Predictive Analytics, Data Mining and Big Data [electronic resource] : Myths, Misconceptions and Methods /
Finlay, S.author.
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.
Cover; Contents; Figures and Tables; Acknowledgments; Chapter 1 Introduction; 1.1 What are data mining and predictive analytics?; 1.2 How good are models at predicting behavior?; 1.3 What are the benefits of predictive models?; 1.4 Applications of predictive analytics; 1.5 Reaping the benefits, avoiding the pitfalls; 1.6 What is Big Data?; 1.7 How much value does Big Data add?; 1.8 The rest of the book; Chapter 2 Using Predictive Models; 2.1 What are your objectives?; 2.2 Decision making; 2.3 The next challenge; 2.4 Discussion; 2.5 Override rules ( business rules)
Chapter 3 Analytics, Organization and Culture3.1 Embedded analytics; 3.2 Learning from failure; 3.3 A lack of motivation; 3.4 A slight misunderstanding; 3.5 Predictive, but not precise; 3.6 Great expectations; 3.7 Understanding cultural resistance to predictive analytics; 3.8 The impact of predictive analytics; 3.9 Combining model-based predictions and human judgment; Chapter 4 The Value of Data; 4.1 What type of data is predictive of behavior?; 4.2 Added value is what''s important; 4.3 Where does the data to build predictive models come from?; 4.4 The right data at the right time
4.5 How much data do I need to build a predictive model?Chapter 5 Ethics and Legislation; 5.1 A brief introduction to ethics; 5.2 Ethics in practice; 5.3 The relevance of ethics in a Big Data world; 5.4 Privacy and data ownership; 5.5 Data security; 5.6 Anonymity; 5.7 Decision making; Chapter 6 Types of Predictive Models; 6.1 Linear models; 6.2 Decision trees (classification and regression trees); 6.3 (Artificial) neural networks; 6.4 Support vector machines (SVMs); 6.5 Clustering; 6.6 Expert systems ( knowledge- based systems); 6.7 What type of model is best?
6.8 Ensemble (fusion or combination) systems6.9 How much benefit can I expect to get from using an ensemble?; 6.10 The prospects for better types of predictive models in the future; Chapter 7 The Predictive Analytics Process; 7.1 Project initiation; 7.2 Project requirements; 7.3 Is predictive analytics the right tool for the job?; 7.4 Model building and business evaluation; 7.5 Implementation; 7.6 Monitoring and redevelopment; 7.7 How long should a predictive analytics project take?; Chapter 8 How to Build a Predictive Model; 8.1 Exploring the data landscape
8.2 Sampling and shaping the development sample8.3 Data preparation (data cleaning); 8.4 Creating derived data; 8.5 Understanding the data; 8.6 Preliminary variable selection (data reduction); 8.7 Pre- processing (data transformation); 8.8 Model construction (modeling); 8.9 Validation; 8.10 Selling models into the business; 8.11 The rise of the regulator; Chapter 9 Text Mining and Social Network Analysis; 9.1 Text mining; 9.2 Using text analytics to create predictor variables; 9.3 Within document predictors; 9.4 Sentiment analysis; 9.5 Across document predictors; 9.6 Social network analysis
9.7 Mapping a social network
Includes index.
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.
Leadership.
Marketing.
Development economics.
Knowledge management.
Market research.
Big data.
Business Strategy/Leadership.
Marketing.
Development Economics.
Knowledge Management.
Market Research/Competitive Intelligence.
Big Data/Analytics.