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Item #:
91658708
Mastering Python for Data Science
Item #:
91658708
MWK 43720
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Product Details
| Publisher | Packt Publishing |
| Publication date | August 31, 2015 |
| Language | English |
| Print length | 356 pages |
| ISBN-10 | 1784390151 |
| ISBN-13 | 978-1784390150 |
| Item Weight | 1.12 pounds (510 grams) |
| Dimensions | 7.5 x 0.67 x 9.25 inches (19.1 x 1.7 x 23.5 cm) |
Product Description
Mastering Python for Data Science
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Data Mining Editorial Review
**** The product in question, a book aimed at introducing readers to data science with Python, elicits a mix of impressions from its audience. Several reviewers appreciate its broad coverage and the practical utility it provides, especially for those who have some familiarity with Python. The initial chapters offer a solid primer on essential libraries like Numpy and Pandas, which can benefit beginners, while also diving into statistical examples and canonical machine learning algorithms that even experienced programmers may find valuable. However, the book faces Considerable criticism for its editorial quality, featuring numerous typos and awkward grammatical Constructions that diminish its credibility. Many readers have noted that certain explanations are dense and confusing, leaving novice readers struggling to grasp key concepts. The clarity of figures and tables is another pain point, as blurry images make it challenging to absorb intricate details. The feedback also indicates that while the book serves as a decent reference—especially for those already familiar with Python—its lack of depth and clarity may alienate newcomers to the field of data science. In summary, while the book demonstrates potential as a resourceful tool for those already versed in Python, it falls short in delivering a user-friendly learning experience for complete beginners, raising questions about its overall educational value. **
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Pros
- Broad coverage of significant topics in data science with Python.
- Useful for experienced Python programmers entering data science.
- Contains practical examples and unique Python statistical techniques.
- Quick pace that allows readers to become acquainted with key concepts swiftly.
Cons
- Many explanations and concepts are vague and not well-articulated.
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