Data Science

Learning Outcomes of the Study Program GENERAL LEARNING OUTCOMES: Evaluate and analyze complex and insufficiently defined problems in the field of occupation using concepts of information theory, applied mathematical theory and best engineering practices Introduce innovative solutions in the field of applied computing by critical analysis and evaluation of current knowledge, models and solutions in the field of expertise, using “best practice solutions” and familiar and modified problem scenarios Apply complex research and analysis methods to determine detailed user or organizational requirements for information solutions or systems Identify, analyze and explain the problems of applying, polishing and implementing existing information systems in a wider business context and propose adequate solutions Manage relationship with users and / or members of a team, recognizing possible sources of misunderstanding and conflict and proactively and effectively influence their inhibition Design , prepare and manage the implementation of development projects in the field of applied computing using recognized methodologies and taking into account available resources, budgets and risks Be aware of business, organizational and sociological aspects of application and impact on the environment (user, organization, society) when planning, designing and applying information systems Evaluate the entrepreneurial idea and propose adequate business and organizational conditions for its realization Proactively manage your own professional and personal development and collect new knowledge and skills in different contexts and environment (e.g. through successful and unsuccessful projects, through continuous self-learning and monitoring of scientific and technological achievements, additional education …) Independently design and manage IT project with available resources, taking responsibility for personal and team tasks in unpredictable business conditions and environment Perform an independently significant final project by following set of requirements and standards and by applying modern technologies, tools and methodology PROFESSIONAL LEARNING OUTCOMES – Data orientation Critically evaluate the impact of disruptive technologies on business environment, evaluate the impact of various disruptive technologies within the sector in which they emerged and analyze the potential for new disruptive technology Choose appropriate methods to work with missing data and data transformation, recommend solutions to identified problems when preparing data and choose adequate solution for a problem in the process of integration, normalization and data discretization Create a program solution that solves part of the data problem Assess the impact of different types of security risks and analyze the provisions of the code of ethics that protect the right to privacy and explain conceptual difficulties in determining the right to privacy Assess the impact of different types of reduction of features and apply the appropriate basic methods of reduction of features and samples and choose appropriate machine and in-depth learning algorithms to address the observed business problem Identify, interpret and determine the basic measures of central tendency and dispersion in terms of applicability, interpretability and usefulness and interpret the basic aspects of correlation and regression analysis Explain social network analysis and what are its goals; recommend basic network, centrality, prestige, and network grouping, and rank basic functionalities of social network analysis software 46. Analyze the advantages and disadvantages of the cloud analytics; choose the adequate cloud services and apply them to solve a specific business problem Analyze features of psychophysical, voice, verbal and facial expressions in the context of model development for automated recognition of affective states in industries Review potentials of large data sets and techniques for analyzing large data sets and evaluate product quality using relevant knowledge Evaluate the role and benefits of visualization of data in relation to numerical representation and choose the appropriate types of visualization tools and explorative analysis for a given problem