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Beginning data science in R [electronic resource] : data analysis, visualization, and modelling for the data scientist
| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000045992264 | |
| 005 | 20190805140551 | |
| 006 | m d | |
| 007 | cr | |
| 008 | 190726s2017 caua ob 001 0 eng d | |
| 020 | ▼a 9781484226704 | |
| 020 | ▼a 9781484226711 (eBook) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 050 | 4 | ▼a QA76.9.B45 |
| 082 | 0 4 | ▼a 001.42 ▼2 23 |
| 084 | ▼a 001.42 ▼2 DDCK | |
| 090 | ▼a 001.42 | |
| 100 | 1 | ▼a Mailund, Thomas. |
| 245 | 1 0 | ▼a Beginning data science in R ▼h [electronic resource] : ▼b data analysis, visualization, and modelling for the data scientist / ▼c Thomas Mailund. |
| 260 | ▼a Berkeley, CA : ▼b Apress, ▼c 2017. | |
| 300 | ▼a 1 online resource (xxvii, 352 p.) : ▼b ill. | |
| 500 | ▼a Title from e-Book title page. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 505 | 0 | ▼a 1. Introduction to R programming -- 2. Reproducible analysis -- 3. Data manipulation -- 4. Visualizing and exploring data -- 5. Working with large data sets -- 6. Supervised learning -- 7. Unsupervised learning -- 8. More R programming -- 9. Advanced R programming -- 10. Object oriented programming -- 11. Building an R package -- 12. Testing and checking -- 13. Version control -- 14. Profiling and optimizing. |
| 520 | ▼a Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. You will: Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code. | |
| 530 | ▼a Issued also as a book. | |
| 538 | ▼a Mode of access: World Wide Web. | |
| 650 | 0 | ▼a Computer science. |
| 650 | 0 | ▼a Quantitative research. |
| 650 | 0 | ▼a Big data. |
| 856 | 4 0 | ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-1-4842-2671-1 |
| 945 | ▼a KLPA | |
| 991 | ▼a E-Book(소장) |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/e-Book 컬렉션/ | 청구기호 CR 001.42 | 등록번호 E14016116 | 도서상태 대출불가(열람가능) | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.
Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.
This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming.
What You Will Learn
- Perform data science and analytics using statistics and the R programming language
- Visualize and explore data, including working with large data sets found in big data
- Build an R package
- Test and check your code
- Practice version control
- Profile and optimize your code
Who This Book Is For
Those with some data science or analytics background, but not necessarily experience with the R programming language.
New feature
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.
Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.
This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming.
You will:
- Perform data science and analytics using statistics and the R programming language
- Visualize and explore data, including working with large data sets found in big data
- Build an R package
- Test and check your code
- Practice version control
- Profile and optimize your code
정보제공 :
목차
1. Introduction to R programming. 2. Reproducible analysis. 3. Data manipulation. 4. Visualizing and exploring data. 5. Working with large data sets. 6. Supervised learning. 7. Unsupervised learning. 8. More R programming. 9. Advanced R programming. 10. Object oriented programming. 11. Building an R package. 12. Testing and checking. 13. Version control. 14. Profiling and optimizing.
정보제공 :