Deep Learning has advanced the state-of-the-art in many fields, including machine translation, where Neural Machine Translation (NMT) has become the dominant approach in recent years. However, NMT still faces many challenges such as domain adaption, over- and under-translation, and handling long sentences, making the need for human translators apparent. Additionally, NMT systems pose the problems of explainability, interpretability, and interaction with the user, creating a need for better analytics systems. This thesis introduces NMTVis, an integrated Visual Analytics system for NMT aimed at translators. The system supports users in multiple tasks during translation: finding, filtering and selecting machine-generated translations that possibly contain translation errors, interactive post-editing of machine translations, and domain adaption from user corrections to improve the NMT model. Multiple metrics are proposed as a proxy for translation quality to allow users to quickly find sentences for correction using a parallel coordinates plot. Interactive, dynamic graph visualizations are used to enable exploration and post-editing of translation hypotheses by visualizing beam search and attention weights generated by the NMT model. A web-based user study showed that a majority of participants rated the system positively regarding functional effectiveness, ease of interaction and intuitiveness of visualizations. The user study also revealed a preference for NMTVis over traditional text-based translation systems, especially for large documents. Additionally, automated experiments were conducted which showed that using the system can reduce post-editing effort and improve translation quality for domain-specific documents.