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Analytics in a Big Data World
Analytics in a Big Data World The Essential Guide to Data Science and its Applications by Bart Baesens
Companies are being flooded with tsunamis of data collected in a multichannel business environment, leaving an untapped potential for analytics to better understand, manage, and strategically exploit the complex dynamics of customer behavior. In this book, we will discuss how analytics can be used to create strategic leverage and identify new business opportunities.
The focus of this book is not on mathematics or theory, but on the practical application. Formulas and equations will only be included when absolutely needed from a practitioner’s perspective. It is also not our aim to provide exhaustive coverage of all analytical techniques previously developed, but rather to cover the ones that really provide added value in a business setting.
The book is written in a condensed, focused way because it is targeted at the business professional. A reader’s prerequisite knowledge should consist of some basic exposure to descriptive statistics (e.g., mean, standard deviation, correlation, confidence intervals, hypothesis testing), data handling (using, for example, Microsoft Excel, SQL, etc.), and data visualization (e.g., bar plots, pie charts, histograms, scatter plots). Throughout the book, many examples of real‐life case studies will be included in areas such as risk management, fraud detection, customer relationship management, web analytics, and so forth. the author will also integrate both his research and consulting experience throughout the various chapters. The book is aimed at senior data analysts, consultants, analytics practitioners, and Ph.D. researchers starting to explore the field.
Discusses big data and analytics. It starts with some example application areas, followed by an overview of the analytics the process model, and job profiles involved, and concludes by discussing key analytic model requirements.
Provides an overview of data collection, sampling, and preprocessing. Data is the key ingredient to any analytical exercise, hence the importance of this chapter. It discusses sampling, types of data elements, visual data exploration and exploratory statistical analysis, missing values, outlier detection, and treatment, standardizing data, categorization, weights of evidence coding, variable selection, and segmentation.
Discusses predictive analytics. It starts with an overview of the target definition and then continues to discuss various analytics techniques such as linear regression, logistic regression, decision trees, neural networks, support vector machines, and ensemble methods (bagging, boosting, random forests). In addition, multiclass classification techniques are covered, such as multiclass logistic regression, multiclass decision trees, multiclass neural networks, and multiclass support vector machines. The chapter concludes by discussing the evaluation of predictive models.
Covers descriptive analytics. First, association rules have discussed that aim at discovering transaction patterns. This is followed by a section on sequence rules that aim at discovering inter transaction patterns. Segmentation techniques are also covered.
Introduces the survival analysis. The chapter starts by introducing some key survival analysis measurements. This is followed by a
discussion of Kaplan Meier analysis, parametric survival analysis, and proportional hazards regression. The chapter concludes by discussing
various extensions and evaluation of survival analysis models.
Covers social network analytics. The chapter starts by discussing an example of social network applications. Next, social network definitions and metrics are given. This is followed by a discussion on social network learning. The relational neighbor classifier and its probabilistic variant together with relational logistic regression are covered next. The chapter ends by discussing ego nets and bigraphs.
Provides an overview of key activities to be considered when putting analytics to work. It starts with a recapitulation of the analytic model requirements and then continues with a discussion of backtesting, benchmarking, data quality, software, privacy, model design and documentation, and corporate governance. Chapter 8 concludes the book by discussing various example applications such as credit risk modeling, fraud detection, net lift response modeling, churn prediction, recommender systems, web analytics, social media analytics, and business process analytics.