Data science & big data analytics: discovering, analyzing, visualizing and presenting data
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Indianapolis, IN
Wiley
2015
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XVIII, 410 Seiten Illustrationen, Diagramme |
| ISBN: | 9781118876138 |
Staff View
MARC
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| 020 | |a 9781118876138 |c hbk |9 978-1-118-87613-8 | ||
| 035 | |a (OCoLC)905378487 | ||
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| 245 | 1 | 0 | |a Data science & big data analytics |b discovering, analyzing, visualizing and presenting data |c EMC Education Services |
| 246 | 1 | 3 | |a Data science and big data analytics |
| 264 | 1 | |a Indianapolis, IN |b Wiley |c 2015 | |
| 300 | |a XVIII, 410 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-118-87622-0 |
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Record in the Search Index
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| DE-BY-FWS_katkey | 648079 |
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| adam_text |
Contents
Introduction
.xvii
Chapter
1 ·
Introduction to Big Data Analytics
.1
1.1
Big Data Overview
.2
7.7.
J
Data Structures
.5
1.1.2
Analyst Perspective on Data Repositories
.9
1.2
State of the Practice in Analytics
. 11
1.2.7
Bl Versus Data Science
.12
1.2.2
Current Analytical Architecture
.13
1.2.3
Drivers of Big Data
.15
1.2.4
Emerging Big Data Ecosystem and a New Approach to Analytics
.16
1.3
Key Roles for the New Big Data Ecosystem
. 19
1.4
Examples of Big Data Analytics
.22
Summary
.23
Exercises
.23
Bibliography
.24
Chapter
2 ·
Data Analytics Lifecycle
.25
2.1
Data Analytics
Liŕecycle
Overview
.26
2.1.1
Key Roles for a Successful Analytics Project
.26
2.1.2
Background and Overview of Data Analytics Lifecycle
.28
2.2
Phase
1:
Discovery
.30
2.2.1
Learning the Business Domain
.30
2.2.2
Resources
.31
2.2.3
Framing the Problem
.32
2.2.4
Identifying Key Stakeholders
.33
2.2.5
Interviewing the Analytics Sponsor
.33
2.2.6
Developing Initial Hypotheses
.35
2.2.7
Identifying Potential Data Sources
.35
2.3
Phase
2:
Data Preparation
.36
2.3.7
Preparing the Analytic Sandbox
.37
2.3.2
Performing ETLT
.38
2.3.3
Learning About the Data
.39
2.3.4
Data Conditioning
.40
2.3.5
Survey and Visualize
.
4Î
2.3.6
Common Tools for the Data Preparation Phase
.42
2
A Phase
3:
Model Planning
.42
2.4.1
Data Exploration and Variable Selection
.44
2.4.2
Model Selection
.45
2.4.3
Common Tools for the Model Planning Phase
.45
CONTENTS
2.5 Phase 4: Model Building.46
2.5.1
Common
Tools
for the
Model Building Phase.48
2.6 Phase 5:
Communicate Results.
49
2.7 Phase 6: Operationalize.50
2.8
Case Study:
Global Innovation Network
and Analysis (GINA).
53
2.8.1 Phase 1: Discovery.54
2.8.2 Phase 2: Data
Preparation
.55
2.8.3 Phase 3:
Model Planning.
56
2.8.4 Phase 4: Model Building.56
2.8.5 Phase 5:
Communicate Results.
58
2.8.6 Phase
б:
Operationalize.59
Summary.
60
Exercises
.61
Bibliography
. 61
Chapter
3 ·
Review of Basic Data Analytic Methods Using
R
.63
3.1
Introduction to
R
. 64
3.1.1
R
Graphical User Interfaces
.
67
3.1.2
Data Import and Export.
. $
3.1.3
Attribute and Data Types
.
^
3.1.4
Descriptive Statistics
. .
3.2
Exploratory Data Analysis
. .
80
3.2.1
Visualization Before Analysis
. .
82
3.2.2
Dirty Data
.'.'.'.'.'.'.','.'.'.
85
3.2.3
Visualizing a Single Variable
.
3.2.4
Examining Multiple Variables
. .«,
3.2.5
Data Exploration Versus Presentation
. .
gg
3.3
Statistical Methods for Evaluation.
··· ■ .
Ill HypotheshTesting
.'"'"".
