. "Computer Science"@en . . "English"@en . . "Mathematics"@en . . "Economics"@en . . "Introduction to data science and analytics"@en . . "8" . "This course will examine how data analysis \ntechnologies can be used to improve decision-making. \nThe aim is to study the fundamental principles and \ntechniques of data science, and we will examine real\u0002world examples and cases to place data science \ntechniques in context, to develop data-analytic \nthinking, and to illustrate that proper application is as \nmuch an art as it is a science. In addition, this course \nwill work hands-on with the Python programming \nlanguage and is associated data analysis libraries.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Probability and statistics for data science"@en . . "8" . "This is a theoretical course covering fundamental \r\ntopics of probability and statistics in the context of \r\ndata science with its inherent challenges. This course \r\nwill start with a review of fundamental probability, \r\ncovering topics like random variables, their \r\ndistribution functions, expected values, conditioning \r\non certain events and independence. The students \r\nwill be acquainted with certain families of probability \r\ndistributions and then will learn how to estimate \r\ncertain quantities of interest from observations. \r\nA range of properties of estimators will be studied, \r\nincluding sufficiency, unbiasedness and consistency, \r\nwhich enable the evaluation of their quality with an \r\nemphasis in the framework of big datasets. \r\nThe students will also learn how to introduce \r\ndifferent types of hypotheses, how to construct tests \r\nfor their hypotheses, as well as how to compare \r\nbetween tests and how to construct confidence \r\nintervals for their estimators.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Statistical simulation and data analysis"@en . . "8" . "The students will be introduced to the R programming \r\nlanguage, a programming language that was \r\nspecifically developed for analyzing data, and \r\nis today widely used in most organizations that \r\nconduct data analysis. The students will learn how \r\nto explore datasets in R, using basic visualization \r\ntools and summary statistics, how to run different \r\nkinds of regressions and analyses, and how to \r\nperform statistical inference in practice, for example \r\nhow to test certain hypotheses regarding the data or \r\nhow to compute confidence intervals for quantities \r\nof interest. The students will also learn how to use R \r\nin order to conduct simulations, an extremely useful \r\ntool that can fulfill a wide range of analytical tasks. \r\nSimulation techniques covered will include Monte \r\nCarlo, importance sampling and rejection sampling. \r\nFinally, the students will learn how to estimate the \r\nprecision of computed sample statistics using \r\nresampling methods. The course uses a hands-on \r\napproach, with nearly half the work done in the lab.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Big data analytics"@en . . "8" . "This course seeks a balance between foundational \r\nbut relatively basic material in algorithms, statistics, graph theory and related fields, with real-world \r\napplications inspired by the current practice of \r\ninternet and cloud services. Specifically, this \r\ncourse will look at social & information networks, \r\nrecommender systems, clustering and community \r\ndetection, search/retrieval/topic models, \r\ndimensionality reduction, stream computing, and \r\nonline ad auctions. Together, these provide a \r\ngood coverage of the main uses for data mining \r\nand analytics applications in social networking, \r\ne-commerce, social media, etc. The course is a \r\ncombination of theoretical materials and weekly \r\nlaboratory sessions, where several large-scale \r\ndatasets from the real world will be explored. \r\nFor this, students will work with a dedicated \r\ninfrastructure based on Hadoop & Apache Spark.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Business analytics applications"@en . . "8" . "This course presents knowledge and skills for \r\napplying business analytics to managerial decision\u0002making in modern organizations. Key topics include \r\ndescriptive, predictive, and prescriptive analytics, \r\nmeasuring the economic value of information in \r\nanalytics investments, and using data to improve \r\ndecision making under risk and uncertainty. \r\nSpecifically, students will learn how to use data and \r\nanalysis to make better decisions across different \r\nfunctional areas of the organization.