語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Beginning data science in R = data a...
~
Mailund, Thomas.
FindBook
Google Book
Amazon
博客來
Beginning data science in R = data analysis, visualization, and modelling for the data scientist /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Beginning data science in R/ by Thomas Mailund.
其他題名:
data analysis, visualization, and modelling for the data scientist /
作者:
Mailund, Thomas.
出版者:
Berkeley, CA :Apress : : 2017.,
面頁冊數:
xxvii, 352 p. :ill., digital ;24 cm.
內容註:
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.
Contained By:
Springer eBooks
標題:
Quantitative research. -
電子資源:
http://dx.doi.org/10.1007/978-1-4842-2671-1
ISBN:
9781484226711
Beginning data science in R = data analysis, visualization, and modelling for the data scientist /
Mailund, Thomas.
Beginning data science in R
data analysis, visualization, and modelling for the data scientist /[electronic resource] :by Thomas Mailund. - Berkeley, CA :Apress :2017. - xxvii, 352 p. :ill., digital ;24 cm.
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.
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.
ISBN: 9781484226711
Standard No.: 10.1007/978-1-4842-2671-1doiSubjects--Topical Terms:
919734
Quantitative research.
LC Class. No.: Q180.55.Q36 / M35 2017
Dewey Class. No.: 001.42
Beginning data science in R = data analysis, visualization, and modelling for the data scientist /
LDR
:02602nmm a2200325 a 4500
001
2093183
003
DE-He213
005
20170309142020.0
006
m d
007
cr nn 008maaau
008
171117s2017 cau s 0 eng d
020
$a
9781484226711
$q
(electronic bk.)
020
$a
9781484226704
$q
(paper)
024
7
$a
10.1007/978-1-4842-2671-1
$2
doi
035
$a
978-1-4842-2671-1
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q180.55.Q36
$b
M35 2017
072
7
$a
UNF
$2
bicssc
072
7
$a
UYQE
$2
bicssc
072
7
$a
COM021030
$2
bisacsh
082
0 4
$a
001.42
$2
23
090
$a
Q180.55.Q36
$b
M222 2017
100
1
$a
Mailund, Thomas.
$3
3227792
245
1 0
$a
Beginning data science in R
$h
[electronic resource] :
$b
data analysis, visualization, and modelling for the data scientist /
$c
by Thomas Mailund.
260
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2017.
300
$a
xxvii, 352 p. :
$b
ill., digital ;
$c
24 cm.
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.
650
0
$a
Quantitative research.
$3
919734
650
1 4
$a
Computer Science.
$3
626642
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
898250
650
2 4
$a
Big Data.
$3
3134868
650
2 4
$a
Programming Languages, Compilers, Interpreters.
$3
891123
650
2 4
$a
Data-driven Science, Modeling and Theory Building.
$3
2210495
650
2 4
$a
Programming Techniques.
$3
892496
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer eBooks
856
4 0
$u
http://dx.doi.org/10.1007/978-1-4842-2671-1
950
$a
Professional and Applied Computing (Springer-12059)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9317557
電子資源
11.線上閱覽_V
電子書
EB Q180.55.Q36 M35 2017
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入