°!
3.3.2
Difference of Means
. .
3.3.3
Wikoxon Rank-Sum Test
. ·■■■-·■■·■.
W4
UATypelonàTypellErrm
.
.'.'.Z.
.'.
Ш
3.3.5
Power andSample Size.
· ./09
3.3.6ANWA
.'.'.'.'."".
m
Summary
. .
™
Exercises
.' ·■■·.
114
Bibliography
. . .
W
.115
Chapter
4 .
Advanced Analytical Theory and Methods: Clustering 117
UOvwviewofCkistering.
.
Ir/
«JK-means
. .118
4
JJ Determining the Number of
austeri.
.
Ш
4J-4Diognostia
. .123
".128
CONTENTS
4.2.5
Reasons to Choose
ond
Cautions
.130
4.3
Additional Algorithms
.134
Summary
.135
Exercises
.135
Bibliography
.136
Chapter
5 ·
Advanced Analytical Theory and Methods: Association Rules
.137
5.1
Overview
.138
5.2
Apriori
Algorithm
.140
5.3
Evaluation of Candidate Rules
.141
5.4
Applications of Association Rules
.143
5.5
An Example: Transactions in a Grocery Store
.143
5.5.
J
The Groceries
Dataset
.144
5.5.2
Frequent
Hemset
Generation
.146
5.5.3
Rule Generation and Visualization
.152
5.6
Validation and Testing
.157
5.7
Diagnostics
.158
Summary
.158
Exercises
.159
Bibliography
.160
Chapter
6 ·
Advanced Analytical Theory and Methods: Regression
.161
6.1
Linear Regression
.162
6.1.1
UseCases
.162
6.1.2
Model Description
.163
6.1.3
Diagnostics
.173
6.2
Logistic Regression
.178
6.2.1
UseCases
.179
6.2.2
Model Description
.179
6.2.3
Diagnostics
.181
6.3
Reasons to Choose and Cautions
.188
6.4
Additional Regression Models
.189
Summary
.190
Exercises
.190
Chapter
7 ·
Advanced Analytical Theory and Methods: Classification
.191
7.1
Decision Trees
.192
7.1.1
Overview of a Decision Tree
.193
7.1.2
The General Algorithm
.197
7.1.3
Decision Tree Algorithms
.203
7.1.4
Evaluating a Decision Tree
.204
7.1.5
Decision Trees in
H
.206
7.2
Naïve Bayes
.211
7.2.1
Bayes'
Theorem
.212
7.2.2
Naïve Bayes
Classifier.
214
CONTfNTS
.277
7.23
Smoothing
.
^
7.2.4
Diagnostics
.■■··■·. ■
7.2.5
Naïve Bayes
in R
.
7.3
Diagnostics
of Classifiers
.
7.4
Additional Classification Methods
.
Summary
.
.^
Exercises
.
Bibliography
.
Chapter
8 ·
Advanced Analytical Theory and Methods: Time Series Analysis
.233
8.1
Overview of Time Series Analysis
.
234
8.1.1
Box-Jenkins Methodology
.
^
8.2
ARIMA Model
.
Ш
1.2.]
Autocorrelation Function (ACF)
.
Ľ(>
8.2.2
Autoregressive Models
.%&
8.23
Moving Average Models
.
^
8.2.4
ARMA
and ARIMA Models
.
241
8.2.5
Building and Evaluating an ARIMA Model
.
244
8.2.6
Reasons to Choose and Cautions
.
^
8.3
Additional Methods
.253
Summary
.254
Exercises
.254
Chapter
9 ·
Advanced Analytical Theory and Methods: Text Analysis
.255
9.1
Text Analysis Steps
.257
9.2
A Text Analysis Example
.259
9.3
Collecting Raw Text
.260
9.4
Representing Text
.264
9.5
Term Frequency—Inverse Document Frequency (TFIDF)
.269
9.6
Categorizing Documents by Topics
.274
9.7
Determining Sentiments
.277
9.8
Gaining Insights
.283
Summary
. 290
Exercises
. .290
Bibliography
. 291
Chapter
10 .
Advanced Analytics—Technology and Tools: MapReduce and Hadoop
.295
10.1
Analytics for Unstructured Data
. 296
lOLM
üst
Cases
.'."////.!!!!!.'.'.'.'.'.'.'.'.'.'.'.'.".'.". """"""".['.296
ìOUMopRcduce
.'.'.[.["." '""""" "" """" 298
lOXÌApocheHoooop
.
