are highlighted in the book?

The book emphasizes several popular Python libraries integral to data science and analytics, including Pandas for data manipulation, NumPy for numerical computations, Matplotlib and Seaborn for data visualization, and Scikit-learn for machine learning. Each library is explained in context with practical examples, which helps readers understand how to implement these tools in data analysis projects. Familiarity with these libraries can significantly enhance your data analysis capabilities.

How does this book address machine learning?

In 'Data Science and Analytics with Python', machine learning is tackled through practical implementation using Scikit-learn. The book covers various supervised and unsupervised learning algorithms, including regression, classification, and clustering techniques. Each algorithm is elucidated with case studies that highlight real-world applications. This practical approach enables readers to understand not only how to apply these techniques but also when to use them effectively in data science projects.

Does the book contain practical examples or case studies?

Absolutely! The book is rich with practical examples and case studies that reinforce the theoretical concepts discussed. Each chapter includes hands-on coding exercises and real-world scenarios, enabling readers to apply what they've learned. This practical approach is crucial for understanding how data science principles can be utilized in various fields, such as finance, healthcare, and marketing. By engaging with these case studies, readers can develop insights into solving actual data challenges.

Are there exercises or problems to solve in the book?

Yes, 'Data Science and Analytics with Python' includes a variety of exercises and problems at the end of each chapter. These exercises encourage readers to practice what they have learned and apply their knowledge practically. The problems range in difficulty, catering to both beginners and advanced users, which facilitates a gradual and thorough understanding of data science techniques. This hands-on practice is essential for reinforcing learning and mastering the content.

How can professionals benefit from this book?

Professionals in data-related fields can benefit significantly from 'Data Science and Analytics with Python'. The book offers advanced insights and detailed methodologies that can be directly applied in professional environments. With its focus on analytical techniques and real-world case studies, it enables professionals to enhance their data analysis skills, leading to better decision-making and strategic planning in their organizations. Additionally, the practical tips provided can help save time and improve efficiency in their data projects.

What programming background is required to understand this book?

A basic understanding of Python is recommended to fully benefit from 'Data Science and Analytics with Python'. Familiarity with programming concepts such as loops, functions, and data structures will be advantageous. However, the book caters to a broad audience, including those transitioning from other programming languages. With well-explained examples, even readers with minimal programming background can quickly grasp the concepts and start applying them effectively in data science scenarios.

Where can I buy the book 'Data Science and Analytics With Python'?

You can purchase 'Data Science and Analytics With Python' on Ubuy in Guinea-Bissau. Ubuy offers a variety of products, including academic books across several categories. Shopping through Ubuy allows you to access detailed product descriptions and user reviews, helping you make an informed decision. Whether you are a student, educator, or professional seeking to expand your knowledge, Ubuy is a reliable platform to acquire this essential resource for data science.

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Data Science and Analytics with Python (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy.

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Data Science and Analytics with Python (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

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Comprehensive Coverage

This book offers in-depth exploration of data science and analytics techniques using Python, covering essential algorithms and applications that empower readers to tackle real-world data challenges effectively.

Hands-On Approach

It includes practical examples and exercises that facilitate experiential learning, ensuring readers can apply theoretical concepts to hands-on data analysis and modeling tasks seamlessly.

Updated Content

The second edition reflects the latest advancements in the field, featuring updated tools and methodologies in data science, making it a relevant and valuable resource for both students and professionals.

Detalhes do produto

Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.

Publisher	Chapman and Hall/CRC
Publication date	June 2, 2025
Edition	2nd
Language	English
Print length	474 pages
ISBN-10	1032772492
ISBN-13	978-1032772493
Item Weight	2 pounds (910 grams)
Dimensions	7.5 x 1 x 9.25 inches (19.1 x 2.5 x 23.5 cm)

Quem Deverá Comprar?

Aspiring Data Scientists
Ideal for beginners seeking to learn practical data science methods using Python in real-world applications.
Academic Researchers
Useful for researchers who require a comprehensive guide to data analysis and statistical methodologies using Python.
Industry Professionals
Suitable for professionals looking to enhance their analytics skills and apply them in data-driven decision-making.

Complete Beginners
Not suitable for those with no prior programming or statistical knowledge as it assumes a foundational understanding.

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Expanded content reflecting the latest advancements in AI and Machine Learning.
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Addresses key issues of explainability, transparency, and fairness in AI.
Designed for both practitioners and academic use as a key textbook.
Adaptable format with chapters that can function as standalone references.
Offers practical insights for both beginner and advanced data scientists.

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