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Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)
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PKR 23633
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Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.
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- Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R.Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars.Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.
| Publisher | Wiley |
| Publication date | April 9, 2019 |
| Edition | 1st |
| Language | English |
| Print length | 256 pages |
| ISBN-10 | 1119526817 |
| ISBN-13 | 978-1119526810 |
| Item Weight | 15.7 ounces (445.1 grams) |
| Dimensions | 6.24 x 0.77 x 9.24 inches (15.8 x 2 x 23.5 cm) |
| Part of series | Wiley Series on Methods and Applications in Data Mining |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners aiming to understand the foundational concepts of data science using Python and R.
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Students and Academics
Perfect for university students or researchers needing a comprehensive resource for data mining methodologies.
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Professionals Transitioning
Suitable for professionals seeking to switch careers to data science, providing practical tools and techniques.
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Advanced Practitioners
Not ideal for experienced data scientists looking for cutting-edge techniques and advanced applications.
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Python Editorial Review
Data Science Using Python and R is a book that provides an example-oriented approach and is well detailed, covering various data science topics. However, the book is only a couple of hundred pages and is printed on cheap paper. Difficult steps are glossed over, such as installing necessary packages to generate output. Additionally, the website to download the datasets mentioned in the book no longer exists.
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Pros
- Example-oriented approach
- Well-detailed
- Code snippets provided
Cons
- Printed on cheap paper
Product Price History
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PKR 23633
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Features & Benefits
- Learn data science using Python and R.
- The book provides step-by-step instructions and walk-throughs for solving data science problems.
- It covers several topics including data preparation, exploratory data analysis, decision trees, regression modeling, and more.
- It emphasizes data-driven error costs to enhance profitability.
- It includes over 500 exercises and Hands-on Analysis exercises to challenge readers.
- Recommended for the general reader with no previous analytics or programming experience.
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