You can contact the coordinators of the Applied Data Science profile via this email: for more information. Below you can find the profile website for each graduate study: Data science is a multidisciplinary field that uses statistical and computational methods to extract insights and knowledge from data. Statistics is the study of collection, interpretation, organization analysis and organization of data and thus, data science professionals need to have solid. This master profile is offered to both Graduate School of Life Sciences (GSLS) and Graduate School of Natural Sciences (GSNS). Data science is used in a wide range of industries, including finance, healthcare, retail, and more. However, the modularity allows the instructor and student to work through the discrete sections in the desired order. The presentation of topics differs somewhat from the standard introductory social science statistics textbooks I have used before. The foundations of applied data science include relevant statistical methods, machine learning techniques and programming skills. Basic statistics are standard, so the core information will remain relevant in perpetuity. The two mandatory courses in this profile (Data analytics 1: Supervised learning and visualization, and Data analytics 2: Battling the curse of dimensionality) provide a thorough introduction to data science, its basic methods, techniques, processes, and the application of data science within specific domains. The Applied Data Science profile extends master students’ knowledge to address the challenges for decision making, planning, and knowledge discovery in large collections of diverse data. Categorical (nominal): the possible responses consist of a set of categories rather than numbers that measure an amount of something on a con-tinuous scale. Continuous data have an in nite number of steps. Applied Data Science is a multidisciplinary profile, offered by the Department of Methodology and Statistics, for students who are not only interested in broadening their knowledge and expertise within the field of data science, but are also eager to apply these capabilities in relevant projects within their research domain. Basic De nitions 2.1 Types of Data There two types of measurements: Quantitative: Discrete data have nite val-ues.
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