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Nº de artículo:
123736949
Time Series Forecasting in Python Paperback – 10 Nov. 2022
87% of respondents would recommend this to a friend
Nº de artículo:
123736949
NIO 2449
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What Stands Out
Comprehensive Guide
This book offers an in-depth exploration of time series forecasting methods, making it suitable for both beginners and experienced practitioners seeking to enhance their Python skills.
Practical Applications
Includes real-world examples and use cases, allowing readers to directly apply theoretical knowledge to practical scenarios in various industries.
Latest Techniques
Covers the most up-to-date forecasting techniques and tools in Python, ensuring readers are equipped with current knowledge and practices in data science.
Detalles de producto
| Publisher | Manning Publications |
| Publication date | 10 Nov. 2022 |
| Edition | 1st |
| Language | English |
| Print length | 456 pages |
| ISBN-10 | 161729988X |
| ISBN-13 | 978-1617299889 |
| Item weight | 703 g |
| Dimensions | 18.75 x 2.9 x 23.5 cm |
Who Should Buy?
-
Data Analysts
Ideal for data analysts seeking to enhance their forecasting skills using Python libraries and methods.
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Students
Students studying statistics or data science, requiring a structured introduction to time series analysis in Python.
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Business Professionals
Business professionals who want to leverage predictive analytics to improve decision-making and strategic planning.
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Beginner Programmers
Not suitable for beginners unfamiliar with Python programming and statistical concepts, as prerequisites may overwhelm.
DESCRIPCIÓN DEL PRODUCTO
Time Series Forecasting in Python Paperback – 10 Nov. 2022
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Data Mining Editorial Review
"Time Series Forecasting in Python" has received high praise from customers for its clear and comprehensive approach to introducing the often complex subject of time series forecasting. Reviewers frequently mention that it serves as an excellent starting point for beginners, offering a structured journey from basic concepts to more advanced techniques. The explanations are notably precise, with an almost line-by-line breakdown of Python code, which helps readers grasp the material easily. This clarity is highlighted as a significant strength, allowing for a smoother learning curve without overwhelming the reader. Furthermore, the book incorporates the latest developments in applying machine learning to time series forecasting, alongside classical methods in Python. This combination provides a well-rounded introduction to both contemporary and foundational techniques. Many readers expressed satisfaction with the flow of the content, specifically noting that the author gradually increases complexity, making it accessible for those without a strong background in data science or programming. However, some feedback pointed out that the transition to machine learning concepts occurs rather quickly, which may pose a challenge for certain readers. Despite this minor critique, the overall sentiment leans positively, with many customers feeling encouraged to pursue further learning, particularly in TensorFlow, as a result of their exposure to the material in this book. **
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ventajas
- Clear and comprehensive explanations, making it beginner-friendly.
- Gradually increasing complexity that helps in understanding.
- Detailed breakdown of Python code for practical application.
- Covers both classical methods and contemporary machine learning approaches.
Contras
- Some readers feel the transition to machine learning is too abrupt.
Product Price History
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características y beneficios
- 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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