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Statistical learning"@en . . "8" . "Students will acquire the knowledge to conduct \r\nstatistical analysis on a variety of data sets using a \r\nwide range of modern computerized methods. The \r\nstudents will learn how to recognize which tools \r\nare needed to analyze different types of datasets, \r\nhow to apply these tools in each case, and how to \r\nemploy diagnostics to assess the quality of their \r\nresults. They will learn about statistical models, their \r\ncomplexity and their relative benefits depending \r\non the available data. Some of the tools that the \r\nstudents will come to learn well include linear simple \r\nand multiple regression, nearest neighbors methods,\r\nshrinkage methods (ridge, lasso), dimension \r\nreduction methods (principal components), logistic \r\nregression, linear discriminant analysis, tree-based \r\nmethods, model selection algorithms with criterion \r\nor by resampling techniques and clustering. \r\nThe focus of the course will be less on theory \r\nand more on providing the students with as much \r\nintuition as possible and acquainting them with as \r\nmany methods as possible. The course will make \r\nsubstantial use of the R statistical programming \r\nlanguage and its libraries. \n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Capstone project in data science (1st phase)"@en . . "5" . "The capstone project has been designed to apply \r\nknowledge into practice and to develop and \r\nimprove critical skills such as problem-solving and \r\ncollaboration skills. Students are matched with \r\nresearch labs within the UCY community and with \r\nindustry partners to investigate pressing issues, \r\napplying data science areas. Capstone projects aim \r\nto give students some professional experience in a \r\nreal work environment and help enhance their soft \r\nskills. These projects involve groups of roughly 3-4 \r\nstudents working in partnership.\r\nThe process is the following:\r\n• A short description of projects are announced \r\nto students. \r\n• Students bid up to three projects taking into \r\naccount the fields of their interest or research.\r\n• The data science directors make the final \r\nassignment of projects to students. The projects \r\nare under the supervision of a member of the \r\nProgramme’s academic staff.\r\n• Specific learning outcomes are stipulated in a \r\nlearning agreement between the student, the \r\nsupervisor and the company.\r\n• The student keeps a log file of his/her work and \r\nat the end writes a progress report (6000 words).\r\n• The company is obliged to monitor the progress \r\nof the students and to provide relevant \r\nmentorship.\r\nFinal assessment is carried out by the company and \r\nthe supervisor.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Capstone project in data science (2nd phase)"@en . . "5" . "The capstone project has been designed to apply \r\nknowledge into practice and to develop and \r\nimprove critical skills such as problem-solving and \r\ncollaboration skills. Students are matched with \r\nresearch labs within the UCY community and with \r\nindustry partners to investigate pressing issues, \r\napplying data science areas. Capstone projects aim \r\nto give students some professional experience in a \r\nreal work environment and help enhance their soft \r\nskills. These projects involve groups of roughly 3-4 \r\nstudents working in partnership.\r\nThe process is the following:\r\n• A short description of projects are announced \r\nto students. \r\n• Students bid up to three projects taking into \r\naccount the fields of their interest or research.\r\n• The data science directors make the final \r\nassignment of projects to students. The projects \r\nare under the supervision of a member of the \r\nProgramme’s academic staff.\r\n• Specific learning outcomes are stipulated in a \r\nlearning agreement between the student, the \r\nsupervisor and the company.\r\n• The student keeps a log file of his/her work and \r\nat the end writes a progress report (6000 words).\r\n• The company is obliged to monitor the progress \r\nof the students and to provide relevant \r\nmentorship.\r\nFinal assessment is carried out by the company and \r\nthe supervisor.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Information retrieval and search engines"@en . . "8" . "_Introduction to Information Retrieval\r\n_Boolean Retrieval\r\n_Text encoding: tokenisation, stemming, \r\nlemmatisation, stop words, phrases. \r\n_Dictionaries and Tolerant retrieval \r\n_Index Construction and Compression\r\n_Scoring and Term Weighting\r\n_Vector Space Retrieval\r\n_Evaluation in information retrieval \r\n_Relevance feedback/query expansion \r\n_Text classification and Naive Bayes\r\n_Vector Space Classification\r\n_Flat and Hierarchical Clustering\r\n_Web Search Basics \r\n_Web crawling and indexes\r\n_Link Analysis\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Advanced topics in data management"@en . . "8" . "Fundamentals of modern Database Management \r\nSystems (DBMSs): storage, indexing, query \r\noptimization, transaction processing, concurrency \r\nand recovery. Fundamentals of Distributed DBMSs, \r\nWeb Databases and Cloud Databases (NoSQL / \r\nNewSQL): Semi-structured data management (XML/\r\nJSON, XPath and XQuery), Document data-stores \r\n(i.e., CouchDB, MongoDB, RavenDB), Key-Value \r\ndata-stores (e.g., BerkeleyDB, MemCached), \r\nIntroduction to Cloud Computing (NFS, GFS/\r\nHadoop HDFS, Replication/Consistency Principles), \r\nBig-data processing/analytic frameworks (Apache \r\nMapReduce/PIG, Spark/Shark), Column-stores \r\n(e.g., Google’s BigTable, Apache’s HBase, Apache’s \r\nCassandra), Graph databases (e.g., Twitter. \r\nFlockDB) and Overview of NewSQL (Google’s \r\nSpanner/F1). Spatio-temporal data management \r\n(trajectories, privacy, analytics) and index structures \r\n(e.g., R-Trees, Grid Files) as well as other selected \r\nand advanced topics, including: Embeeded \r\nDatabases (sqlite), Sensor / Smartphone / Crowd \r\ndata management, Energy-aware data management, \r\nFlash storage, Stream Data Management, etc.