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Item #:
90002878
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value (The Pragmatic Programmers)
Item #:
90002878
KWD 9
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What Stands Out
Comprehensive Curriculum
Covers essential aspects of data science from data collection to predictive modeling, ensuring learners acquire a well-rounded skill set tailored for real-world applications.
Practical Examples
Offers hands-on exercises and practical examples, allowing readers to apply concepts in Python, enhancing their ability to solve actual data problems efficiently.
Expert Insights
Written by seasoned professionals, providing valuable industry insights and best practices that equip readers with knowledge and skills to excel in data science careers.
Product Details
| Publisher | Pragmatic Bookshelf |
| Publication date | September 13, 2016 |
| Edition | 1st |
| Language | English |
| Print length | 226 pages |
| ISBN-10 | 9781680501841 |
| ISBN-13 | 978-1680501841 |
| Item Weight | 14.4 ounces (408.24 grams) |
| Dimensions | 7.5 x 0.48 x 9.25 inches (19.1 x 1.2 x 23.5 cm) |
Who Should Buy?
-
Beginner Data Scientists
Ideal for novices looking to grasp fundamental data science concepts and Python's application within the field.
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Programming Students
Great for students studying programming who wish to apply Python to data science tasks and enhance their skills.
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Business Analysts
Beneficial for analysts wanting to leverage data science techniques for data-driven decision-making and value extraction.
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Advanced Practitioners
Not suitable for experienced data scientists seeking advanced methodologies or specialized techniques not covered in the book.
Product Description
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value (The Pragmatic Programmers)
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Decision-Making & Problem Solving Editorial Review
**** "Data Science Essentials in Python" by The Pragmatic Programmers has garnered positive reception from both novice and experienced programmers alike. Many users laud the book for its clarity and structured approach, making it suitable for beginners and intermediate Python programmers. The introductory sections are praised for effectively laying down the foundations of Python, while the later chapters delve into more complex concepts related to deep learning and data science methodologies. Readers found the book's explanations of algorithms, data collection, organization, and statistical analysis to be particularly concise yet comprehensive. The inclusion of practical exercises at the end of each chapter also enhances learning, allowing readers to test their understanding in a hands-on way. This aspect of the book has been highlighted as beneficial for students and self-learners alike, as well as for educational settings like classrooms. However, some customers noted a significant downside regarding the book's presentation, specifically the black-and-white print of graphs, which diminishes their clarity and interpretability. Despite this, the overall Consensus is that the book serves as a valuable resource for anyone interested in the intersections of Python and data science—transforming readers from novice programmers to more competent practitioners and enriching their understanding of the field as a whole. **
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Pros
- Clear and concise explanations suitable for beginners and intermediates.
- Structured content that introduces Python fundamentals effectively.
- Comprehensive coverage of data science methods and algorithms.
- Practical exercises for hands-on experience.
- Positive impact on user interest in data science and programming.
Cons
- Graphs printed in black and white, making them challenging to interpret.
Product Price History
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Features & Benefits
- Transform unstructured data into organized datasets.
- Ideal for graduate and undergraduate students in data science.
- Helpful for instructors and entry-level professionals transitioning from R to Python.
- Includes advanced topics like machine learning, network analysis, and statistics.
- Quick reference format allows for easy navigation and learning.
- Master essential Python functions crucial for data science success.
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