He is the terror that flaps in the night…   He is the ferocious fowl who plucks the evil eye from the face of foul play…   He is Darkwing Duck!   Alongside his trusty sidekick Lauchpad McQuack, Darkwing hyper-vigilantly defends St. Canard from the dastardly, devilish demons who would wage wanton war! By night, our caped defender lurks in the shadows, striking fear (and maybe confusion?) in the heart of the criminal underworld…but by day, no one suspects that Darkwing is also mild-mannered Drake Mallard, a well-meaning father to his adorable adopted daughter, Gosalyn! Can Darkwing successfully navigate his two separate lives, all while looking incredibly cool and impossibly handsome? (Hint: Probably not, but…) You’ll have to read to find out!

In the last decades, the production of goods and the offer of services have become quite complex activities mostly because of the markets globalisation, of the continuous push to the innovation and of the constant requests from more and more demanding markets. The main objective of a company system has become the achievement of the quality for the business management cycle. This cycle goes from the design (Plan) to the production (Do), from the control (Check) to the man agement (Action), as well as to the marketing and distribution. Nowadays, the Total Quality of the company system is evaluated, according to the ISO 9000 regulations, in terms of its capacity to adjust the design and the pro duction to the needs expressed (explicitly or implictly) by the final users of a product/service. In this process, the use of statistical techniques is essential not only in the classical approach of Quality Control of a product but also, and most importantly, in the Quality Design oriented to the satisfaction of customers. Thus, Total Quality refers to the global capacity of a company to fit its system to the real needs of its customers by designing products which are able to match the customers' taste and by implementing a statistical control of both the product and the Customer Satisfaction. In such a process of design and evaluation, several statistical variables are involved and with a different nature (numerical, categorical, ordinal)."

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.