In this course, you will learn how to analyze and improve the performance of a computer system with a specific focus on data-intensive systems.

To investigate a system’s performance and come up with solutions to improve it, there are three key things to know.

1. How to devise a methodology to design experiments so that you can investigate the system’s performance,
2. The toolset to run the necessary experiments and collect the results, and
3. At least a basic understanding of the systems layers (hardware, operating systems, etc.) to be able to interpret your results and come up with solutions.

This is what we will cover in this course.

The subject of the course is functional, declarative programming in general and F# in particular.

This course gives an introduction to IoT as well as practical experience with the design and implementation of end-to-end IoT systems.

Practical examples and exercises will predominatly be from domains Energy and Environment.

This course introduces mobile app design and development using Kotlin and Jetpack Compose for the Android platform, with a focus on concurrency and modern architectural patterns.

This course gives a fundamental overview of Android programming concepts and the best practices for mobile app development.

The course "DevOps, Software Evolution and Software Maintenance" is a BSc elective.

In this course, the students will discover all the software engineering activities that take place after an initial software product is delivered or after a legacy system is taken over from a theoretical and practical perspective. Students (in groups) will take over such a system that is live and serving users, they will refactor and migrate it to the languages and technologies of their liking. All subsequent DevOps, software evolution and software maintenance activities will be performed directly on the systems of the students.

All use of generative artificial intelligence is allowed during the exam, as long as the student follows the official ITU guidelines on generative artificial intelligence.

The course "DevOps, Software Evolution and Software Maintenance" is an MSc elective.

In this course, the students will discover all the software engineering activities that take place after an initial software product is delivered or after a legacy system is taken over from a theoretical and practical perspective. Students (in groups) will take over such a system that is live and serving users, they will refactor and migrate it to the languages and technologies of their liking. All subsequent DevOps, software evolution and software maintenance activities will be performed directly on the systems of the students.

Algorithmic problem solving is the art of designing algorithms to solve challenging programming problems. This requires extensive practice in classifying problems, choosing the right data structures, and identifying suitable algorithms. This is very different to a standard algorithm and data structure course, where the focus is on understanding the tools at hand, but not on using them to solve problems. This course bridges the gap between theoretical knowledge and practical solutions to computational problems.

New course for KDDIT 2024 intake. Replaces KDDIT course Data Visualisation Design

The course addresses the process of datafication through the lenses of data visualization. Datafication is pervasive in contemporary society, presenting both challenges and opportunities for individuals and communities. However, it is neither a neutral nor a natural process. It occurs within specific sociotechnical systems, shaping and being shaped by their contexts, and has profound, far-reaching implications The course will explore this process dealing with the most urgent issues such as bias in data and data fairness and then, building of the critical understanding of data processes,  it will introduce the key elements of practical data visualization using Tableau.

New mandatory course for BDDIT 2026. Redesigned version of Philosophy of Science and Technology, DMD/B-DDIT (last offered Autumn 2024).  

The course provides a basic introduction to philosophical and ethical problems related to digital design. The course puts a special focus on digitality, exploring its foundations, ontology, and ethical implications.

This course will introduce classes of tasks that are at the core of most real-world production systems. It will teach advanced solutions to solve these tasks on complex and large-scale data with state-of-the-art tools.

This course concerns the use and design of advanced type systems for programming. Students will learn how to use dependent types to describe properties of programs, and to use session types to describe communication protocols. Students will also learn how to express a type system using typing rules. 

The course "IT Program Management" is a one semester 7.5 ECTS MSc elective in the MSc Computer Science program. The course focuses on program management theory and methods, key concepts, common research approaches, and case studies of IT programs. The course covers themes such as pitfalls of program management, IT program success and failure, and software engineering concepts including the software development life cycle and software development methodologies.

In this course, we teach both advanced machine learning (ML) approaches and hands-on skills for applying these approaches to data science problems.

This course introduces students to quantitative methods in the social sciences, with a focus on information systems and business research. Students learn to make valid statistical inferences from data, critique and design quantitative research, and apply business analytics and machine learning techniques to support organizational decision-making.

This course offers a comprehensive exploration of service design and management, equipping students with essential processes, principles, methods, and tools. It focuses on the strategic design and management of services, including digital components, and draws on expertise from various disciplines within service design and management. Through lectures, in-depth case studies, and interactive hands-on exercises, students will examine the characteristics and dynamics of services and service ecosystems. The course aims to deepen students' understanding of how services can be effectively designed, managed, and implemented to deliver substantial value to users, providers, and other stakeholders.

The goal of the course is to make students able to manage and use data sets, e.g. by learning about tools for data interpretation and visualization, and to reason about the use of data in larger contexts.

This course introduces students to cutting-edge robotics research and hands-on applications. Over several multi-week modules, students will work with modern robotic systems, learning both theory and practical implementation. Each module covers a specific area of robotics research, combining lectures with hands-on projects that let students apply what they've learned. The course emphasizes practical problem-solving and real-world applications of robotics technology.

In today's world of professional software development, ensuring your code works perfectly is not just a final step--it is an ongoing process. As systems become more complex, developers need more than just intuition and manual checks. They need systematic ways to test and analyze programs to catch bugs and validate behavior. This course is all about giving you the tools, techniques and hands-on experience to just do that.

In this course, we teach advanced machine learning (ML) techniques and hands-on skills for applying these techniques to natural language processing (NLP) problems.

