Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data ... (Graph & Big Data Analytics Applied Path)
genuine image
Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data ... (Graph & Big Data Analytics Applied Path)
Unlock new, actionable insights from your data with Graph Data Science with Python and Neo4j.
Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data ... (Graph & Big Data Analytics Applied Path)
商品＃: 101196647

Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data ...

商品＃: 101196647

JPY 6186

Price Details

Excluding Shipping & Custom charges ( Shipping and custom charges will be calculated on checkout )

*All items will import from アメリカ

在庫あり
アメリカ USA ストアからの輸入品
今すぐ注文すると 頃に届きます 水曜日, 7月 08
安全な取引安全な取引

Ubuyはお客様のセキュリティとプライバシーの保護に努めています。当社の高度な決済セキュリティシステムは、AES（高度暗号化標準）およびSSL（セキュアソケットレイヤー）プロトコルを使用して送信中の情報を暗号化することで機密性を確保しています。お客様の決済情報は第三者の販売者と共有されることはなく、100%安全に保護されています。

最高の物流パートナー
  • fedex
  • dhl
Unlock new, actionable insights from your data with Graph Data Science with Python and Neo4j.
U-Care保証：
なし
プランを選択してください
fast shipping

Fast
Shipping

free return

Free
Return*

安全な梱包

安全な梱包

100％本物

100％本物

pci-dss

PCI DSS 準拠

iso certified

ISO 27001 保証

paypal payment
visa payment
mastercard payment
american express payment
jcb payment

この商品の利点

Comprehensive Learning
Offers hands-on projects that teach graph data science concepts using Python and Neo4j, enabling learners to visualize and analyze complex data relationships effectively.
Real-World Application
Focuses on practical applications of graph data science in enterprise strategies, making the skills acquired highly relevant and valuable for professionals in various industries.
Integrated Approach
Seamlessly integrates Python with Neo4j, providing readers actionable insights into data processing and enhancing their ability to tackle complex analytical problems using a modern toolkit.

製品詳細

Shop Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data ... (Graph & Big Data Analytics Applied Path) online at a best price in 日本. 8197081964
Item Weight1 lbs (450 grams)

どんな人にお勧めですか？

Suitable For
  • Data Scientists

    Ideal for data scientists looking to enhance skills in graph data analysis using Python and Neo4j.

  • Data Analysts

    Beneficial for analysts wishing to visualize and manipulate complex datasets with graph structures.

  • Developers

    Great for developers interested in integrating graph databases into applications for data-rich environments.

Not Suitable For
  • Beginners

    Not suitable for beginners without prior knowledge of Python or database management concepts.

  • Non-Technical Users

    May not benefit non-technical users who lack experience in programming or data science principles.

  • Casual Learners

    Not ideal for individuals seeking light, introductory content rather than hands-on, project-based learning.

製品説明書

Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data ... (Graph & Big Data Analytics Applied Path)

何か質問はありますか？ おしゃべりしましょう

顧客の質問と回答

  • 質問: What is the main focus of the book 'Graph Data Science with Python and Neo4j'?

    答え: The primary focus of this book is to teach readers how to integrate Python with Neo4j for data visualization and analysis specifically within the realm of graph data science. By engaging with hands-on projects, readers can grasp complex concepts through practical applications. This approach not only enhances understanding but also gives you the skills to implement graph algorithms and create visualizations that reveal insightful patterns in your data.
  • 質問: Who is the target audience for this book?

    答え: This book is designed for data scientists, software developers, students, and analytics professionals interested in leveraging graph databases and data science techniques. It caters to individuals with a basic understanding of Python and statistics and offers a structured approach to applying these skills in real-world scenarios. By focusing on hands-on projects, the book helps bridge the gap between theory and practice, making it ideal for anyone looking to deepen their knowledge in graph data science.
  • 質問: What programming skills do I need to start this book?

    答え: To effectively engage with 'Graph Data Science with Python and Neo4j', you should have a foundational understanding of Python programming. Familiarity with basic data manipulation and visualization using libraries such as Pandas and Matplotlib is also beneficial. These skills will allow you to fully participate in the hands-on projects found within the book. As you progress, you will also learn how to utilize Neo4j's query language, Cypher, expanding your skill set even further.
  • 質問: What types of projects can I expect to work on?

    答え: The book includes a range of hands-on projects that cover various applications of graph data science. These may include social network analysis, recommendation systems, fraud detection, and more. Each project is designed to provide a practical context for applying the theoretical concepts discussed in the text. By working through these projects, you’ll gain valuable experience in using graph databases for real-world data challenges.
  • 質問: How does this book integrate Python and Neo4j?

