Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages
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Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages
Master data validation, manipulation, and visualization in Power BI with advanced Python and R techniques.
Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages
товар №: 87481123

Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages

товар №: 87481123

KZT 25451

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  • Ingest, transform, manipulate, and visualize your data beyond Power BI's capabilities.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesDiscover best practices for using Python and R in Power BI by implementing non-trivial codeEnrich your Power BI dashboards using external APIs and machine learning modelsCreate any visualization, as complex as you want, using Python and R scriptsBook DescriptionThe latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python.This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis.You'll reinforce your learning with questions at the end of each chapter.What you will learnConfigure optimal integration of Python and R with Power BIPerform complex data manipulations not possible by default in Power BIBoost Power BI logging and loading large datasetsExtract insights from your data using algorithms like linear optimizationCalculate string distances and learn how to use them for probabilistic fuzzy matchingHandle outliers and missing values for multivariate and time-series dataApply Exploratory Data Analysis in Power BI with RLearn to use Grammar of Graphics in PythonWho this book is forThis book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.Table of ContentsWhere and How to Use R and Python Scripts in Power BIConfiguring R with Power BIConfiguring Python with Power BISolving Common Issues When Using Python and R in Power BIImporting Unhandled Data ObjectsUsing Regular Expressions in Power BIAnonymizing and Pseudonymizing your Data in Power BILogging Data from Power BI to External SourcesLoading Large Datasets Also Beyond the Available RAM in Power BIBoosting Data Loading Speed in Power BI with Parquet FormatCalling External APIs To Enrich Your DataCalculating Columns Using Complex Algorithms: DistancesCalculating Columns Using Complex Algorithms: Fuzzy MatchingCalculating Columns Using Complex Algorithms: Optimization ProblemsAdding Statistics Insights: AssociationsAdding Statistics Insights: Outliers and Missing Values(N.B. Please use the Look Inside option to see further chapters)
Publisher Packt Publishing
Publication date March 29, 2024
Edition 2nd
Language English
File size 61.4 MB
Screen Reader Supported
Enhanced typesetting Enabled
X-Ray Not Enabled
Word Wise Not Enabled
Print length 1364 pages
ISBN-13 978-1837635863
Page Flip Enabled
Item Weight1 lbs (450 grams)

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Вопросы и ответы клиентов

  • вопрос: What is the main focus of 'Extending Power BI with Python and R'?

    отвечать: The book primarily focuses on enhancing Power BI's capabilities through the integration of Python and R, two powerful analytical programming languages. By incorporating these languages, users can perform complex data manipulations and advanced analytics that are not readily achievable with Power BI's built-in features. This can be particularly beneficial for data scientists and analysts looking to create sophisticated visualizations or compute specialized metrics, thereby elevating their reporting capabilities.
  • вопрос: Who would benefit from reading this book?

    отвечать: This book is targeted at data professionals, including data analysts, data scientists, and BI developers who want to deepen their understanding of Power BI and enhance their data analysis skills. Those familiar with Python and R, or those who aim to learn how to use them within Power BI, will find practical guidance and examples that can be applied in various data-driven projects. It's excellent for anyone looking to leverage analytical languages for enriching their business intelligence practices.
  • вопрос: What advanced tools does the book cover for Power BI?

    отвечать: The book delves into advanced tools like Python and R scripting that can be utilized within Power BI to perform intricate data analysis. These tools enable users to access statistical methods, machine learning techniques, and complex data manipulations that extend the functionality of Power BI. Practical examples illustrate how to implement these techniques, making it an ideal resource for enhancing reporting capabilities and gaining deeper insights into data.
  • вопрос: Can I learn to code in Python or R through this book?

    отвечать: While 'Extending Power BI with Python and R' does include code snippets and examples, it is not intended as a beginner's guide to learning these programming languages from scratch. Readers should have some familiarity with Python or R to effectively utilize the book's content. However, for those already equipped with basic coding skills, the book serves as a solid resource for applying these languages in Power BI environments, facilitating advanced data analysis and visualization.
  • вопрос: Does the book provide practical examples?

