This paper provides a practical guideline for the assurance and (re-)usage of clinical data. It proposes a process which aims to provide a systematic data quality assurance even without involving a medical domain expert. Especially when (re-)using clinical data, data quality is an important topic because clinical data are not purposely collected. Therefore, data driven conclusions might be false, because a given dataset is not representative. These false data driven conclusions could even harm the life of patients. Thus, all researchers should adhere to some basic principles that can prevent false conclusions. Twelve empirical experiments were conducted in order to prove that my process is able to assure data quality with respect to the descriptive and predictive analysis. Descriptive results obtained by applying stratified sampling are conflicting in four out of nine population inputs. Sampling is carried based on the top ranked feature drawn by the Contextual Data Quality Assurance (CDQA). Between datasets these features are confirmed by the Mutual Data Quality Assurance (MDQA). Stratified sampled inputs improve predictive results compared to raw data. Both Area Under the Curve (AUC) scores and accuracy scores increase by three percent.

One of the application fields for data analysis techniques and technologies gaining momentum is the area of social good or “common good”, covering cases related to humanitarian crises, global health care, or ecology and environmental issues, among others. The promotion of data-driven projects in this field aims at increasing the efficacy and efficiency of social initiatives, improving the way these actions help humanity in general and people in need in particular. This application field, however, poses its own barriers and challenges when developing data-driven projects, lagging behind in comparison with other scenarios. These challenges derive from aspects such as the scope and scale of the social issue to solve, cultural and political barriers, the skills of main stakeholders and the technological resources available, the motivation to be engaged in such projects, or the ethical and legal issues related to sensitive data. This paper analyzes the application of data projects in the field of social good, reviewing its current state and noteworthy initiatives, and presenting a framework covering the key aspects to analyze in such projects. The goal is to provide guidelines to understand the main challenges and opportunities for this type of data project, as well as identifying the main differential issues compared to “classical” data projects in general. A case study is presented on the initial steps and stakeholder analysis of a data project for the inclusion of refugees in the city of Frankfurt, Germany, in order to empirically confront the framework with a real example. Keywords—Data-Driven projects, humanitarian operations, personal and sensitive data, social good, stakeholders analysis.

The interest to become a data scientist or related professions in data science domain is rapidly growing. To meet such a demand, we propose a novel educational service that aims to provide a tailored learning paths for data science. Our target user is one who aims to be an expert in data science. Our approach is to analyze the background of the practitioner and match the learning units. A critical feature is that we use gamification to reinforce the practitioner engagement. We believe that our work provides a practical guideline for those who wants to learn data science.

......................................................................................................................................................... II DECLARATION OF AUTHORSHIP .............................................................................................................. III ACKNOWLEDGMENT..................................................................................................................................... IV TABLE OF CONTENTS ...................................................................................................................................... V LIST OF FIGURES AND ILLUSTRATIONS ................................................................................................. IX LIST OF TABLES ................................................................................................................................................ X LIST OF ABBREVIATIONS ............................................................................................................................. XI 1. CHAPTER