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E-raamat: Data Science for Business With R

(Syracuse University, USA), (Syracuse University, USA)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Feb-2021
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781544370477
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Feb-2021
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781544370477

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Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available.

Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field.

Instructor Preface xiii
Teaching Resources xv
Introduction: Data Science, Many Skills xvii
What Is Data Science? xviii
The Steps in Doing Data Science xix
The Skills Needed to Do Data Science xx
Identifying Data Problems xxii
Additional Introductory Thoughts xxv
Case Study Overview: Customer Churn in the Airline Industry xxvii
Net Promoter Score xxviii
Southeast and Its Regional Airline Partners xxviii
The Data Available xxix
Attribute Names xxix
Chapter Challenges xxxii
Sources xxxii
Chapter 1 Begin at the Beginning With R
1(20)
Installing R
3(1)
Using R
4(1)
Creating and Using Vectors
5(3)
Subsetting Vectors
8(2)
The Command Console
10(1)
Using an Integrated Development Environment
11(1)
Installing RStudio
12(1)
Creating R Scripts
13(4)
Case Study: Calculating NPS
17(2)
Chapter Challenges
19(1)
Sources
19(1)
R Functions Used in This
Chapter
19(2)
Chapter 2 Rows and Columns
21(18)
Creating Dataframes
24(3)
Exploring Dataframes
27(4)
Accessing Columns in a Dataframe
31(3)
Case Study: Calculating NPS Using a Dataframe
34(3)
Chapter Challenges
37(1)
Sources
37(1)
R Functions Used in This
Chapter
38(1)
Chapter 3 Data Munging
39(22)
Reading a CSV Text File
40(4)
Removing Rows and Columns
44(2)
Renaming Rows and Columns
46(1)
Cleaning up the Elements
47(2)
Sorting and Subsetting Dataframes
49(2)
Tidyverse: An Introduction and How to Install the Package
51(2)
Sorting and Subsetting Dataframes Using Tidyverse
53(2)
Case Study: Reading, Cleaning, and Exploring a Survey Dataset
55(4)
Chapter Challenges
59(1)
Sources
60(1)
R Functions Used in This
Chapter
60(1)
Chapter A What's My' Function?
61(18)
Why Create and Use Functions?
62(1)
Creating Functions in R
63(5)
Defensive Coding
68(2)
Installing a Package to Access a Function
70(2)
Case Study: Creating and Using a Calculate NPS Function
72(4)
Chapter Challenges
76(1)
Sources
76(1)
R Functions Used in This
Chapter
77(2)
Chapter 5 Beer, Farms, Peas, and the Use of Statistics
79(18)
Historical Perspective
80(2)
Sampling a Population
82(1)
Understanding Descriptive Statistics
82(2)
Using Descriptive Statistics
84(4)
Using Histograms to Understand a Distribution
88(3)
Normal Distributions
91(1)
Case Study: Exploring LTR Distributions
92(3)
Chapter Challenges
95(1)
Sources
95(1)
R Functions Used in This
Chapter
96(1)
Chapter 6 Sample in a Jar
97(22)
Sampling in R
100(1)
Repeating our Sampling
101(2)
Law of Large Numbers and the Central Limit Theorem
103(4)
Comparing Two Samples
107(5)
Case Study: Analyzing the Impact of a New Treatment
112(4)
Chapter Challenges
116(1)
Sources
116(1)
R Functions Used in This
Chapter
117(2)
Chapter 7 Storage Wars
119(34)
Importing Data Using RStudio
121(3)
Accessing Excel Data
124(5)
Working with Data From External Databases
129(1)
Accessing a Database
130(5)
Comparing SQL and R/Tidyverse for Accessing a Dataset
135(4)
Accessing JSON Data
139(6)
Case Study: Reading, Cleaning, and Exploring a Survey Dataset
145(5)
Chapter Challenges
150(1)
Sources
151(1)
R Functions Used in This
Chapter
151(2)
Chapter 8 Pictures Versus Numbers
153(28)
A Visualization Overview
155(2)
Basic Plots in R
157(1)
Using the ggplot2 Package
158(8)
More-Advanced Visualizations
166(5)
Case Study: Visualizing Key Attributes Related to NPS
171(8)
Chapter Challenges
179(1)
Sources
179(1)
R Functions Used in This
Chapter
180(1)
Chapter 9 Map Mashup
181(26)
Creating Map Visualizations With ggplot2
183(9)
Showing Points on a Map
192(6)
Zooming Into a Subset of a Map
198(2)
Case Study: Explore NPS by State and City
200(4)
Chapter Challenges
204(1)
Sources
204(1)
R Functions Used in This
Chapter
205(2)
Chapter 10 Lining Up Our Models
207(32)
What Is a Model?
208(1)
Supervised and Unsupervised Machine Learning
208(2)
Linear Modeling
210(2)
An Example--Car Maintenance
212(9)
Using the Caret Package
221(2)
Partitioning into Training and Cross Validation Datasets
223(5)
Using k-fold Cross Validation
228(3)
Case Study: Building a Linear Model Using Survey Data
231(5)
Chapter Challenges
236(1)
Sources
236(1)
R Functions Used in This
Chapter
237(2)
Chapter 11 What's Your Vector, Victor?
239(38)
More Supervised Learning
