Think stats
exploratory data analysis
Seiten
2025
|
1. Auflage
O'Reilly Media (Verlag)
978-1-0981-9025-5 (ISBN)
O'Reilly Media (Verlag)
978-1-0981-9025-5 (ISBN)
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.
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
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
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 Master's and Bachelor's degrees from MIT. He is the author of Think Python, Think Bayes, Think DSP, and a blog, Probably Overthinking It.
| Erscheinungsdatum | 22.04.2025 |
|---|---|
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Mathematik | |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 1-0981-9025-4 / 1098190254 |
| ISBN-13 | 978-1-0981-9025-5 / 9781098190255 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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