The Ph.D. in Data Science and Engineering is a joint collaborative program betweenElectrical Engineering and Computer Science,Industrial Engineering, andMathematicsdepartments. Data science is a rapidly growing interdisciplinary field that involves researchers from many STEM disciplines with applications found throughout science and engineering. The advancement in business analytics can be attributed to data science and has proven to unlock actionable intelligence and insights.

The Ph.D. in Computational Data Science and Engineering is an interdisciplinary graduate program designed for students who seek to use advanced computational methods to solve problems involving big data, extensive computations, and complex modeling, simulation, optimization and visualization.

Geoinformatics is a field of study involving the science and technology dealing with the acquisition, storage, processing production, presentation, and dissemination of geoinformation using the theory and practical implications of geodesy within information science infrastructure. The core of geoinformatics is geospatial analytics, a branch of data science that focuses on developing cutting-edge technologies supporting processes of acquiring, analyzing, and visualizing geospatial Big Data. Advances in various location-aware technologies, (e.g., GPS, the Internet of Things (IoT), mobile sensors, remote sensing), and ever-increasing time-stamped, location-based data from sensors as diverse as satellites, drones, and smartphones have created unprecedented demand and opportunities for code-savvy geospatial professionals with the ability to automate processing, visualizing and finding patterns in both structured and unstructured data.

The Master of Philosophy (MPhil) and Doctor of Philosophy (PhD) Programs in Data Science and Analytics aim to facilitate close integration of statistical analytics, logical reasoning, and computational intelligence in the study of data processing and analytics.

PhD Thesis Research in Information Systems & Data Management

In the digital era, following advancements made in innovative technologies, data handling is growing at an unprecedented pace. The data-driven world opens tremendous possibilities and opportunities for companies and businesses for all industries as they can make use of the data information to create value for their business. As a disruptive consequence of the digital revolution, data science and analytics has become an emerging and cross-disciplinary field that requires knowledge and skills in many areas such as computer science, statistics, and mathematics.

Vistula University from Warsaw, Poland offers a certified postgraduate study program designed to prepare technical teams equipped with excellent skill sets to meet industry-specific needs. There is a shortage of skilled data scientists to meet today’s business needs and the gap is set to widen in the future. With our customized study program, you can fill this gap by learning from our academics and practitioners. Our study program will provide you the most essential skills and tools that are needed in top-notch companies in the world.

The doctoral program offers a course of study that emphasizes a strong foundation in science, technology, engineering, and mathematics disciplines. Doctoral candidates are required to complete six doctoral-level courses (18 semester hours); 6 semester hours in a formal research seminar; and 12 semester hours of dissertation work. In the second year (or equivalent), doctoral candidates participate in an informal, non-credit (but required) research symposium to provide exposure to current and interesting research in the student’s field(s) of interest and industry needs, seeking trends for research and innovation.

Information technology (IT) is a strategic asset used to mold competitive strategies and change organizational processes.

Ph.D. in Developing and Evaluating Quality Plan is a qualification more related to theories that show relevance with theories related to development, as well as evaluation of plans connected with quality.