This cumulative dissertation summarizes and critically discusses seven peer-reviewed publications where I was involved as a co-author. All publications contribute to data analytics techniques. The dissertation consists of four main sections. (1) Machine Leaning in Finance: In this section a Decision Support Algorithm based in Reinforcement Learning is introduced which filters rule-based trading decisions. We contribute to the literature by describing the implementation of the algorithm. We also provide empirical evidence of financial market anomalies. (2) Mining Customer Reviews: Opinions from customers about certain products are more and more expressed on social media platforms. Here we provide the first study which analyses YouTube comments as a data source for an aspect-based Sentiment Analysis. We also contribute to the literature by proposing a filtering method based on Google Trends which sorts product aspects according to their relevance for the customers. (3) Forecasting Resale Prices of Used Cars: In this section we show how to efficiently forecast resale prices of used cars with Artificial Neural Networks. We provide lessons learned about long-term forecasts. We also provide insights in the importance of certain independent factors which determine the resale price. (4) Visual Model Evaluation: The research in this section is mainly driven by the question of how to better incorporate human domain knowledge in data science. We develop a visualization technique based on heat maps which provides a more intuitive view on errors of a machine learning model. The visualization technique allows domain experts to discuss the results of machine learning models with data science experts on the same level of complexity.