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Natural language processing"@en . . "8" . "Natural language processing (NLP) is one of the most \r\nimportant technologies of the information age, and \r\na crucial part of artificial intelligence. Applications of \r\nNLP are everywhere because people communicate \r\nalmost everything in language: web search, \r\nadvertising, emails, customer service, language \r\ntranslation, medical reports, etc. In this course, \r\nseveral models and algorithms for automated textual \r\ndata processing will be described: \r\n>1morpho-lexical level: electronic lexica, spelling \r\ncheckers; \r\n>2 syntactic level: regular, context-free, stochastic \r\ngrammars, parsing algorithms; \r\n>3 semantic level: models and formalisms for the \r\nrepresentation of meaning. \r\nSeveral application domains will be presented: \r\nLinguistic engineering, Information Retrieval, Text \r\nmining (automated knowledge extraction), Textual \r\nData Analysis (automated document classification, \r\nvisualization of textual data).\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Deep learning"@en . . "8" . "The course aims at teaching the required skills to \r\nuse deep learning methods on applied problems. \r\nIt will show how to design and train a deep \r\nneural network for a given task, and the sufficient \r\ntheoretical basis to go beyond the topics directly \r\nseen in the course. The planned content of \r\nthe course:\r\n• What is deep learning, introduction to tensors.\r\n• Basic machine-learning, empirical risk \r\nminimization, simple embeddings.\r\n• Linear separability, multi-layer perceptrons, \r\nback-prop.\r\n• Generalized networks, autograd, batch processing, \r\nconvolutional networks.\r\n• Initialization, optimization, and regularization.\r\nDrop-out, activation normalization, skip \r\nconnections.\r\n• Deep models for Computer Vision.\r\n• Analysis of deep models.\r\n• Auto-encoders, embeddings, and generative models.\r\n• Deep learning for sequences - Recurrent neural \r\nnetworks (RNNs); vanishing and exploding \r\ngradients; Long Short-Term Memory (LSTM); \r\ndeep RNNs; bidirectional RNNs; combination of \r\nCNNs with RNNs - pytorch tensors, deep learning \r\nmodules, and internals.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Cloud computing"@en . . "8" . "This course covers topics and technologies \r\nrelated to Cloud Computing and their practical \r\nimplementations. The course is organized in four \r\nparts focising on: (i) Fundamental concepts and \r\nmodels of Cloud Computing; (ii) Cloud-enabling \r\ntechnologies: warehouse-scale machines, \r\nvirtualization, and storage; (iii) Cloud application \r\nprogramming models and paradigms. (iv) Cloud \r\nresource orchestration, monitoring, and DevOps. \r\nThe student will explore different architectural and \r\nservice models of cloud computing, the concepts \r\nof virtualization, containerization, and cloud \r\norchestration. Through lectures, tutorials, and \r\nlaboratory sessions, the student will gain hands\u0002on experience with various features of popular \r\ncloud platforms, such as Openstack, VMWare, \r\nDocker, and Kubernetes, as well as commercial \r\nofferings like Google App Engine, Microsoft \r\nAzure and Amazon Web Service. Advanced \r\ncloud programming paradigms such as Hadoop’s \r\nMapReduce and Microservices are also included in \r\nthe course. Students will also learn the concept of \r\nmodern Big Data analysis on cloud platforms using \r\nvarious data mining tools and techniques. The lab \r\nsessions will cover cloud application development \r\nand deployment, use of cloud storage, creation \r\nand configuration of virtual machines and data \r\nanalysis on cloud using data mining tools. Different \r\napplication scenarios from popular domains that \r\nleverage the cloud technologies such as online \r\nsocial networks will be explained. The theoretical \r\nknowledge, practical sessions and assignments aim \r\nto help students to build their skills to develop large\u0002scale industry standard applications using cloud \r\nplatforms and tools.