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Data Science Bookcamp: Five real-world Python projects
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MGA 289402
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Learn data science with Python by building five real-world projects!
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Ce qui se démarque
Détails du produit
- Learn data science with Python by building five real-world projects
- Techniques for computing and plotting probabilities
- Statistical analysis using Scipy
- Organize datasets with clustering algorithms
- Visualize complex multi-variable datasets
- Train a decision tree machine learning algorithm
| Publisher | Manning |
| Publication date | November 30, 2021 |
| Edition | 1st |
| Language | English |
| Print length | 704 pages |
| ISBN-10 | 1617296252 |
| ISBN-13 | 978-1617296253 |
| Item Weight | 2.54 pounds (1.15 kg) |
| Dimensions | 7.38 x 1.1 x 9.25 inches (18.7 x 2.8 x 23.5 cm) |
À qui est-ce destiné ?
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Aspiring Data Scientists
Ideal for individuals wanting to build practical Python skills through real-world data science projects.
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Students in Tech
Perfect for college students in computer science or related fields seeking hands-on experience with data analysis.
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Professionals Upgrading Skills
Beneficial for tech professionals looking to pivot into data science or enhance their existing Python capabilities.
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Complete Beginners
Not suitable for those without any prior programming knowledge, as foundational concepts may be challenging to grasp.
DESCRIPTION DU PRODUIT
Data Science Bookcamp: Five real-world Python projects
Questions et réponses des clients
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question:
What topics does the Data Science Bookcamp cover?
répondre: The Data Science Bookcamp explores essential data science concepts through hands-on Python projects. Readers can expect to delve into real-world applications such as data manipulation, exploratory data analysis, machine learning, and visualization techniques. Each project is designed to help readers translate theory into practice, making it easier to understand complex topics in a relatable context. -
question:
Is this book suitable for beginners in data science?
répondre: Yes, this book is well-suited for beginners who are looking to grasp the fundamental concepts of data science. It provides clear explanations and step-by-step instructions for various projects, allowing readers to build their skills from the ground up. Moreover, practical examples are included, which makes it easier for newcomers to relate to the material and apply what they learn. -
question:
What kind of Python projects are included in the book?
répondre: The Data Science Bookcamp includes five hands-on Python projects that vary in complexity and application. Projects cover areas such as data cleaning, analysis, and the development of predictive models. These projects not only enrich the reader's understanding of Python but also enhance their capability to tackle real-world data challenges effectively. -
question:
Can this book help me prepare for a career in data science?
répondre: Absolutely! This book provides practical experience through real-world projects, which are crucial for building a portfolio. Employers often value practical knowledge alongside theoretical understanding, and working through these projects can give you a competitive edge. Readers will gain insightful experiences that they can discuss in interviews. -
question:
What programming skills do I need before starting the book?
répondre: A basic understanding of Python is beneficial before beginning the Data Science Bookcamp. While the book is designed to be approachable, familiarity with concepts such as variables, functions, and loops will enhance the learning experience. If you're new to Python, consider reviewing introductory materials or tutorials to build foundational skills. -
question:
Are there any supplementary resources available with the book?
répondre: Yes, the Data Science Bookcamp often comes with supplementary resources such as code snippets, data sets, and access to an online community where readers can share ideas and ask questions. These resources serve to enrich the learning experience and provide additional support as you work on the projects included in the book. -
question:
How does this book differ from other data science books?
répondre: The Data Science Bookcamp stands out due to its project-based approach, focusing on real-world applications. While many books provide theoretical knowledge, this one emphasizes hands-on experience, enabling readers to apply what they learn. The combination of practical projects and supportive community interaction makes this book a unique learning tool in the data science field. -
question:
Is there a focus on any particular industry applications in the book?
répondre: Yes, the Data Science Bookcamp encompasses a variety of industry applications within its projects. Areas covered might include finance, healthcare, marketing, and technology, showcasing how data science is utilized across different fields. This broad exposure not only enhances learning but also prepares readers for diverse opportunities in the job market. -
question:
What is the expected time commitment to complete the book's projects?
répondre: The time commitment to complete the projects varies based on individual skill levels, but each project can typically be completed within a few hours to a couple of days. The structured approach and clear instructions aid in efficiently managing time, making it feasible for both busy professionals and students with varying schedules. -
question:
Where can I buy the Data Science Bookcamp: Five real-world Python projects 1st Edition?
répondre: You can purchase the Data Science Bookcamp: Five real-world Python projects 1st Edition from Ubuy in Madagascar. Ubuy provides a wide selection of books and educational resources, ensuring you can easily access the materials needed to elevate your data science skills.
Data Processing Editorial Review
**** The "Data Science Bookcamp: Five Real-World Python Projects" serves as an insightful guide for aspiring data scientists, striking a balance between foundational knowledge and practical application. The author's methodical approach to structuring the book enhances the learning experience, offering five case studies that gradually increase in complexity and introduce various algorithms. This progression allows readers to build understanding step-by-step, making it particularly beneficial for those who may not have a solid grounding in statistics or data science. For readers from different backgrounds, such as a seasoned developer or even a newcomer to the technical field, the book makes data science accessible. The clear writing style demystifies complex subjects, while the inclusion of code snippets and diagrams serves to clarify concepts visually. It’s a credible resource for learning Python as it applies to real-world use cases, rather than simply focusing on theoretical concepts. The content is further enriched by the available downloadable Jupyter notebooks, allowing readers to engage practically with the material. However, readers should be encouraged to have a basic understanding of Python before diving in, as that prior knowledge will prevent any possible frustration with the projects. While the book is highly regarded, it does have some critiques—most notably regarding the presentation of code blocks, which can be difficult to read against a white backdrop. Despite this minor drawback, the comprehensive nature and structured approach make "Data Science Bookcamp" a highly recommended resource for anyone looking to enhance their data science skills. ---- **
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Avantages
- Structured case studies presenting related concepts through practical use.
- Iterative approach to concept introduction aids understanding, especially of complex statistics.
- Familiarity with key Python modules while introducing less-known but useful ones.
- Color-coded section headers enhance visual navigation.
- Clear and approachable writing style makes for a manageable read.
Les inconvénients
- Code presentation could be improved; challenging to read against a white page background.
CONFIANCE EN LA PLATEFORME & PROTECTION DE L'ACHETEUR
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Caractéristiques et avantages
- Techniques for computing and plotting probabilities
- Statistical analysis using Scipy
- Organize datasets with clustering algorithms
- Visualize complex multi-variable datasets
- Train a decision tree machine learning algorithm
- No prior data science or machine learning skills required.
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Profitez d'une expérience d'achat sereine avec des produits 100 % originaux, une sécurité de paiement conforme PCI DSS, une protection des données certifiée ISO 27001, la livraison transfrontalière la plus rapide, les retours gratuits* et un emballage sécurisé pour chaque commande.