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216104105
Building Statistical Models in Python: Develop useful models for regression classification time series and survival analysis
88% of respondents would recommend this to a friend
Item #:
216104105
CVE 7031
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Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.
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What Stands Out
Comprehensive Coverage
Covers regression, classification, time series, and survival analysis, providing a holistic approach to statistical modeling in Python, making it a one-stop resource for diverse analytical needs.
Practical Applications
Focuses on real-world applications, ensuring that learners can immediately apply statistical models to solve practical problems, enhancing their relevance and usability in various industries.
User-Friendly Approach
Designed for both beginners and experienced data scientists, the book breaks down complex concepts into digestible content, facilitating a smoother learning curve while mastering Python for statistical modeling.
Product Details
- Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insightsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesGain expertise in identifying and modeling patterns that generate successExplore the concepts with Python using important libraries such as stats modelsLearn how to build models on real-world data sets and find solutions to practical challengesBook DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more.By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.What you will learnExplore the use of statistics to make decisions under uncertaintyAnswer questions about data using hypothesis testsUnderstand the difference between regression and classification modelsBuild models with stats models in PythonAnalyze time series data and provide forecastsDiscover Survival Analysis and the problems it can solveWho This Book Is ForIf you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.Table of ContentsProduct Information DocumentSampling and GeneralizationDistributions of DataHypothesis TestingParametric TestsNon-Parametric TestsLinear RegressionMore Discussion on Model Selection & RegularizationLogistic RegressionDiscriminant AnalysisIntroduction to Time SeriesARIMA ModelsMultivariate Time Series MethodsTime to Event variables - An introductionModels with Survival Responses
| Publisher | Packt Publishing |
| Publication date | 31 Aug. 2023 |
| Language | English |
| Print length | 383 pages |
| ISBN-10 | 1804614289 |
| ISBN-13 | 978-1804614280 |
| Dimensions | 19.05 x 2.41 x 23.5 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to enhance their modeling skills and apply statistical concepts using Python effectively.
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Analysts
Suitable for analysts who wish to deepen their analytical skills for accurate predictions in various fields.
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Students
Great for students studying data science or statistics who want practical experience building models in Python.
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Beginners
May not be suitable for absolute beginners without prior knowledge of Python or statistical modeling concepts.
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Editorial Review
Building Statistical Models In Python: Develop Useful Models For Regression Classification Time Series And Survival Analysis is an insightful guide published by Packt Publishing on 31 Aug. 2023. Spanning 383 pages, this book is ideal for data enthusiasts looking to enhance their skills in statistical modeling using Python. It delves into various modeling techniques, important for tasks related to regression, classification, time series analysis, and survival analysis. The publication is well-structured, making complex concepts accessible to readers of differing experience levels. Many readers appreciate the practical approach that emphasizes hands-on application, which significantly aids understanding.
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Pros
- Covers regression, classification, and time series analysis
- Hands-on approach enhances practical understanding
- Suitable for various skill levels in data science
- Well-structured format for easy comprehension
- Published by a reputable publisher in the field
Cons
- Some readers may desire more advanced topics
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Features & Benefits
- Gain expertise in identifying and modeling patterns for success.
- Explore key statistical concepts with essential Python libraries.
- Build models on real-world data sets for practical solutions.
- No prior statistics knowledge needed; basic Python experience recommended.
- Learn regression, classification, time series, and survival analysis.
- Achieve fluency in statistical modeling for informed decision-making.
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