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Item #:
34868663
Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools
Item #:
34868663
TMT 89
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What Stands Out
Practical Approach
Focuses on the essential 80% of data cleaning processes, equipping readers with hands-on techniques and tools necessary for effective data management in real-world scenarios.
Multi-Tool Integration
Covers Python, R, and command-line tools, ensuring a versatile skill set for data scientists to choose the best tool for their specific cleaning needs.
Comprehensive Guidance
Provides thorough insights into data cleaning, addressing common challenges and offering practical solutions that enhance the quality and usability of datasets for analysis.
Product Details
| Publisher | Packt Publishing |
| Publication date | March 31, 2021 |
| Language | English |
| Print length | 498 pages |
| ISBN-10 | 1801071292 |
| ISBN-13 | 978-1801071291 |
| Item Weight | 1.87 pounds (850 grams) |
| Dimensions | 7.5 x 1.13 x 9.25 inches (19.1 x 2.9 x 23.5 cm) |
Who Should Buy?
-
Data Analysts
Analyzing large datasets effectively requires cleaning skills, making this guide invaluable for data analysts.
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Novice Data Scientists
Beginners will benefit from structured cleaning techniques that are essential for successful data science projects.
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Academics
Researchers needing to preprocess data for analysis can enhance their skills through practical examples and code implementations.
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Experienced PhDs
Advanced data scientists may find content too basic and not suitable for their high-level requirements.
Product Description
Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools
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Machine Theory Editorial Review
"Cleaning Data for Effective Data Science" is a highly recommended book for anyone interested in data science or data analysis. The author, Dr. Mertz, presents the material in an engaging and informative manner, emphasizing the mindset and thought process behind data cleaning rather than focusing on rote memorization of code. The book is particularly useful for those who are new to data science, as it provides a great introduction to the subject matter. Even individuals with a background in data science can benefit from the book, as it serves as a valuable review of tools and methods for cleaning and preparing data. The book covers a wide range of topics, including data formats, encoding, cleaning, and feature selection. The author provides numerous code examples that can be used as templates by readers. These examples highlight how much can be accomplished with just a few lines of code, which is encouraging for those who may be new to certain tools such as Pandas and UNIX shell scripting. Readers appreciate that the book is well-written and easily understandable, making it accessible to a wide range of individuals, including those who may not have a strong background in data science. The author's engaging writing style helps to contextualize the information presented, keeping readers engaged throughout. Overall, "Cleaning Data for Effective Data Science" is highly recommended for anyone looking to expand their knowledge and skills in data cleaning and preparation. Whether you are a data scientist or someone interested in NLP, this book provides a valuable overview of common issues and techniques in the field of data analysis.
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Pros
- Engaging and informative writing style
- Emphasizes mindset and thought process over rote memorization of code
- Useful for beginners and experienced data scientists alike
- Covers a wide range of topics
- Provides code examples for practical application
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