Articles liés à Think Stats: Exploratory Data Analysis

Think Stats: Exploratory Data Analysis - Couverture souple

Downey, Allen B

 
9781098190255: Think Stats: Exploratory Data Analysis
Couverture souple
ISBN 10 : 1098190254 ISBN 13 : 9781098190255
Editeur : O'Reilly Media, 2025

Synopsis

If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis--from wrangling data and generating statistics to identifying patterns and testing hypotheses.

Whether you're a data scientist, software engineer, or data enthusiast, you'll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You'll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place.

  • Analyze data distributions and visualize patterns using Python libraries
  • Improve predictions and insights with regression models
  • Dive into specialized topics like time series analysis and survival analysis
  • Integrate statistical techniques and tools for validation, inference, and more
  • Communicate findings with effective data visualization
  • Troubleshoot common data analysis challenges
  • Boost reproducibility and collaboration in data analysis projects with interactive notebooks

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Allen B. Downey is a Professor of Computer Science at Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Masterâs and Bachelorâs degrees from MIT. He is the author of Think Python, Think Bayes, Think DSP, and a blog, Probably Overthinking It.

À propos de la quatrième de couverture

If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis--from wrangling data and generating statistics to identifying patterns and testing hypotheses.

Whether you're a data scientist, software engineer, or data enthusiast, you'll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You'll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place.

  • Analyze data distributions and visualize patterns using Python libraries
  • Improve predictions and insights with regression models
  • Dive into specialized topics like time series analysis and survival analysis
  • Integrate statistical techniques and tools for validation, inference, and more
  • Communicate findings with effective data visualization
  • Troubleshoot common data analysis challenges
  • Boost reproducibility and collaboration in data analysis projects with interactive notebooks

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
O'Reilly Media
Date d'édition
2025
Langue
anglais
ISBN 10 
1098190254
ISBN 13 
9781098190255
Reliure
Broché
Numéro d'édition
3
Nombre de pages
321
Coordonnées du fabricant
O'Reilly
https://oreilly.com

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