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)
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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)
Preces #: 101196647

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

Preces #: 101196647

€ 35

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What Stands Out

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.

Produkta informācija

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 Latvia. 8197081964
Item Weight1 lbs (450 grams)

Who Should Buy?

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.

PRODUKTA APRAKSTS

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)

Vai jums ir kādi jautājumi? Terzet ar mums

Klientu jautājumi un atbildes

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

    atbildi: 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.
  • jautājums: Who is the target audience for this book?

    atbildi: 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.
  • jautājums: What programming skills do I need to start this book?

    atbildi: 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.
  • jautājums: What types of projects can I expect to work on?

    atbildi: 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.
  • jautājums: How does this book integrate Python and Neo4j?

    atbildi: 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.
  • jautājums: Does this book cover data visualization techniques?

    atbildi: 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.
  • jautājums: Can this book help in preparing for data science interviews?

    atbildi: 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.
  • jautājums: What prerequisites should I have before starting this book?

    atbildi: 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.
  • jautājums: What makes this book different from other data science books?

    atbildi: 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.
  • jautājums: Where can I buy 'Graph Data Science with Python and Neo4j'?

    atbildi: 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 Latvia. Ubuy offers a variety of options for obtaining this book, making it easier for customers in Latvia to access valuable resources for their learning and professional development. Explore Ubuy’s platform for additional details and purchasing options.

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Customer Reviews & Ratings

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Pros

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

Mīnusi

  • Some projects may require prior knowledge.

Product Price History

Svarīga informācija

  • Ierobežojumi: attiecībā uz produktiem, kas tiek nosūtīti starptautiski, lūdzu, ņemiet vērā, ka jebkura ražotāja garantija var nebūt derīga; ražotāja servisa iespējas var nebūt pieejamas; produktu rokasgrāmatas, instrukcijas un drošības brīdinājumi var nebūt galamērķa valsts valodās; produkti (un pavadošie materiāli) var nebūt izstrādāti saskaņā ar galamērķa valsts standartiem, specifikācijām un marķēšanas prasībām; un produkti var neatbilst galamērķa valsts spriegumam un citiem elektriskajiem standartiem (ja nepieciešams, nepieciešams adapteris vai pārveidotājs). Par to, lai produktu varētu likumīgi ievest galamērķa valstī, ir atbildīgs saņēmējs. Pasūtot no Ubuy vai tā saistītajiem uzņēmumiem, saņēmējs ir ierakstu importētājs, un viņam jāievēro visi galamērķa valsts tiesību akti un noteikumi.
  • Ne visi Ubuy uzskaitītie produkti ir pārdošanā, jo Ubuy ir globāla meklētājprogramma. Uz produktiem attiecas eksporta/tirdzniecības noteikumi.

€ 35

Pasūtiet tūlīt un saņemiet to Trešdiena, Jūlijs 08

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Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

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Funkcijas un priekšrocības

  • 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.