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Data science and engineering
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Summary of the Profile
General Description: Objective(s) of a study programme: To prepare qualified specialists which are able 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. Learning outcomes:
Specific Skills:
A1 (A02) Is able to think mathematically, communicate in mathematical language.
A2 (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.
A3 (A02) Knows the principles of mathematical modeling, is able to create mathematical models of real systems, compare them with each other and interpret the results.
A4 (A02, B02) Is able to develop algorithms and computer programs, use mathematical software for data science and data engineering models development.
A5 (B02) Is able to conceptually identify information systems problems, analyze, plan, model and predict organizational processes.
A6 (B02) Is able to design information systems and databases according to specified requirements.
Knowledge and its Application:
B1 (A02) Has knowledge and understanding of concepts, definitions, theorems, proofs of basic mathematical fields (algebra, geometry, mathematical analysis, probability theory, mathematical statistics) and is able to apply them in solving theoretical and practical problems.
B2 (A02). Has knowledge of mathematics, that is required to build data science and data engineering models, and is able to apply them in practice.
B3 (A02) Has knowledge of basic mathematical machine learning methods and is able to apply them to solve artificial intelligence tasks.
B4 (A02, B02) Has knowledge of theoretical foundations of algorithms, programming, structure of mathematical software systems, programming environments and is able to apply them for software development.
B5 (B02) Has knowledge of information systems, their development stages, is able to prepare specification for subject area requirements of information system, to develop design specifications for integration of data analytics models into information systems.
B6 (B02) Has knowledge of database theory, is able to formulate and implement a conceptual data model of subject area using database management system modeling, design and programming tools.
B7 (A02, B02) Has knowledge of mathematical methods of data security and is able to apply them in information systems.
Research Skills:
C1 (A02, B02) Is able to find and analyze literature, collect data from various sources, process and analyze the obtained information.
C2 (A02) Is able to analyze the structure and properties of mathematical models, evaluate their application possibilities.
C3 (A02, B02) Is able to analyze information systems processes in the context of mathematical modeling.
C4 (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 skills:
D1 (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.
D2 (A02, B02) Is able to work in an interdisciplinary team, generate new ideas and integrate knowledge.
D3 (A02, B02) Is able critically to evaluate one's knowledge and values.
D4 (A02, B02) Is able to take responsibility for data security, comply with ethical standards.
D5 (A02, B02) Is able to learn independently and understands the importance of lifelong learning.
D6 (A02, B02) Is able to evaluate the impact of one's activities and their results on society.
D7 (A02, B02) Is able to organize professional activities, plan time and resources.
Activities of teaching and learning: The material of all learning courses is gained during classroom and individual work. Classroom work consists of lectures,
practice, laboratory works and seminars. Student’s individual work is the mastering of theoretical material, preparation for lectures, practice and lab works, interim and final exams, accomplishment of homework and projects as well as other activities. The study program concludes with final practice and bachelor final project. Methods of assessment of learning achievements: Student’s knowledge, skills and abilities obtained while studying the module are graded in ten-point scale for performed semester activities. Final grade consists of cumulative score of semester activities and exam during the session. Variety of student achievements assessment methods can be applied: written test; Written and Oral Examination; Test; Report of Laboratory works and their assessment; Modeling works; problem solving; Report of Individual or group project; Oral and Poster Reports; Presentation and defense of practical work (research) reports Colloquium; Control work with closed and / or open questions; Written papers (literature review, essay, report, etc.); Term paper, final paper and its defense. Framework: Study subjects (modules), practical training: General university subjects (BUS), 12 ECTS: Electives of Philosophy, Foreign Language Electives (level C1). Subjects of Applied Mathematics field (105 ECTS.): Mathematical Analysis 1, 2, 3, Discrete Mathematics, Introduction to Matrix Theory and Geometry, Linear Algebra, Physics 1, Mathematical Software, Optimization Methods, Data Analysis, Machine Learning Methods, Cryptology and Information Security, Applied Multidimensional Data Analysis, Mathematical Modeling Techniques, Random Processes and Time Series, Bayesian Methods and uncertainty analysis. Information systems study field subjects (27 ECTS): Fundamentals of Object Oriented Programming 1 or Object Oriented Programming 1, Data Bases, Fundamentals of Information Systems, Information Systems Design and CASE Technologies. Elective special subjects (36 ECTS): Introduction to Mathematics and Computer Science, Programming for Data Processing and Visualization, Data Science and Engineering Project, Deep learning, Applications of Artificial Intelligence solutions. Advanced Studies (Branch deepening studies, 30 ECTS.): Three from Business Process Management and Modernization, Algorithm Design and Analysis, Digitization of Business Processes, or Teamwork in Information Systems Projects. One from Data Warehouse and Performance Analytics, Parallel Computing and Distributed Databases, or Network Services Development and Deployment. One from Graph Theory and Network Analysis, Numerical Methods in Data Science or Mathematical Methods for Digital Image Processing. Final Practice 15 ECTS;
Final Degree Project (Bachelor's thesis) 15 ECTS; Specialisations: Optional courses: In semesters 1-7, subjects are selected from the list of alternatives Distinctive features of a study programme: Graduates will have mathematical and algorithmic thinking skills, will be able to manage data in organizations' information systems, develop and implement data science models for operational decision-making, have advanced database management and security and data quality assurance technologies, and be able to work in an interdisciplinary team. Graduates of the program will become specialists with good knowledge of mathematics and computer science and analytical knowledge and skills, who will be able to implement and automate proposed solutions in computer science systems, ensuring their reliability, operability and compatibility with business processes. Opportunities for professional activity and further studies: Career opportunities: Graduate can work as a data scientist, systems analyst, information systems designer integrating solutions, data analysis specialist, programmer analyst, in the modeling, design, information analysis and information technologies departments of various companies, and as developer of new financial technologies (fintech). Further study opportunities: Postgraduate studies in Mathematics or Computer Science.
