Exploratory Data Analysis Using R
Sorozatcím: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series;
-
10% KEDVEZMÉNY?
- Kiadói listaár GBP 94.99
-
45 381 Ft (43 220 Ft + 5% áfa)
Az ár azért becsült, mert a rendelés pillanatában nem lehet pontosan tudni, hogy a beérkezéskor milyen lesz a forint árfolyama az adott termék eredeti devizájához képest. Ha a forint romlana, kissé többet, ha javulna, kissé kevesebbet kell majd fizetnie.
- Kedvezmény(ek) 10% (cc. 4 538 Ft off)
- Kedvezményes ár 40 843 Ft (38 898 Ft + 5% áfa)
40 843 Ft
Beszerezhetőség
Még nem jelent meg, de rendelhető. A megjelenéstől számított néhány héten belül megérkezik.
Why don't you give exact delivery time?
A beszerzés időigényét az eddigi tapasztalatokra alapozva adjuk meg. Azért becsült, mert a terméket külföldről hozzuk be, így a kiadó kiszolgálásának pillanatnyi gyorsaságától is függ. A megadottnál gyorsabb és lassabb szállítás is elképzelhető, de mindent megteszünk, hogy Ön a lehető leghamarabb jusson hozzá a termékhez.
A termék adatai:
- Kiadás sorszáma 2
- Kiadó Chapman and Hall
- Megjelenés dátuma 2026. május 26.
- ISBN 9781032814810
- Kötéstípus Keménykötés
- Terjedelem592 oldal
- Méret 234x156 mm
- Súly 453 g
- Nyelv angol
- Illusztrációk 74 Illustrations, black & white; 35 Illustrations, color; 74 Line drawings, black & white; 35 Line drawings, color; 3 Tables, black & white 700
Kategóriák
Rövid leírás:
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA), and this revised edition is accompanied by the R package ExploreTheData that implements many of the approaches described. The focus is the use of R to explore and explain datasets and the analysis results derived from them.
TöbbHosszú leírás:
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA), and this revised edition is accompanied by the R package ExploreTheData that implements many of the approaches described. As before, the primary focus of the book is on identifying "interesting" features - good, bad, and ugly - in a dataset, why it is important to find them, how to treat them, and more generally, the use of R to explore and explain datasets and the analysis results derived from them.
The book begins with a brief overview of exploratory data analysis using R, followed by a detailed discussion of creating various graphical data summaries in R. Then comes a thorough introduction to exploratory data analysis, and a detailed treatment of 13 data anomalies, why they are important, how to find them, and some options for addressing them. Subsequent chapters introduce the mechanics of working with external data, structured query language (SQL) for interacting with relational databases, linear regression analysis (the simplest and historically most important class of predictive models), and crafting data stories to explain our results to others. These chapters use R as an interactive data analysis platform, while Chapter 9 turns to writing programs in R, focusing on creating custom functions that can greatly simplify repetitive analysis tasks. Further chapters expand the scope to more advanced topics and techniques: special considerations for working with text data, a second look at exploratory data analysis, and more general predictive models.
The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. It keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.
TöbbTartalomjegyzék:
1. Data, Exploratory Analysis, and R 2. Graphics in R 3. Exploratory Data Analysis: A First Look 4. Thirteen Important Data
Anomalies 5. Working with External Data 6. SQL and Relational Databases 7. Linear Regression Models 8. Crafting Data Stories 9. Programming in R 10. Working with Text Data 11. Exploratory Data Analysis: A Second Look 12. More General Predictive Models