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Data security"@en . . "8" . "Processing data is often realized through systems that can operate under hostile conditions, where adversaries try to monetize access to sensitive data. In this course we provide a short introduction of data security, and we review the basic arsenal we have for protection. We cover a large portion of applied cryptographic primitives and protocols that facilitate secure transmission of data. We then proceed and review how systems that process data can be attacked and protected. Finally, we discuss advanced attacks, and potential defenses, for systems that are based on Machine Learning.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Data visualization"@en . . "8" . "Introduction to Data visualization, Web development, \r\nJavascript, Data driven documents (D3.js), \r\nInteraction, filtering, aggregation, Perception, \r\ncognition, Designing visualizations (UI/UX), Text \r\nvisualization, Graphs, Tabular data viz Music viz, \r\nIntroduction to scientific visualization, Storytelling \r\nwith data / data journalism, Creative coding.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Survey sampling"@en . . "8" . "Survey design, sampling and nonsampling errors, \r\nsimple random sampling, stratified sampling, \r\nsystematic sampling, cluster sampling, ratio \r\nestimators, regression estimators, determination \r\nof optimal sample size, bias in survey sampling, \r\nmodern techniques of survey sampling.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Time series analysis"@en . . "8" . "Stochastic processes, weak and strong stationarity. \r\nAutoregressive and moving average based models \r\nfor stationary and non-stationary time series. \r\nTrend and seasonal behaviour, sample \r\nautocorrelation function and sample partial \r\nautocorrelation function. Parameter estimations, \r\nmodel identification, prediction. ARMA, ARIMA \r\nand SARIMA models. Properties, estimation and \r\nexamples. ARCH and GARCH models for volatility.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Multivariate analysis"@en . . "8" . "This course studies topics from multivariate \r\nstatistical analysis. Topics covered include: \r\nrandom vectors, measures of center and variation \r\nin multivariate moments. Multivariate normal \r\ndistribution. Tests for normality. Estimation of \r\nthe mean vector and the variance analysis, \r\nindependence, multivariate –covariance matrix. \r\nWishart and Hotelling distributions. Statistical \r\ninference. Union – Intersection Test. Confidence \r\nregions. Multivariate analysis of variance and \r\nmultivariate regression analysis. Least squares \r\nmethod and Wilks distribution. Analysis of \r\ncovariance. Principal components, Factor analysis, \r\nDiscriminant analysis, Cluster analysis. The R \r\nstatistical programming language will be used for \r\napplying the introduced methods in a range of Data \r\nScience problems.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Bayesian statistics"@en . . "8" . "This course introduces Bayesian Statistics, \r\nan intuitive approach to Statistics allowing for \r\nbetter accounting of uncertainty. Topics include: \r\nsubjective probability, Bayes rule, prior and posterior \r\ndistributions, conjugate and non-informative \r\npriors, point-wise estimation and credible intervals, \r\nhypothesis testing, introduction to Bayesian \r\ndecision analysis, introduction to empirical Bayes \r\nanalysis, introduction to Markov chain Monte Carlo \r\ntechniques. The course will make use of R statistical \r\nprogramming language for the implementation \r\nof algorithms for extracting information from the \r\nposterior and for the application of the introduced \r\nmethods in a range of Data Science problems.\r\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Computational statistics"@en . . "8" . "Multiple regression, Cholesky decomposition, \r\ndiagnostics and collinearity, principal components \r\nand eigenvalue problems. Nonlinear statistical \r\nmethods: Maximum likelihood estimation, Newton\u0002Raphson and related methods, multivariate data \r\nand the Newton Raphson method, optimization \r\ntechniques (unconditional and under constraints) EM \r\nalgorithm. Numerical Integration and Approximation: \r\nNewton-Coates method, spline interpolation, Monte \r\nCarlo integration, general approximation methods. \r\nProbability Density Estimation: Histogram, linear and \r\nnon-linear smoothing, splines. Bootstrap.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Managing business processes with information systems & analytics"@en . . "8" . "This course provides students the key tools \r\nto analyze and improve business processes in \r\norganizations, with an emphasis on the service \r\nsector. This is achieved by bringing together key \r\nideas from the fields of information systems, \r\nbusiness analytics, and business process design \r\nand management. The course introduces the \r\nfundamental types of information systems, including \r\nenterprise-wide systems (ERP, SCM, CRM), and \r\nthe basic principles of supporting business strategy \r\nwith Information Systems. The students will learn \r\nhow to use information systems to support their \r\norganization’s