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Data science and engineering
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Summary of the Profile
Objective(s) of a study programme:
To develop competences to creatively apply the knowledge of mathematical and computer sciences creating data engineering solutions and data analysis models, also integrating them into information systems for efficient solutions of enterprise operational problems.
Description of the study programme: https://admissions.ktu.edu/programme/b-data-science-and-engineering/
Learning outcomes:
Knowledge and its application:
(A02) Is able to think mathematically, communicate in mathematical language.
(A02, B02) Is able to formulate data science and data engineering problems in information systems in mathematical language, to choose appropriate methods of mathematics and computer science for their solution.
(A02) Is able to create mathematical models of real systems, compare them, and interpret results, based on the principles of mathematical modelling.
(A02, B02) Is able to develop algorithms and computer programs, use mathematical software for data science and data engineering models development.
(B02) Is able to conceptually identify information systems problems, analyse, plan, model and predict organizational processes.
(B02) Is able to design information systems and databases according to specified requirements.
Research skills:
(A02) Is able to systematically explain the fundamental concepts of various mathematical areas (such as algebra, geometry, mathematical analysis, probability theory, mathematical statistics) and apply them to solve theoretical and practical problems.
(A02) Is able to practically apply mathematical knowledge to create models for data science and data engineering.
(A02) Is able to identify fundamental machine learning mathematical methods and apply them to solve artificial intelligence tasks.
(A02, B02) Is able to outline the theoretical foundations of algorithms, programming, structure of mathematical software systems, programming environments and apply them for software development.
(B02) Is able to define information systems, their development stages, to prepare specification for subject area requirements of information system, to develop design specifications for integration of data analytics models into information systems.
(B02) Is able to explain database theory, formulate and implement a conceptual data model of subject area using database management system modelling, design and programming tools.
(A02, B02) Is able to select mathematical methods for data security and apply them practically in information systems.
Special abilities:
(A02, B02) Is able to find and analyse literature, collect data from various sources, process and analyse the obtained information.
(A02) Is able to analyse the structure and properties of mathematical models, evaluate their application possibilities.
(A02, B02) Is able to analyse information systems processes in the context of mathematical modelling.
(A02, B02) Is able to plan and perform analysis from information system problems identification, model development to results evaluation and dissemination.
Developed social and personal abilities:
(A02, B02) Is able to present in oral or written form knowledge, understanding and results of data science and data engineering task solutions to practitioners and other managers.
(A02, B02) Is able to work in an interdisciplinary team, generate new ideas and integrate knowledge.
(A02, B02) Is able critically to evaluate one's knowledge and values.
(A02, B02) Is able to take responsibility for data security, comply with ethical standards.
(A02, B02) Is able to learn independently and understands the importance of lifelong learning.
(A02, B02) Is able to evaluate the impact of one's activities and their results on society.
(A02, B02) Is able to organize professional activities, plan time and resources.
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):
Discrete Mathematics, Geometry, Introduction to Mathematics and Informatics Studies, Introduction to Object-Oriented Programming, Mathematical Analysis 1, Linear Algebra, Mathematical Analysis 2, Programming for Data Processing and Visualization, Cryptology, Mathematical Analysis 3, Mathematics Software, Theory of Probability, Databases, Mathematical Statistics, Optimization Methods, Physics 1, Data Analysis, Fundamentals of Information Systems, Machine Learning Methods, Methods of Mathematical Modelling, Applied Multivariate Analysis, Deep Learning, Product Development Project, Artificial Intelligence Solutions Development, Bayes Methods and Uncertainty Analysis, Information System Design and CASE Technology, Stochastic Processes, Bachelor’s Degree Final Project, Professional Internship.
Electives of Philosophy and Sustainable Development: Media Philosophy, Sustainable Development;
Electives: Business Intelligence and Data Mining, Teamwork in Information Systems Projects, Parallel Computing and Distributed Databases, Object-Oriented Programming 2, Fundamentals of Object-Oriented Programming 2, Design and Analysis of Computer Algorithms, Business Process Management and Modernization, Business Process Digitalization;
Foreign Language Electives (Level C1): Academic and Technical Communication in English (Level C1), Academic and Technical Communication in German (Level C1), Academic and Technical Communication in French (Level C1).
Study programme abstract:
A graduate knows mathematics, data science, machine learning methods, information systems, their specification and design methods. Is able to create, code and apply data science models for analysis of various systems and decision making and integrate them into business information systems for management of a whole data life cycle, creatively apply knowledge developing smart products, critically evaluate data and analysis results.
Access to professional activity:
The graduate can work as a data scientist, systems analyst, information systems designer integrating solutions, data analysis specialist, programmer analyst, in the modelling, design, information analysis and information technologies departments of various companies, and as developer of new financial technologies.
Access to further study:
S/he has access to the second cycle studies.
