. "Computer Science"@en . . "English"@en . . "Mathematics"@en . . "Modelling and optimization for analysis of big data"@en . . "5" . "Main target of the course is on providing precise and \r\npowerful tools strongly required in the study of optimization models. Namely, a solid \r\nintroduction to multidimensional calculus of variations and multidimensional control theory \r\nwill be given, coupled with elementary modern linear geometry, basics of functional analysis, \r\nbasics of discrete harmonic analysis and basics of discrete differential geometry needed in the \r\nmodern Big Data Analysis. As an application, many optimization problems are illustrated by \r\nexamples arising from Big Data science, like CCA, generalized CCA, Kernel and Nonlinear \r\nCCA.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Big data technologies"@en . . "5" . "The discipline \"Big Data Technologies\" is developed \r\nthematically in the areas of mathematical and conceptual models of big data analysis knowledge \r\ndata discovery (KDD), technological frameworks and software tools for big data analytics, \r\nprogramming languages for analytical models, technological frameworks and streaming \r\nsoftware tools for the analysis of Big data streams.\n\nOutcome:\nstudents will:\r\n• Gain knowledge about the innovative ecosystem of Big data, the conceptual models of this \r\necosystem, the types of big data analytics according to the depth of knowledge, modern \r\nplatforms, technological frameworks and software tools for big data analysis and knowledge\r\ndiscovery, software libraries and knowledge data discovery tools.\r\n• Acquire skills for implementation of computer models and software applications for \r\nknowledge data discovery based on analysis of Big data." . . "Presential"@en . "TRUE" . . "Statistical methods for big data analysis"@en . . "4" . "The main topics concern: tests for the statistical \r\nhypothesizes, power of the tests, multiple linear and nonlinear regression, multiple analyses of \r\nvariance, testing of non-parametrical hypothesis, survival analysis.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Intelligent knowledge management"@en . . "4" . "Intelligent knowledge management course is aimed at \r\nproviding master students with basic notions on main tasks for knowledge generation by \r\nmachine learning algorithms and skills for applying them in specialized software systems. To \r\nacquire skills for design of applications involving aggregated knowledge and dashboard \r\nvisualization within business intelligence software. To get acquainted with semantic web \r\ntechnologies and become skillful in representing knowledge by designing ontologies and their \r\nquerying with a practical software system.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Analysis of business data in social networks"@en . . "4" . "The course \"Analysis of business data in social \r\nnetworks\" aims to acquaint students with current approaches and methods of artificial \r\nintelligence for data analysis in social networks, related to assessing the effectiveness of \r\ncampaigns in social networks, analysis of the interaction between users and users, social media \r\nbehavior, measuring the return on investment of social media campaigns, and real-time data \r\nanalysis to tailor campaigns to specific user profiles for maximum impact. The main channels \r\nthat will be discussed in the course are: Facebook, Instagram, LinkedIn.\n\nOutcome:\nUpon completion of the course, students will acquire knowledge and \r\nskills to:\r\n➢ Evaluate the effectiveness of a campaign by analyzing data on social networks.\r\n➢ Analyze consumer behavior in social networks.\r\n➢ Present convincing arguments for investing in social networks.\r\n➢ Choose the most appropriate social channels for a specific business." . . "Presential"@en . "TRUE" . . "Intelligent security systems of big data and internet of things ecosystems"@en . . "3" . "The course \"Intelligent security systems for Big data and Internet of Things ecosystems\" is \r\ndeveloped thematically in the areas of Big data streams analytics in real time, pre-combining \r\nvulnerability management with real-time analysis, risk assessment and identification, before \r\nthey become violations, data collection, normalization and analysis, design and implementation \r\nof proactive security solutions, as well as digital platforms for intelligent security solutions.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Cybersecurity and law"@en . . "3" . "The course develops basic orientation of the students in \r\nthe legal instruments of securing protection against cybercrime by providing knowledge of the \r\nenforced criminal and administrative legislation and its implementation, as well as practical \r\nknowledge and skills to recognize, investigate and prevent cybercrime. The course provides \r\nknowledge in the field of types of cybercrime, modus operandi, criminal consequences, \r\nperpetrator’s profile, crime discovery and proving. The training methodology includes interactive \r\nlectures, role-plays, practical tasks and simulations. It also envisages the participation of practicing \r\njudges, prosecutors and special-unit investigators in the seminars, conducting of topic-based \r\nseminar exercises, case-solving, visits and internship in the judiciary, prosecution and law\u0002enforcement institutions, incl. General Directorate ‘Fight against Organized Crime’ and Interpol. \r\nThe knowledge and skill which are expected to be developed by the students establish conditions \r\nfor their high-competitive professional realization in the sectors of criminal justice, state \r\nadministration, international relations, national security, and businesses\n\nOutcome:\nAfter the course has been