business processes, and how to use \r\nbusiness analytics and business process modeling \r\ntechniques to inform key decisions during Business \r\nProcess Re-engineering. The students will be \r\nintroduced to different business analytics systems \r\nin fields such as marketing, retail, supply-chain \r\nmanagement, e-commerce, etc. and will learn how \r\nto measure business process performance through \r\nappropriate metrics and frameworks (e.g. the \r\nBalanced Scorecard approach)\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Project management using analytical tools"@en . . "8" . "This course examines the project management \r\nprocess with a focus on business analytics \r\ntechniques to overcome the pitfalls and obstacles \r\nthat frequently occur during a typical project. \r\nDesigned for business leaders responsible for \r\nimplementing projects, as well as beginning and \r\nintermediate project managers. Includes topics \r\non planning and scheduling issues, costing \r\nand budgeting, staffing and organizing, project \r\nmanagement methodologies, and the use of data to \r\ninform the project manager’s decisions throughout \r\nthe project’s lifecycle. During the course, computer \r\nsoftware dealing with project management will also \r\nbe presented.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Information networks"@en . . "8" . "Topics include: how to model the formation of \r\nsocial and economic networks; understand and \r\nmeasure certain patterns of real-world networks; identify, quantify and model how opinions, fads, \r\npolitical movements and diseases spread through \r\ninterconnected systems and measure the robustness \r\nand fragility of them. We will bring together models \r\nand techniques from economics, sociology, math, \r\nphysics, statistics and computer science to answer \r\nthese questions.\r\nIn more detail the course will include: Repetition \r\nof Statistical Definitions, Background and Network \r\nElements, Networking, Social Networking & \r\nBehavioral Contagion, Project Management \r\nNetworks, Economic complexity, Visualization \r\nof Networks\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Quantitative and qualitative decision-making"@en . . "8" . "This course explores decision making and policy \r\nformulation in organizations. Includes goal \r\nsetting and the planning process, rational models \r\nof decision making, effective combination of \r\nqualitative and quantitative data (e.g. triangulation, \r\ncomplementarity etc.) with respect to the goal set, \r\nevaluation of alternatives, prediction of outcomes, \r\ncost-benefit analysis, decision trees, uncertainty \r\nand risk assessment, and procedures for evaluation \r\nof outcomes\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Web analytics for business"@en . . "8" . "The course explores web analytics, text mining, web \r\nmining, and practical application domains. The web \r\nanalytics part of the course studies the metrics \r\nof websites, their content, user behavior, and \r\nreporting. The Google analytics tool is used for \r\ncollection of website data and doing the analysis. \r\nThe text mining module covers the analysis of \r\ntext including content extraction, string matching, \r\nclustering, classification, and recommendation \r\nsystems. The web mining module presents how \r\nweb crawlers process and index the content of web \r\nsites, how search works, and how results are ranked. \r\nApplication areas mining the social web and game \r\nmetrics will be extensively investigated\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Data mining for business analytics"@en . . "8" . "Enterprises, organizations and individuals are \r\ncreating, collecting, and using massive amount \r\nof structured and unstructured data with the \r\ngoal to convert the information into knowledge, \r\nto improve the quality and the efficiency of their \r\ndecision-making process, and to better position \r\nthemselves to the highly competitive marketplace. \r\nData mining is the process of finding, extracting, \r\nvisualizing and reporting useful information and \r\ninsights from both small and large datasets with \r\nthe help of sophisticated data analysis methods. \r\nIt is part of the business analytics, which refers \r\nto the process of leveraging different forms of \r\nanalytical techniques to achieve desired business \r\noutcomes through requiring business relevancy, \r\nactionable insight, performance management, and \r\nvalue management. The students in this course will \r\nstudy the fundamental principles and techniques of \r\ndata mining. They will learn how to apply advanced \r\nmodels and software applications for data mining. \r\nFinally, students will learn how to examine the overall \r\nbusiness process of an organization or a project with \r\nthe goal to understand (i) the business context where \r\nhidden internal and external value is to be identified \r\nand captured, and (ii) exactly what the selected data \r\nmining method does\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Financial theory"@en . . "8" . "The course presents the theory of financial \r\ndecisions and corporate policy. It covers discounted cash flow and contemporary methods of capital \r\nbudgeting (comparison of techniques, relevant \r\ncash flows, projects with different lives, optimal \r\ntiming, constraints, inflation), risk and uncertainty, \r\nmean-variance portfolio choice, capital asset \r\npricing models and arbitrage pricing theory, efficient \r\nmarkets, capital structure and dividend policy, basic \r\noption pricing, corporate restructuring and mergers \r\nand acquisitions.