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89402933
Time Series Forecasting in Python
89% of respondents would recommend this to a friend
товар №:
89402933
TJS 939
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What Stands Out
Comprehensive Toolkit
Offers a wide array of libraries and tools for effective time series analysis, including statistical models, machine learning techniques, and data visualization to enhance prediction accuracy and insights.
User-Friendly Libraries
Utilizes popular libraries like Pandas and StatsModels for streamlined data manipulation and forecasting, making it accessible for beginners while remaining powerful for expert analysts.
Community Support
Backed by a large community and extensive online resources, allowing users to find solutions, tutorials, and forums that enhance learning and troubleshooting during the forecasting process.
Информация о продукте
| Publisher | Manning Publications |
| Publication date | October 4, 2022 |
| Language | English |
| Print length | 456 pages |
| ISBN-10 | 161729988X |
| ISBN-13 | 978-1617299889 |
| Item Weight | 1.55 pounds (700 grams) |
| Dimensions | 7.38 x 1.14 x 9.25 inches (18.7 x 2.9 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists wanting to enhance their skills in time series analysis and prediction techniques.
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Business Analysts
Beneficial for business analysts who need to predict trends and make data-driven decisions.
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Students and Learners
Perfect for students learning data analysis and forecasting concepts using Python through practical applications.
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Complete Beginners
Not suitable for those new to programming or Python, as foundational knowledge is assumed.
ОПИСАНИЕ ТОВАРА
Time Series Forecasting in Python
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Probability & Statistics Editorial Review
In reviewing "Time Series Forecasting in Python," it's clear that customer experiences vary widely, reflecting both appreciation and disappointment with the book's content and structure. Many readers highlight its strengths, particularly its accessibility for beginners and clear explanations of complex concepts. The book skillfully guides readers from foundational principles to more advanced topics, making it particularly valuable for non-technical individuals. The integration of Python code with theoretical concepts is praised as it enhances understanding, allowing readers to grasp how to apply machine learning to time series forecasting effectively. However, some customers express significant issues with the book. A recurring critique is its failure to demonstrate how to forecast beyond the dataset at hand. Readers expect books on forecasting to teach future prediction methods, but many feel this book falls short in that regard. Additionally, criticisms have been raised about the apparent redundancy in the text, leading to perceptions of unnecessary length and a high price tag that some believe is not justified by the content provided. Overall, while the book offers a well-structured introduction to time series forecasting with Python that is beneficial for beginners, it may not meet the expectations of those looking to advance into future forecasting techniques. **
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Плюсы
- Clear explanations, especially for Python code, beneficial for beginners.
- Covers a range of topics from basic to more sophisticated concepts effectively.
- Good for non-technical readers due to its non-mathematical approach.
- Unique structure that integrates concepts with practical Python examples.
Минусы
- Fails to demonstrate forecasting beyond available datasets.
Product Price History
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Особенности и преимущества
- Create models that capture seasonal effects and external variables.
- Utilize multivariate forecasting to predict multiple time series effectively.
- Employ deep learning techniques for large datasets using Python.
- Automate your forecasting process for efficiency and accuracy.
- Access immediately applicable concepts that deliver real results.
- Written for data scientists looking to elevate their skills from R to Python.
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