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Item #:
161006545
Causal Inference for Data Science
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Item #:
161006545
GHS 1365
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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.
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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
- 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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GHS 1365
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Features & Benefits
- Learn to determine causality and estimate effects with statistical and machine learning techniques.
- Access practical methodologies even without experiments or A/B testing.
- Gain insights into customer behaviors and enhance decision-making skills.
- Apply causal reasoning to ordinary business scenarios for optimal outcomes.
- Utilize Directed Acyclic Graphs (DAGs) for assessing advertising performance and more.
- Purchase includes free eBook in PDF and ePub formats.
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