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Item #:
25572311
Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series)
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Item #:
25572311
ETB 15477
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What Stands Out
Comprehensive Coverage
This book offers extensive coverage of data analysis and predictive algorithms, equipping readers with the essential tools needed to tackle real-world data science challenges using R.
Practical Examples
Featuring practical, hands-on examples, readers will apply theoretical concepts to real datasets, enhancing their learning experience and preparing them for data-driven decision-making.
Expert Insights
Authored by industry experts, this guide provides valuable insights into best practices in data science, ensuring readers gain a strong foundation in both statistical analysis and data prediction.
Product Details
| Publisher | Chapman and Hall/CRC |
| Publication date | November 8, 2019 |
| Edition | 1st |
| Language | English |
| Print length | 713 pages |
| ISBN-10 | 0367357984 |
| ISBN-13 | 978-0367357986 |
| Item Weight | 3.65 pounds (1.66 kg) |
| Dimensions | 7.25 x 1 x 10 inches (18.4 x 2.5 x 25.4 cm) |
| Part of series | Chapman & Hall/CRC Data Science |
Who Should Buy?
-
Aspiring Data Scientists
Individuals looking to build foundational skills in data analysis and predictive modeling using R programming.
-
Students in Academia
University students enrolled in data science or statistics courses needing a comprehensive guide to practical skills.
-
Professionals Transitioning
Career changers seeking to enhance their analytical skills for roles requiring data-driven decision making.
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Absolute Beginners
Those with no prior experience in programming or statistics may find the material challenging to comprehend.
Product Description
Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series)
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Machine Theory Editorial Review
The printed edition of the Harvard class offers a comprehensive and detailed guide to using R for data science. It is considered a valuable teaching tool with exceptional examples. However, there have been some reports of damaged books upon delivery.
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Pros
- Comprehensive and detailed guide to using R for data science
- Valuable teaching tool
- Exceptional examples
Cons
- Reports of damaged books upon delivery
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