MA/MS in Data Science and Artificial Intelligence

Objective(s) of a study programme: To provide knowledge about advanced mathematical methods of data science and artificial intelligence and to develop skills to create complex intelligent systems based on the critical and ethical use of data and mathematical models. Description of the study programme: https://admissions.ktu.edu/programme/m-data-science-and-artificial-intelligence/ Learning outcomes: Knowledge and its application: Is able to apply various data acquisition and processing methods, form datasets based on knowledge of mathematics and data processing. Is able to perform data analysis of processes and events, revealing their causal relationships and creating mathematical models based on them for decision-making, forecasting, risk assessment. Able to develop models of machine and deep learning methods, justifying their theoretical foundations and applying various error estimation methods to recognize, identify, segment, predict or solve problems. Able to evaluate ethical issues related to data management and their application in various fields, knowing systems based on artificial intelligence rules and logic and machine learning. Is able to critically assess the latest scientific and technological achievements, their development potential and the need to apply them in selected areas, taking into account the available resources. Research skills: Is able to select and analyze scientific literature necessary for evaluating theoretical assumptions and research methods, for conducting planned scientific research and for solving the tasks of developing and applying artificial intelligence products. Is able to perform analytical and numerical calculations for conducting research, using modern statistical methods to collect, systematize, analyze and model experimental data. Is able to implement experimental, theoretical and applied research developing mathematical models based on classical and artificial intelligence methods, planning, justifying and executing them. Is able to implement data science and artificial intelligence methods application and development projects, initiating, preparing and executing them, interpreting the obtained results, formulating reasonable conclusions and forecasts, preparing analysis reports and insights, conveying knowledge and understanding to business representatives and/or scientists. Special abilities: Is able to logically, analytically and critically evaluate theories and practical challenges of data science and artificial intelligence applications. Is able to apply technical, scientific, digital resources and existing skills to solve complex data science and AI application problems in various fields. Is able to create high-quality and reliable hybrid intelligent solutions, data processing and machine learning models described by mathematical relationships. Is able to apply machine learning and artificial intelligence methods for automatic analysis of experimental data, required to create mathematical models described in mathematical language and relationships and based on machine learning. Is able to find optimal methods to solve atypical complex tasks in new and unfamiliar environments, applying knowledge of data science and artificial intelligence and selecting appropriate criteria. Developed social and personal abilities: Is able to critically evaluate the complexity and consequences of self-made decisions, compliance with academic and professional ethics norms, assuming moral responsibility for automated decisions, ethics and biases of artificial intelligence system use. Is able to responsibly and critically assess their planned, organized or implemented activities, their results and impact on society and the environment. Realizing the importance of lifelong learning and development, he is able to choose his own activities, develop acquired competences and skills. Is able to communicate professionally on the topics of mathematics and its application with specialists in his own and other fields, working in an interdisciplinary team, presenting problems and solutions to an audience of specialists and non-specialists. Activities of teaching and learning: The studies include classroom work (lectures, practical work, laboratory work, seminars, outgoing visits to enterprises, etc.) and individual work for mastering theoretical material, preparation for classroom work, intermediate and final assessments and performing other activities. The studies of each study module are completed by the assessment of the student’s knowledge and skills – an examination or another final assessment; the study programme is completed by the final degree project and its defence. Methods of assessment of learning achievements: The applied cumulative assessment system of the learning outcomes ensures constant and involving work of students during the entire semester of studies; the final evaluation of the study module consists of the sum of the grades of intermediate assessments and the final assessment multiplied by the weighting coefficients (percentages of components). Study subjects (modules): Data Science Project, Ethics of Artificial Intelligence, Mathematical Methods of Artificial Intelligence, Statistical Data Analysis and Forecasting Methods, Advanced Mathematical Methods of Artificial Intelligence, Artificial Intelligence Application Project, Neural Networks, Master’s Degree Final Project. Electives: Combinatorial Optimisation, Data-driven Methods of Mathematical Modelling, Data-driven Decision Making. Study programme abstract: A graduate has fundamental and applied knowledge in data science, artificial intelligence, machine learning, and big data analytics. He/she is able to analyse, model, and optimise data management and decision-making processes by applying modern algorithms and technologies. The graduate is prepared to independently solve complex problems, carry out interdisciplinary projects, and contribute to the innovation development. Access to professional activity: The graduate can work in job positions of analyst, data analyst, information analyst, operations analyst, modeller, artificial intelligence specialist, data scientist, supply chain analyst, etc. Access to further study: S/he has access to the third cycle studies.