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size
22.86 x 17.78 x 2.03 cm
Product Information
Ideal For
General Readers, Fiction Lovers, Mythology & Historical Fiction Readers, Indian Mythology Enthusiasts, Young Adults (YA) & Adults, College Students, Competitive Exam Aspirants (General Reading / Culture), Gift Purpose (Books Gift Set), Spiritual & Philosophy Interest Readers, Library / Book Collection Buyers
Country Of Origin
India
About Author
Matheus Facure Is An Economist, Researcher, And Author Known For His Work In Causal Inference And Applied Econometrics. He Focuses On Explaining Modern Data Science And Causal Analysis Methods In A Clear, Practical Way, Helping Readers Understand How To Draw Reliable Conclusions From Data. He Is Especially Recognized For Bridging Theory And Real-World Applications For Students And Professionals.
Author Name
Matheus Facure
Genre
Computer Science
No Of Pages
408
Publisher
Penguin Random House
Product Description
- Premium Quality: This paperback edition of Causal Inference in Python addresses real-world business questions - such as how additional marketing spend affects buyer volume and how to establish an optimal pricing strategy - through the lens of causal inference.
- Product Design: The book is structured around the principle that the best way to determine how business levers affect key metrics is through causal inference, offering a rigorous yet practical framework for data-driven decision-making.
- User Experience: Author Matheus Facure is known for bridging theory and real-world applications, making complex causal analysis methods clear and accessible for both students and working professionals.
- Versatile Occasion: The book tackles questions such as which customers will only buy when given a discount coupon, demonstrating how causal inference can be applied directly to marketing, pricing, and customer behaviour analysis.
- Quality Assurance: At 408 pages, this comprehensive volume published by Books Kingdom covers modern data science and causal analysis methods with a focus on helping readers draw reliable conclusions from data.
- Lifestyle Essential: A vital resource for economists, data scientists, and business analysts seeking to move beyond correlation and apply principled causal reasoning to their work.