    отвечать: Yes, the book offers a variety of practical examples and use cases that demonstrate how to apply Python and R in Power BI. These examples are designed to illustrate specific analytics scenarios, enabling readers to see how to implement scripts and functions in real-world applications. This hands-on approach helps bridge the gap between theory and practice, allowing data professionals to enhance their analytical capabilities efficiently.
  • вопрос: Is this book suitable for beginners in data analytics?

    отвечать: While the book contains valuable insights into extending Power BI with Python and R, it is better suited for individuals who have a foundational understanding of Power BI and some experience with programming. Beginners in data analytics might find the content quite advanced, as it assumes some familiarity with data visualization tools and analytical languages. Therefore, it's advisable for newcomers to consider foundational materials before diving into this advanced text.
  • вопрос: How can this book improve my data visualization skills?

    отвечать: This book enhances data visualization skills by teaching readers how to incorporate complex analytics and dynamic visual elements into Power BI reports using Python and R. By mastering these programming languages within the context of Power BI, users can create more sophisticated visualizations that convey deeper insights. This leads to better storytelling with data, making reports not just informative but also engaging for stakeholders and audiences.
  • вопрос: What is unique about the 2nd edition of this book?

    отвечать: The 2nd edition of 'Extending Power BI with Python and R' includes updated content that reflects the latest features and enhancements of Power BI, along with new examples and scripts that leverage current best practices in data analytics. This edition aims to provide readers with more relevant and practical information, helping them stay ahead in the rapidly evolving field of business intelligence and analytical programming.
  • вопрос: What prerequisites should I have before reading this book?

    отвечать: Before diving into this book, it's beneficial to have a foundational knowledge of Power BI, data analytics concepts, and a basic understanding of programming in Python or R. Familiarity with data manipulation and visualization techniques will enhance your ability to grasp the advanced topics discussed. If you are comfortable with these prerequisites, you will find the book a valuable addition to your learning journey in utilizing analytical languages within Power BI.
  • вопрос: Where can I buy 'Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages 2nd Edition'?

    отвечать: You can purchase 'Extending Power BI with Python and R' on Ubuy. Ubuy offers a convenient platform for acquiring this Kindle edition, making it accessible for enthusiasts and professionals looking to bolster their data analysis skills. Whether you are upgrading your BI toolbox or seeking new insights, Ubuy provides an easy way to access this essential resource.

Data Processing Editorial Review

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The editorial review of "Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages, 2nd Edition" reveals overwhelmingly positive feedback from users who are both beginners and seasoned professionals within the data analysis community. This updated edition effectively caters to its target audience of business analysts and data scientists looking to harness the capabilities of Python and R within Power BI. Readers appreciate the book’s in-depth exploration of optimizing the integration of Python and R, emphasizing practical techniques that facilitate advanced data analysis. Users have noted the book's comprehensive coverage of key topics, such as managing large datasets, utilizing the Intel Math Kernel Library for improved performance, and effectively tackling integration challenges within Power BI. The arguments presented throughout the text encourage analysts to push beyond traditional limitations, turning Power BI into a versatile analytical hub. Moreover, the author's straightforward writing style, enriched with visuals, screenshots, and sample code, enhances the learning experience, making complex principles more digestible. The inclusion of quizzes at the end of each chapter is highly praised as a feature that allows readers to reinforce their learning. Overall, the book is not only seen as a vital resource for technical advancement and practical analytics but also as an essential guide for leveraging existing Power BI functionalities. Users with prior knowledge of Power BI found the second edition significantly impressive, claiming it expanded their analytical capabilities Considerably. **

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Плюсы

  • Practical guidance on optimizing R and Python in Power BI
  • Covers advanced techniques like fuzzy matching and SQL Server external languages
  • Highly instructive for managing large datasets efficiently
  • Includes quizzes for self-assessment at the end of each chapter
  • Clear explanations complemented by visuals and sample code
  • New content expands on practical applications and problem-solving

Минусы

  • May be challenging for complete beginners unfamiliar with Power BI or programming languages

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Особенности и преимущества

  • Unlock advanced analytics capabilities with Python and R in Power BI.
  • Learn to create complex visualizations and data manipulations.
  • Discover best practices for integrating APIs and machine learning models.
  • Optimize performance using Intel's Math Kernel Library and Parquet format.
  • Enhance data security with pseudonymization and anonymization techniques.
  • Reinforce skills with chapter-end questions for effective learning.

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