240(1)
A Classification Example
240(7)
Supervised Learning via Support Vector Machines
247(3)
Support Vector Machines in R
250(11)
Supervised Learning via Classification and Regression Trees
261(5)
Case Study: Building Supervised Models From the Survey
266(8)
Chapter Challenges
274(1)
Sources
274(1)
R Functions Used in This
Chapter
275(2)
Chapter 12 Hi Ho, Hi Ho--Data Mining We Go
277(26)
Data Mining Processes
279(1)
Association Rules Data
280(1)
Association Rules Mining
281(6)
Exploring How the Association Rules Algorithm Works
287(1)
Building Association Rules in R
288(7)
Case Study: Exploring Association Rules Within the Survey
295(5)
Chapter Challenges
300(1)
Sources
301(1)
R Functions Used in This
Chapter
301(2)
Chapter 13 Word Perfect (Text Mining)
303(32)
Reading-In Text Files
305(2)
Creating Word Clouds Using the Quanteda Package
307(4)
Exploring the Text via Sentiment Analysis
311(3)
Topic Modeling
314(4)
Other Uses of Text Mining
318(1)
Case Study: Connecting Topics to NPS
319(13)
Chapter Challenges
332(1)
Sources
332(1)
R Functions Used in This
Chapter
333(2)
Chapter 14 Shiny Web Apps
335(18)
Creating Web Applications in R
336(5)
Deploying the Application
341(6)
Case Study: Visualizing NPS by Key Attributes
347(4)
Chapter Challenges
351(1)
Sources
351(1)
R Functions Used in This
Chapter
351(2)
Chapter 15 Time for a Deep Dive
353(32)
The Impact of Deep Learning
354(1)
Deep Learning Is Supervised Learning
355(1)
How Does Deep Learning Work?
356(2)
Deep Learning in R--An Example
358(7)
Deep Learning in R--An Image Analysis Example
365(9)
Deep Learning in R--Using a Prebuilt Model
374(4)
Case Study: Building Neural Networks From the Survey
378(3)
Chapter Challenges
381(1)
Sources
382(1)
R Functions Used in This
Chapter
383(2)
Index 385
Jeffrey S. Saltz is an Associate Professor at Syracuse University in the School of Information Studies and Director of the schools Masters of Science program in Applied Data Science. His research and teaching focus on helping organizations leverage information technology and data for competitive advantage. Specifically, his current research focuses on the socio-technical aspects of data science projects, such as how to coordinate and manage data science teams. In order to stay connected to the real world, Dr. Saltz consults with clients ranging from professional football teams to Fortune 500 organizations. Prior to becoming a professor, Dr. Saltzs two decades of industry experience focused on leveraging emerging technologies and data analytics to deliver innovative business solutions. In his last corporate role, at JPMorgan Chase, he reported to the firms Chief Information Officer and drove technology innovation across the organization. Jeff also held several other key technology management positions at the company, including CTO and Chief Information Architect. He also served as Chief Technology Officer and Principal Investor at Goldman Sachs, where he helped incubate technology start-ups. He started his career as a programmer, project leader and consulting engineer with Digital Equipment Corp. Dr. Saltz holds a B.S. degree in computer science from Cornell University, an M.B.A. from The Wharton School at the University of Pennsylvania, and a PhD in Information Systems from the New Jersey Institute of Technology.

Jeffrey M. Stanton, Ph.D. is a Professor at Syracuse University in the School of Information Studies. Dr. Stantons research focuses on the impacts of machine learning on organizations and individuals. He is the author of Reasoning with Data (2017), an introductory statistics textbook. Stanton has also published many scholarly articles in peer-reviewed behavioral science journals, such as the Journal of Applied Psychology, Personnel Psychology, and Human Performance. His articles also appear in Journal of Computational Science Education, Computers and Security, Communications of the ACM, Computers in Human Behavior, the International Journal of Human-Computer Interaction, Information Technology and People, the Journal of Information Systems Education, the Journal of Digital Information, Surveillance and Society, and Behaviour & Information Technology. He also has published numerous book chapters on data science, privacy, research methods, and program evaluation.  Dr. Stantons research has been supported through 19 grants and supplements including the National Science Foundations CAREER award. Before getting his PhD, Stanton was a software developer who worked at startup companies in the publishing and professional audio industries. He holds a bachelors degree in Computer Science from Dartmouth College, and a masters and Ph.D. in Psychology from the University of Connecticut.