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Causal Inference for Data Science
Causal Inference for Data Science
Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes.
Causal Inference for Data Science
Item #: 161006545

Causal Inference for Data Science

Item #: 161006545

EGP 4587

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Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes.
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What Stands Out

Data Integration
Seamlessly integrates various data sources, allowing data scientists to perform sophisticated analyses without complications, enhancing workflow efficiency and accuracy.
Robust Methodologies
Offers advanced causal inference methodologies that empower data scientists to derive actionable insights, addressing user challenges in making reliable predictions from complex datasets.
User-Friendly Interface
Designed with an intuitive interface that simplifies complex processes, making it accessible for data professionals of all skill levels and improving overall productivity.

Product Details

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  • When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.In Causal Inference for Data Science you will learn how to:Model reality using causal graphsEstimate causal effects using statistical and machine learning techniquesDetermine when to use A/B tests, causal inference, and machine learningExplain and assess objectives, assumptions, risks, and limitationsDetermine if you have enough variables for your analysisIt's possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You'll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.
Publisher Manning Publications
Publication date 24 Jan. 2025
Edition 1st
Language English
Print length 392 pages
ISBN-10 1633439658
ISBN-13 978-1633439658
Item weight 1.05 kg
Dimensions 18.73 x 2.54 x 23.5 cm

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Data Mining Editorial Review

Causal Inference For Data Science is a comprehensive guide published by Manning Publications on January 24, 2025. This 1st edition book, written in English, spans 392 pages and is designed to equip readers with essential knowledge in causal inference techniques relevant to data science. With dimensions of 18.73 x 2.54 x 23.5 cm and weighing 1.05 kg, it is a substantial yet manageable resource for professionals and enthusiasts alike. It aims to bridge the gap between statistics and practical data science applications, making complex concepts accessible and applicable in real-world scenarios.

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Pros

  • Comprehensive guide on causal inference techniques
  • Well-structured content for better understanding
  • Written by reputable Manning Publications
  • Applicable to real-world data science problems
  • Ideal for both professionals and enthusiasts

Cons

  • Might be heavy for extended reading sessions

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