    答え: The integration of Python and Neo4j in this book is facilitated through detailed examples and practical exercises that demonstrate how to use the Neo4j Python Driver in data science projects. You will learn how to leverage Python for data manipulation, visualization, and running queries in Neo4j. This seamless integration allows you to build powerful applications that can analyze and visualize complex data sets effectively.
  • 質問: Does this book cover data visualization techniques?

    答え: Yes, the book thoroughly covers data visualization techniques tailored for graph data. Through hands-on projects, you’ll learn to create visual representations of graph data, helping you to uncover insights that traditional tabular data formats may obscure. By understanding how to visualize data effectively, you will enhance your storytelling capabilities, allowing stakeholders to grasp complex data relationships at a glance.
  • 質問: Can this book help in preparing for data science interviews?

    答え: Absolutely! 'Graph Data Science with Python and Neo4j' equips you with practical, hands-on experience that can enhance your resume and interview preparation for data science roles. Familiarity with graph databases and the ability to analyze complex datasets are increasingly sought-after skills. By working through the projects, you will build a portfolio of tangible work that demonstrates your problem-solving skills in data science, which is valuable during an interview.
  • 質問: What prerequisites should I have before starting this book?

    答え: Before diving into 'Graph Data Science with Python and Neo4j', you should have a basic understanding of Python programming, some familiarity with data analysis concepts, and an interest in graph theory. While the book starts with introductory content, having these skills will help you follow along more comfortably. Additionally, understanding statistical basics can enhance your ability to interpret data insights as you progress through the topics.
  • 質問: What makes this book different from other data science books?

    答え: The distinct feature of 'Graph Data Science with Python and Neo4j' is its specific focus on graph data science, an area that many data science books overlook. The combination of practical projects, theoretical discussions, and the integration of two powerful tools—Python and Neo4j—provides a unique learning experience. This specialization allows readers to explore graph databases in-depth, making it a valuable resource for those specifically interested in this field.
  • 質問: Where can I buy 'Graph Data Science with Python and Neo4j'?

    答え: You can purchase 'Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data ...' from Ubuy in Japan. Ubuy offers a variety of options for obtaining this book, making it easier for customers in Japan to access valuable resources for their learning and professional development. Explore Ubuy’s platform for additional details and purchasing options.

Structured Design Editorial Review

編集レビューが見つかりませんでした

お客様のレビュー＆評価

4.0
1 カスタマー評価
  • 5 星
    0%
  • 4 星
    100%
  • 3 星
    0%
  • 2 星
    0%
  • 1 星
    0%

この商品のレビュー

お考えをお聞かせください

長所

  • Engaging hands-on projects
  • Great for beginners and experts
  • Excellent use of real-world examples
  • Strong focus on data visualization
  • Clear and concise explanations

短所

  • Some projects may require prior knowledge.

Product Price History

重要な情報

  • 注意：国際運送の商品に関して、製造会社保障は無効になる可能性、アフターサービスは受けれない可能性、取説や安全情報は発送先の言語になっていない可能性ある。商品とその付属品は配送先の国の規格、仕様、ラベル表示法などに適応していない可能性があります。また、配送先の国の電力企画に適応しいない（アダプタや変換器を必要とする）可能性があります。ご注文の商品は配送先の国に輸入することは合法なのかを確認するのは購入者の責任になります。Ubuyからご購入の際、受け取り者は正式な輸入者となり、配送先の全ての法律やルールに遵守する必要があります。
  • Ubuyはグローバル検索エンジンのためリストにある全ての商品が購入できないことがあります。商品は輸出規制、貿易規制があります。

JPY 6186

今すぐ注文すると 頃に届きます 水曜日, 7月 08

QTY:

安全な取引安全な取引

Ubuyはお客様のセキュリティとプライバシーの保護に努めています。当社の高度な決済セキュリティシステムは、AES（高度暗号化標準）およびSSL（セキュアソケットレイヤー）プロトコルを使用して送信中の情報を暗号化することで機密性を確保しています。お客様の決済情報は第三者の販売者と共有されることはなく、100%安全に保護されています。

最高の物流パートナー
  • fedex
  • dhl
最速の国際配送

特徴と利点

  • Comprehensive guide integrating Python and Neo4j for graph data science.
  • Hands-on approach to solving real-world challenges with interconnected data.
  • Focus on practical application with detailed explanations and examples.
  • Explore advanced analytics and machine learning techniques.
  • Utilizes cutting-edge integrations with Large Language Models like ChatGPT.
  • Includes access to a dedicated GitHub repository for code examples.