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Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn
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Gain hands-on experience with industry-standard data analysis and machine learning tools in Python
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Detalji o proizvodu
| Publisher | Packt Publishing |
| Publication date | April 30, 2019 |
| Language | English |
| Print length | 374 pages |
| ISBN-10 | 1838551026 |
| ISBN-13 | 978-1838551025 |
| Item Weight | 1.51 pounds (680 grams) |
| Dimensions | 7.5 x 0.85 x 9.25 inches (19.1 x 2.2 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners wanting hands-on experience with practical data science projects using Python and key libraries.
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Intermediate Learners
Great resource for those with basic knowledge seeking to deepen skills and apply them in real-world scenarios.
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Industry Professionals
Beneficial for practitioners looking to enhance their project management skills and learn effective data science techniques.
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Advanced Experts
Experienced data scientists may find the content too basic and not challenging enough for their skill level.
OPIS PROIZVODA
Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn
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Odgovor: Vrijeme isporuke vašeg naručenog proizvoda ovisi o tome šta ste naručili i načinu dostave koji ste odabrali.. Predviđeno vrijeme dostave se spominje prilikom naplate, stoga budite bezbrižni prilikom kupovine.
Intelligence & Semantics Editorial Review
Data Science Projects with Python is a comprehensive guide that takes readers through real-life data problems and provides a detailed explanation of concepts. The book covers everything from exploratory data analysis to model creation and evaluation. The author does a good job of explaining the code and concepts, making it a valuable resource for beginners and intermediate data scientists. However, some readers felt that the book did not clarify or illuminate the subject further and suggested supplementing it with online tutorials. There were also complaints about the code not working and the quality of the printing. Overall, the book is well-written and provides a practical approach to learning data science and machine learning.
Customer Reviews & Ratings
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Pros
- Detailed explanation of code and concepts
- Practical approach to learning data science and machine learning
- Covers real-life data problems
- Valuable resource for beginners and intermediate data scientists
Cons
- Does not clarify or illuminate the subject further for some readers
Product Price History
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Karakteristike i prednosti
- Practical guidance on industry-standard data analysis and machine learning tools
- Learn how to examine, extract and present insights from your own data
- Build knowledge in preparing data and feeding it to machine learning algorithms
- Tune algorithms to provide accurate predictions on new and unseen data
- Gain insights into the working and output of machine learning algorithms
- Developed by trusted experts with step-by-step guidance and worked solutions
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