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Python for Data Science: A Hands-On Introduction
Python for Data Science: A Hands-On Introduction
Python for Data Science: A Hands-On Introduction
Python for Data Science: A Hands-On Introduction
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Python for Data Science: A Hands-On Introduction
Python for Data Science: A Hands-On Introduction
Python for Data Science: A Hands-On Introduction
Python for Data Science: A Hands-On Introduction
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.
Python for Data Science: A Hands-On Introduction
Item #: 52380517

Python for Data Science: A Hands-On Introduction

88% of respondents would recommend this to a friend

Item #: 52380517

AUD 106

AUD 129

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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

Practical Learning
Engages users with hands-on projects that reinforce data science concepts, allowing learners to apply Python in real-world scenarios effectively.
Comprehensive Coverage
Covers essential data science tools and libraries, ensuring a strong foundational understanding for beginners while addressing common user challenges.
User-Friendly Format
Designed for easy navigation and understanding, making complex topics accessible and enjoyable for readers with varying experience levels.

Product Details

Shop Python for Data Science: A Hands-On Introduction online at a best price in Kiribati. 1718502206
  • 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
Publisher No Starch Press
Publication date August 2, 2022
Language English
Print length 240 pages
ISBN-10 1718502206
ISBN-13 978-1718502208
Item Weight 15.2 ounces (430.92 grams)
Dimensions 7.05 x 0.71 x 9.25 inches (17.9 x 1.8 x 23.5 cm)

Who Should Buy?

Suitable For
  • Beginners in Python

    Ideal for newcomers to programming who want to learn Python while focusing on data science applications.

  • Data Science Enthusiasts

    Perfect for individuals interested in data science who wish to gain practical experience with real-world datasets.

  • Professionals Upskilling

    Great for professionals looking to enhance their skill set in data analysis and Python programming for career advancement.

Not Suitable For
  • Experienced Programmers

    Not suitable for seasoned programmers who may find the content too basic and lacking in advanced topics.

Product Description

Have any Query? Chat with us

Customer Questions & Answers

  • Question: What skills will I gain from Python for Data Science A Hands-On Introduction?

    Answer: You will gain essential skills in data manipulation, analysis, and visualization using Python. This course introduces libraries such as Pandas, NumPy, and Matplotlib, allowing you to handle data effectively. It’s beneficial for aspiring data scientists and analysts and can help professionals in various fields leverage data-driven insights to make informed decisions.
  • Question: Is this book suitable for beginners in programming?

    Answer: Yes, this book is designed for beginners with no prior programming experience. It starts with the fundamentals of Python programming, gradually introducing data science concepts. This approach allows you to build a strong foundation, making it ideal for those starting their journey into data science.
  • Question: What topics are covered in this introduction to data science?

    Answer: The book covers a variety of topics, including basic Python programming, data visualization, exploratory data analysis, and statistical concepts fundamental to data science. Each chapter provides hands-on projects that reinforce these concepts, enabling you to apply your learning in real-world scenarios.
  • Question: How practical is the content provided in the book?

    Answer: The content is highly practical, featuring hands-on exercises that help reinforce concepts through application. Real-world datasets are utilized to demonstrate data cleaning, analysis, and visualization techniques, making it an ideal resource for anyone looking to apply data science skills immediately.
  • Question: Do I need specific software or tools to follow this book?

    Answer: You will need Python installed on your computer, along with libraries such as Pandas, NumPy, and Matplotlib. The book guides you on how to set up your environment step-by-step. This ensures that you can follow along with the practical exercises and projects effortlessly.
  • Question: Can this book help me with data science job interviews?

    Answer: Absolutely, this book covers key concepts and techniques commonly discussed in data science job interviews. By mastering the topics and hands-on projects provided, you will enhance your understanding, giving you an edge during technical interviews and assessments.
  • Question: What makes this book different from other data science resources?

    Answer: This book stands out due to its hands-on approach, combining theory with practical exercises. While many resources focus purely on theoretical knowledge, this book helps you apply what you learn, bridging the gap between reading and real-world application, which is essential for becoming proficient in data science.
  • Question: How long does it take to complete the book?

    Answer: The completion time varies per individual, but on average, readers can finish the book in several weeks if they dedicate a few hours a week. This pace allows you to absorb the material thoroughly and practice effectively, ensuring you grasp the core concepts of data science.
  • Question: What level of mathematical knowledge do I need?

    Answer: Basic knowledge of statistics and algebra is beneficial but not mandatory. The book introduces necessary mathematical concepts in a beginner-friendly manner. You will learn how these concepts apply within Python and data science, making it accessible for those without an extensive math background.
  • Question: Where can I buy Python For Data Science A Hands-On Introduction?

    Answer: You can buy 'Python For Data Science A Hands-On Introduction' from Ubuy in Kiribati. Ubuy offers a variety of formats, making it easy for you to choose the one that best fits your learning preferences.

Data Mining Editorial Review

  • ubuy Kiribati

Python for Data Science: A Hands-On Introduction offers a mixed experience with an emphasis on practical coding although it presents some challenges. Published by No Starch Press in August 2022, the book spans 240 pages and attempts to guide beginners through Python and data science concepts. Several reviewers highlight the lack of adequate support resources such as practice solutions on GitHub, which can impede the learning process. Additionally, the book’s structure is criticized for abruptly introducing complex coding concepts without sufficient explanation, making it potentially overwhelming for those new to programming. Despite its intention to incorporate statistical techniques, crucial topics like handling real data and common statistical methods are notably absent, leading readers to seek alternative materials for a more thorough understanding.

Customer Reviews & Ratings

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Pros

  • Well-structured for hands-on coding experience
  • Publisher has a strong reputation
  • Appealing print length for concise learning
  • Interesting topics in data science
  • Focus on practical applications is beneficial

Cons

  • Missing supplementary materials can hinder understanding

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Features & Benefits

  • 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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