Articoli correlati a Think Stats: Exploratory Data Analysis
Sinossi
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
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull'autore
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.
Dalla quarta di copertina
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
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Paperback. Condizione: new. Paperback. 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 librariesImprove predictions and insights with regression modelsDive into specialized topics like time series analysis and survival analysisIntegrate statistical techniques and tools for validation, inference, and moreCommunicate findings with effective data visualizationTroubleshoot common data analysis challengesBoost reproducibility and collaboration in data analysis projects with interactive notebooks 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781098190255
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Paperback. Condizione: New. 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 librariesImprove predictions and insights with regression modelsDive into specialized topics like time series analysis and survival analysisIntegrate statistical techniques and tools for validation, inference, and moreCommunicate findings with effective data visualizationTroubleshoot common data analysis challengesBoost reproducibility and collaboration in data analysis projects with interactive notebooks. Codice articolo LU-9781098190255
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