Finlay Predictive Analytics, Data Mining and Big Data

Myths, Misconceptions and Methods
2014
ISBN: 978-1-137-37928-3
Verlag: Palgrave Macmillan UK
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 261 Seiten

Reihe: Business in the Digital Economy

This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.  

Autoren/Hrsg.

Finlay, S.

Inhaltsverzeichnis

1;Cover;1
2;Half-Title;2
3;Title;4
4;Copyright;5
5;Dedication;6
6;Contents;8
7;Figures and Tables;11
8;Acknowledgments;13
9;1 Introduction;14
9.1;1.1 What are data mining and predictive analytics?;15
9.2;1.2 How good are models at predicting behavior?;19
9.3;1.3 What are the benefits of predictive models?;20
9.4;1.4 Applications of predictive analytics;22
9.5;1.5 Reaping the benefits, avoiding the pitfalls;24
9.6;1.6 What is Big Data?;26
9.7;1.7 How much value does Big Data add?;29
9.8;1.8 The rest of the book;32
10;2 Using Predictive Models;34
10.1;2.1 What are your objectives?;35
10.2;2.2 Decision making;36
10.3;2.3 The next challenge;44
10.4;2.4 Discussion;47
10.5;2.5 Override rules (business rules);49
11;3 Analytics, Organization and Culture;52
11.1;3.1 Embedded analytics;53
11.2;3.2 Learning from failure;55
11.3;3.3 A lack of motivation;56
11.4;3.4 A slight misunderstanding;58
11.5;3.5 Predictive, but not precise;63
11.6;3.6 Great expectations;65
11.7;3.7 Understanding cultural resistance to predictive analytics;67
11.8;3.8 The impact of predictive analytics;73
11.9;3.9 Combining model-based predictions and human judgment;75
12;4 The Value of Data;78
12.1;4.1 What type of data is predictive of behavior?;79
12.2;4.2 Added value is what's important;83
12.3;4.3 Where does the data to build predictive models come from?;86
12.4;4.4 The right data at the right time;89
12.5;4.5 How much data do I need to build a predictive model?;92
13;5 Ethics and Legislation;98
13.1;5.1 A brief introduction to ethics;99
13.2;5.2 Ethics in practice;102
13.3;5.3 The relevance of ethics in a Big Data world;103
13.4;5.4 Privacy and data ownership;105
13.5;5.5 Data security;109
13.6;5.6 Anonymity;110
13.7;5.7 Decision making;112
14;6 Types of Predictive Models;117
14.1;6.1 Linear models;119
14.2;6.2 Decision trees (classification and regression trees);125
14.3;6.3 (Artificial) neural networks;127
14.4;6.4 Support vector machines (SVMs);131
14.5;6.5 Clustering;133
14.6;6.6 Expert systems (knowledge-based systems);135
14.7;6.7 What type of model is best?;137
14.8;6.8 Ensemble (fusion or combination) systems;141
14.9;6.9 How much benefit can I expect to get from using an ensemble?;143
14.10;6.10 The prospects for better types of predictive models in the future;144
15;7 The Predictive Analytics Process;147
15.1;7.1 Project initiation;148
15.2;7.2 Project requirements;151
15.3;7.3 Is predictive analytics the right tool for the job?;155
15.4;7.4 Model building and business evaluation;156
15.5;7.5 Implementation;158
15.6;7.6 Monitoring and redevelopment;162
15.7;7.7 How long should a predictive analytics project take?;167
16;8 How to Build a Predictive Model;170
16.1;8.1 Exploring the data landscape;171
16.2;8.2 Sampling and shaping the development sample;172
16.3;8.3 Data preparation (data cleaning);175
16.4;8.4 Creating derived data;176
16.5;8.5 Understanding the data;177
16.6;8.6 Preliminary variable selection (data reduction);178
16.7;8.7 Pre-processing (data transformation);179
16.8;8.8 Model construction (modeling);183
16.9;8.9 Validation;184
16.10;8.10 Selling models into the business;185
16.11;8.11 The rise of the regulator;189
17;9 Text Mining and Social Network Analysis;192
17.1;9.1 Text mining;192
17.2;9.2 Using text analytics to create predictor variables;194
17.3;9.3 Within document predictors;194
17.4;9.4 Sentiment analysis;197
17.5;9.5 Across document predictors;198
17.6;9.6 Social network analysis;199
17.7;9.7 Mapping a social network;204
18;10 Hardware, Software and All that Jazz;207
18.1;10.1 Relational databases;210
18.2;10.2 Hadoop;213
18.3;10.3 The limitations of Hadoop;215
18.4;10.4 Do I need a Big Data solution to do predictive analytics?;216
18.5;10.5 Software for predictive analytics;219
19;Appendix A. Glossary of Terms;222
20;Appendix B. Further Sources of Information;231
21;Appendix C. Lift Charts and Gain Charts;236
22;Notes;240
23;Index;259