accomplished the \r\nacquired knowledge and skills include:\r\n- free professional usage of penal-law terminology and familiarity with the special preventive \r\nand regulative legislation and its terminology;\r\n- ability to recognize types of cybercrime related to abuse of or impact against information \r\nsystems, technology, or data, modus operandi and tools for their perpetration, typical crime \r\nschemes, motivation mechanism, perpetrator’s profile;\r\n- differentiation of criminal from non-criminal conduct;\r\n- ability to undertake measures within students’ main professional domain to prevent \r\ncybercrime in the area of their professional activities;\r\n- well-integrated basic knowledge of cybercrime discovery and investigation and the ole of the \r\ncourt-expertise and court experts in the evidence-gathering process, including innovative \r\napproaches and techniques in the area;\r\n- basic personal experience in the recognition of a cybercrime, its detection, investigation and \r\nestablishing, court-expert’s counteraction with investigation and judiciary authorities, \r\nincluding team-work and performing under role-distribution." . . "Presential"@en . "TRUE" . . "Course project"@en . . "2" . "No Description, No Learning Outcome" . . "Presential"@en . "TRUE" . . "Cloud platform and services for big data"@en . . "3" . "Cloud Platform and Services for Big Data is a \r\nspecialized course for the students of the specialty \"Big Data Analytics\". It considers the \r\ncapabilities and challenges of computing distributed component-based and service-oriented \r\narchitectures, cloud-based services and cloudware, application programming interfaces, \r\ntaxonomy and cloud-based platforms, application development and integration technologies, \r\ndata centers and cloud computing, specific aspects such as computational load balance, \r\ndistributed transactions, authentication and authorization. Another focus is the design and \r\nimplementation of portals for the provision of services through containers of portlets as well as \r\nthe implementation of workflows. The theoretical part covers modern cloud service platforms \r\nworldwide, as well as methods and tools for the development and integration of enterprise cloud \r\napplications. The practical part includes developing applications, designing and implementing \r\nportals with workflows of services. Open-source cloud computing environments, as well as \r\nRESTFul Web services, are used to develop effective applications.\r\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Big data analytics for precision medicine"@en . . "3" . "The discipline \"Big Data Analytics for Precision \r\nMedicine\" is developed thematically in the areas of in silico technologies, in silico knowledge \r\ndata discovery, Big data analytics from the ecosystem of the Internet of Medical Things (IomT), \r\nInternet of medical imaging Things, technologies for data analysis and knowledge data \r\ndiscovery from the ecosystem of Big data and Big streams of biological and medical data, cloud \r\ntechnologies and services in the health industry.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Digital big data and computer forensics"@en . . "3" . "The course includes topics from criminology; \r\nregulations, standards and rules for working with evidence; microscopic and molecular \r\nbiological methods for identifying evidence; microscopic image recognition and classification \r\nsoftware; dactyloscopic data, digitization and organization of dactyloscopic data, automatic \r\nsearch software; standards and protocols for the exchange of fingerprints between different \r\nIAFIS, IDENT and EURODAC databases; genetic information as a method of identification; \r\nDNA profiling; software for organizing databases with reference DNA profiles; Biometric \r\nrecognition; handwriting recognition; signature recognition and image recognition.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Business strategies for software products"@en . . "3" . "The scope of the course concerns the basic \r\nclassifications of the business models in the software industry. Corporate and marketing \r\nstrategies in the software industry. Also, in the course are considered the main principals used \r\nin market analysis, the main criteria for decision making process and product, distribution, \r\ncommunication and price strategies development. Last but not least the course focuses on the \r\nstrategic alliances and the ecosystem in the software industry\n\nOutcome:\nin the course students will gain practical skills which will help them to understand and apply in \r\npractice the process of market segmentation, competitive analysis and strategic decision-making process that lead to the effective performance of the business strategy" . . "Presential"@en . "FALSE" . . "Entrepreneurship through innovation"@en . . "3" . "Developing a practical understanding of what the \r\nentrepreneurial process is from identifying an opportunity or analyzing a problem to be \r\naddressed through a creative solution, to developing an innovation and its successful \r\npresentation and feasibility. In order to realize this, analysis, creative and critical thinking, \r\nevaluation of different ideas, consistency in the development of the chosen solution, the \r\nappropriate preparation for its implementation and presentation must be applied. The course \r\npresents contemporary examples, discusses and discusses real situations and cases.