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Investments"@en . . "8" . "The course covers the basic principles of investment \r\nanalysis and valuation, with emphasis on security \r\nanalysis and portfolio management in a risk-return \r\nframework. Security analysis focuses on whether \r\nan individual security is correctly valued in the \r\nmarket (i.e., it is the search for mispriced securities). \r\nPortfolio management deals with efficiently \r\ncombining securities into a portfolio tailored to the \r\ninvestor’s preferences and monitoring/evaluating \r\nthe portfolio. The course covers both the theory and \r\npractical aspects of investments.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Master of Science in Data Science"@en . . "https://datascience.cy/" . "90"^^ . "Presential"@en . "The MSc in Data Science programme brings together the expertise of three departments in the University of Cyprus – Business and Public Administration, Computer Science, and Mathematics and Statistics – to prepare the next generation of data-oriented thinkers.\n\nData science is a modern interdisciplinary field that uses scientific methods, processes, algorithms and systems in order to extract knowledge and insights from data. The programme’s objective is to offer a strong understanding of basic and advanced methods in statistical inference, machine learning, data visualization, data mining, and business analytics.\n\nThe programme is designed for students that have a background in STEM or in Business and Economics, who can demonstrate good knowledge of English, and who, as part of their undergraduate degree, have completed an introductory statistics course, as well as a course in a programming language such as Python and/or R (see Admissions for further information).\n\n\nThe duration of the programme is 18 months (90 ECTS). The first two semesters are dedicated to core courses, and homogenize students’ knowledge on the basics of data science. The third semester allows students to select one of three tracks: Computational Science, Statistics, and Business Analytics. In the summer semester the Capstone project, brings students in contact with real world problems and helps them cement the knowledge and skills they acquired.\n\nOutcome:\nAt the completion of the programme, students will have gained skills that are critical in a modern data-driven world, and will be in a position to think across disciplines and to transform data into actionable insights.\n\nIntended learning outcomes:\nBuild strong background in Data Science\n_ Master powerful tools that address a wide range \nof topics in Data Science\n_ Acquire statistical skills at an appropriately \nadvanced level \n_ Acquire deep knowledge in one or more fields \nof Data Science \n_ Obtain familiarity with basic concepts in other \nNatural and/or Social Sciences, pertinent to \ndata-driven discovery \n• Build research foundations\n_ Get acquainted with faculty research in fields of \nData Science \n_ Demonstrate in depth understanding of a \nbreadth of disciplines, and become familiar with \nthe dominant research directions \n_ Acquire experience of independent work, ideally \nso in the context of class research projects \n• Learn to solve real-world problems\n_ Identify and assess the needs of an organization \nfor a data science task \n_ Collect and manage the data needed \n_ Interpret data science analysis outcomes \n_ Transform findings into actionable business \nstrategies \n_ Communicate data science-related information \neffectively using audience-appropriate format \nand delivery \n_ Value and safeguard the ethical use of data \n• Build multi-context skills\n_ Develop transferable skills such as: oral and \nwritten scientific communication, near fluent \nuse of scientific English, use of information/\ncommunication technology, organization and \nplanning of group work \n_ Exhibit versatility and innovative thinking in \naddressing and managing open questions in \na variety of contexts, as an essential asset \nfor careers in research, industry, commerce, \neducation and the public sector"@en . . . . "1.5"@en . "TRUE" . . "Master"@en . "None" . "5125.00" . "Euro"@en . "5125.00" . "Mandatory" . "No Job Prospects Listed"@en . "3"^^ . "TRUE" . "Midstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Joint Program by (1) Facuty of Economics and Management and (2) Faculty of Pure and Applied Sciences."@en . .