ľ.!"!"!!^'" ľ
300
Ш
The Hadoop Ecosystem
. .
ш
*
■"ľz:;:::;:;::::;:;:::;:;;:;;
.
j
*****
ìOJJHBase.
CONTENTS
10.3NoSQL.322
Summary
.323
Exercises
.324
Bibliography
.324
Chapter
11 ·
Advanced Analytics—Technology and Tools: In-Database Analytics
.327
11.1
SQL Essentials
.328
11.1.1
Joins
.330
11.1.2
Set Operations
.332
11.1.3
Grouping Extensions
.334
11.2
In-Database Text Analysis
.338
11.3
Advanced SQL
.343
//.3./
Window Functions
.343
11.3.2
User-Defined Functions and Aggregates
.347
11.3.3
Ordered Aggregates
.351
11.3.4
MADlib
.352
Summary
.356
Exercises
.356
Bibliography
.357
Chapter
12 ·
The Endgame, or Putting It All Together
.359
12.1
Communicating and Operationalizing an Analytics Project
.360
12.2
Creating the Final Deliverables
.362
12.2.1
Developing Core Material for Multiple Audiences
.364
12.2.2
Project Goals
.365
12.2.3
Main Findings
.367
12.2.4
Approach
.369
12.2.5
Model Description
.371
12.2.6
Key Points Supported with Data
.372
12.2.7
Model Details
.372
12.2.8
Recommendations
.374
12.2.9
Additional Tips on Final Presentation
.375
12.2.10
Providing Technical Specifications andCode
.376
12.3
Data Visualization Basics
.377
12.3.1
Key Points Supported with Data
.378
12.3.2
Evolution of a Graph
.380
12.3.3
Common Representation Methods
.386
12.3.4
Ho
w
to Clean Up a Graphic
.387
12.3.5
Additional Considerations
.392
Summary
.393
Exercises
.394
References and Further Reading
.394
Bibliography
.394
Index
.
Data Science and Big Data Analytics
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book
covers the breadth of activities, methods and tools that Data Scientists use. The content focuses
on concepts, principles and practical applications that are relevant to any industry and technology
environment, and the learning is supported and explained with illustrative examples using open-source
software.
• Become a contributor on a data science team
• Deploy a structured lifecycle approach to data analytics problems
• Apply appropriate analytic techniques and tools to analyze big data
• Learn how to tell a compelling story with data to drive business action
• Prepare for EMC Proven™ Professional Data Scientist certification |
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| id | DE-604.BV042050382 |
| illustrated | Illustrated |
| indexdate | 2025-04-03T04:00:43Z |
| institution | BVB |
| institution_GND | (DE-588)1034947737 |
| isbn | 9781118876138 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027491485 |
| oclc_num | 905378487 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-11 DE-1050 DE-473 DE-BY-UBG DE-29T DE-384 DE-1102 DE-M347 DE-B768 DE-29 DE-739 DE-Aug4 DE-92 DE-91 DE-BY-TUM DE-525 DE-862 DE-BY-FWS |
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| physical | XVIII, 410 Seiten Illustrationen, Diagramme |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Wiley |
| record_format | marc |
| spellingShingle | Data science & big data analytics discovering, analyzing, visualizing and presenting data Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4428654-5 |
| title | Data science & big data analytics discovering, analyzing, visualizing and presenting data |
| title_alt | Data science and big data analytics |
| title_auth | Data science & big data analytics discovering, analyzing, visualizing and presenting data |
| title_exact_search | Data science & big data analytics discovering, analyzing, visualizing and presenting data |
| title_full | Data science & big data analytics discovering, analyzing, visualizing and presenting data EMC Education Services |
| title_fullStr | Data science & big data analytics discovering, analyzing, visualizing and presenting data EMC Education Services |
| title_full_unstemmed | Data science & big data analytics discovering, analyzing, visualizing and presenting data EMC Education Services |
| title_short | Data science & big data analytics |
| title_sort | data science big data analytics discovering analyzing visualizing and presenting data |
| title_sub | discovering, analyzing, visualizing and presenting data |
| topic | Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Big Data Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027491485&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027491485&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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