\r\n\nOutcome: Not Provided" . . "Presential"@en . "FALSE" . . "Application of artificial intelligence in big data technologies"@en . . "3" . "Attention is being paid to basic artificial intelligence domains – search, representation, \nuncertainty. Discussed are approaches, techniques, representation techniques and basic \nalgorithms. Besides classical algorithms search topic considers heuristics and \napproximation as modelling strategies. The representation topic covers constraint \nsatisfaction, logical formalism and effective algorithms for logical inference. The \nuncertainty topic refers to probabilistic inference, formalisms for decision processes and \napproaches for uncertainty modeling. Algorithms used in practical artificial intelligence are\npresented. Application of investigated artificial intelligence models in natural language \nprocessing, vision, machine learning and robotics is discussed. \n\nOutcome:\nEquipped with theoretical knowledge and practical skills the students will be able to select the \nalgorithm that fits best for inference in a specific domain; to implement and tune artificial \nintelligence algorithms; to select the appropriate representation of artificial intelligence \nproblem or domain model, as well as to design models with desired representation." . . "Presential"@en . "FALSE" . . "Managing and business models for open source software projects"@en . . "3" . "The course Managing and Business Models for Open \r\nSource Software Projects is optional for students for specialty “Big Data Analytics”, Master’s \r\ndegree. The course teaches students basic business models, methods and techniques, knowing \r\nwhich is a must for a successful IT project management, particularly – the open-source ones in \r\nthe organizations in the sphere of manufacturing, services, in implementing technological, \r\nproduct and managerial innovations, R&D projects and so on\n\nOutcome: Not Provided" . . "Presential"@en . "FALSE" . . "Diploma project"@en . . "15" . "No Description, No Learning Outcome" . . "Presential"@en . "TRUE" . . "Advanced java programming"@en . . "4" . "After completing the course, students will acquire \r\nskills to create different types of Java applications, create applets, and develop desktop and \r\nclient / server applications, databases, JSF and Web Services.\n\nOutcome: Not Provided" . . "Presential"@en . "FALSE" . . "Metaheuristics"@en . . "4" . "The discipline \"Metaheuristics\" is developed thematically \r\nin the areas of mathematical models and adaptive problem-independent algorithmic frameworks, \r\nclassification of metaheuristics as part of computational intelligence and study of operations, \r\nmetaheuristics inspired by nature as genetic algorithms, artificial bee and ant colonies, wide \r\nspectrum of metaheuristics algorithms for solving wide spectrum of NP-hard problems based on \r\ntrajectories and populations. Particular attention is paid to mapping the metaphor of adaptive \r\nproblem-independent algorithmic frameworks to the specifics of the solved NP-difficult problem, \r\nas well as the development on this basis of effective software applications\n\nOutcome:\nstudents will:\r\n• Gain knowledge of mathematical foundations and problem-independent adaptive algorithmic \r\nframeworks and computer models for solving a wide range of NP-hard computational problems, \r\nmethodologies for coding the problem and mapping the metaphor of the algorithmic framework to \r\nthe specifics of the problem.\r\n• Acquire skills for implementation of software applications based on metaheuristic computer \r\nmodels;" . . "Presential"@en . "FALSE" . . "Mathematical methods for signal processing"@en . . "4" . "The main topics concern: general theory of Fourier analysis of analog signals (Fourier series, \r\nFourier transform); convolution; discrete sequences; discrete Fourier transform; introduction to \r\nsignal processing; linear time-invariant systems; discrete convolution operators; difference \r\nequations; signal analysis and processing with use of cross-correlation; circular convolution \r\ntheorem and cross-correlation theorem; numerical methods in signal studying and modelling \r\n(interpolation, least-squares method, numerical integration).\n\nOutcome: Not Provided" . . "Presential"@en . "FALSE" . . "Master of Big Data Analytics"@en . . "https://www.tu-sofia.bg/specialties/preview/863 https://www.tu-sofia.bg/uplanEn/FAMI/Master/UPlan_M_BDA_2020_EN.pdf" . "60"^^ . "Presential"@en . "The Master's Degree focuses on training related to the development of software for Big Data Analysis on Social Networks, Big Data Ecosystem Security, Digital Big Data and Computer Forensics and Intelligent Personalized Medicine Systems. The specialized disciplines are related to the most up-to-date areas of software science and informatics (cloud platforms and services, big data analysis, knowledge extraction, decision making). The practical orientation of the training is related to the development of coursework in compulsory disciplines and individual software projects in selected disciplines. A diploma thesis is being developed during the last academic semester. Graduates in the specialty “Analysis of Large Arrays and Data Flows” receive a Master's Degree in Computer Science, Big Data Analyst."@en . . . "1"@en . "FALSE" . . . "Master"@en . "Thesis" . "1000.00" . "Bulgarian Lev"@en . "7036.5" . "Recommended" . "The specialty “Big Data Analytics” is among the 5 with highest income for graduates according to the rating system of higher schools.\r\n\r\nThey could find work as:\r\n\r\n1. analyzers and software specialists in scientific institutes, organizations and companies, using contemporary information technologies;\r\n2. analyzers in software companies;\r\n3. analyzers in state administration, in the development of e-management;\r\n4. in financial and insurance institutions;\r\n5. university lecturers and research workers."@en . "1"^^ . "FALSE" . "Midstream"@en . . . . . . . . . . . . . . . . . . . . . "Bulgarian"@en . . "Faculty of Applied Mathematics and Informatics"@en . .