$12.24
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman & Hall/CRC: The R Series)—
$12.24
The Story
Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions.
The books collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including:
Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency
Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data.
Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers computational reasoning of real-world data analyses.
ASIN: 1482234815
VSKU: BVV.1482234815.G
Condition: Good
Author/Artist:Nolan, Professor in the Department of Statistics Deborah|Lang, Duncan Temple
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.
ASIN: 1482234815
VSKU: BVV.1482234815.G
Condition: Good
Author/Artist:Nolan, Professor in the Department of Statistics Deborah|Lang, Duncan Temple
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.
Description
Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions.
The books collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including:
Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency
Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data.
Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers computational reasoning of real-world data analyses.
ASIN: 1482234815
VSKU: BVV.1482234815.G
Condition: Good
Author/Artist:Nolan, Professor in the Department of Statistics Deborah|Lang, Duncan Temple
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.
ASIN: 1482234815
VSKU: BVV.1482234815.G
Condition: Good
Author/Artist:Nolan, Professor in the Department of Statistics Deborah|Lang, Duncan Temple
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.