This course introduces advanced machine learning techniques with a focus on the visual domain, emphasizing their role in both research and real-world applications. Students will gain practical skills through hands-on exercises to support theoretical understanding. We aim to enabling them to choose and adapt appropriate methods for specific tasks. In the age of generative AI, understanding the underlying principles is essential. The course contributes to developing AI literacy by introducing foundational concepts, diving into selected technical details, and fostering critical discussions around the capabilities and limitations of current approaches.

In this course, you will learn different aspects of modern data systems, in particular state-of-the-art data mangement techniques that have been used or proposed in industry and academia.

Learn how to analyze software systems to ensure they meet security policies under specific trust assumptions. This course covers topics such as information flow, declassification, non-interference, timing attacks, and program analysis tools, with a focus on reasoning about program behavior and securing data flows.

This course considers the nature as well as the challenges associated with ‘global project management and IT’.

This is an introductory course on information security. The course focuses on introductory aspects of analysis, design and implementation of secure software.

The course is an introduction to software engineering and software development for data science. The goal of the course is to allow students to join professional software engineering teams that include data scientist and data analysts.

Revideret kursus for BDDIT 2024. Erstatningskursus for tidligere BDDIT optag 

Formålet med kurset er at give de studerende en grundlæggende forståelse for kvantitative metoder og deres anvendelse i både forskning og designpraksis.

Contemporary organizations critically rely on enterprise systems and digital platforms to support and transform their business processes. In this course, students learn to analyze work systems within organizations and to identify ways for improving them. Students acquire these skills by analyzing a real-world work system and by gaining hands-on experience suggesting requirements for a sociotechnical system that supports the work system.

The course will teach students to analyse complexity within an empirical case that explores a current topic within the fields of science, technology, and innovation.

This is a course in mathematics covering linear algebra and analysis (calculus) of functions of several variables. These are perhaps the two areas of mathematics that have found most uses in practical applications. In particular, the course equips the student with mathematical tools necessary for analysis of big data.

This hands-on course teaches you how the design, implementation, and implications, of computer systems influence your task as an application programmer. You will get an in-depth understanding of how hardware and the operating system work; in the process, you will learn how computers actually work, and what actually happens at the lowest level when you run an application. This enables you to fully exploit underlying hardware and systems software to write high-quality software.

Discrete Mathematics covers different topics in mathematics, which support many disciplines in software development. The goal of this course is to give the students the ability to apply formal reasoning. The first part of the course is dedicated to learning how to construct logical proofs, proofs on set theory and proofs by induction, while the second half of the course builds upon the first part to cover number-theoretical concepts, graphs, combinatorics, discrete probabilities, and models of computation. The student will obtain the fundamental skill of computational thinking and will be better equipped to tackle technical subjects throughout the curriculum. The course is an introduction to discrete mathematics as a foundation to work within the fields of computer science, information technologies, and software development. The course develops the necessary terminology and conceptual tools needed for later courses.

This includes:

• formal reasoning, proofs, logic, set theory, sequences and sums
• number theory, combinatorics and (discrete) probability theory
• induction, recursion and counting
• relations and functions
• basic graph theory, language theory
• theory and models of computation, such as finite state machines, regular expressions and grammars

This course will introduce students to techniques for solving complex programming tasks arising in modern IT systems. The focus in the course is on algorithm design and analysis.

This course addresses advanced programming techniques, with a special attention on functional programming and its applications. The course is a perfect balance of theory and practice, with focus on the Scala programming language.

The course is designed to enable the student to engage with future developments in software engineering by providing a forum for the critical discussion of processes, methods, tools, and architectures. By combining theoretical insights from state-of-the-art research and hands-on experience with advanced tools and techniques, students gain a comprehensive understanding of software engineering.

This course is an advanced course on algorithms which builds on top of an introductory course on algorithms and data structures. The course focuses on advanced techniques for identifying and solving computationally hard problems and on how to adapt such techniques to real-world scenarios.

This course is about that part of programming that focuses on parallelism and concurrency. It covers concurrency concepts that are universal to any programming language. The Java programming language is the main language used for practically addressing such aspects; and Erlang is also used for a topic in the course.

This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.

In this course students will learn to apply modern state-of-the-art solutions for natural language processing problems. We go beyond simple classification tasks, and tackle more advanced types of tasks, like generation and structured prediction. 

Networks are all around us: We are ourselves, as individuals, the units of a network of social relationships of different kinds; the Internet and the highway system can be modelled as networks embedded in space; networks can be also entities defined in an abstract space, such as networks of acquaintances or collaborations between individuals. This course aims at providing the computational tools to study these networks and form an advanced understanding of the current state of the art of network science. The final objective is to have the students master computational techniques to solve advanced network problems, to be able to contribute to the development of network analysis, and to appreciate the limitations and future developments of scientific papers dealing with network problems in real-world data.

Kursets formål er at give en dybere praktisk og teoretisk forståelse for implementation af programmeringssprog, herunder især maskinmodellen bag Java, C# og F#. Kursusemnerne omfatter leksikalsk analyse, grammatikker, syntaksanalyse, abstrakt syntaks, stakmaskiner, virtuelle maskiner, mellemkodesprog (inklusive JVM og .NET CLI), og continuations. Endvidere implementering af fortolkere, typeinferens, oversættere for en delmængde af C og for en delmængde af SML, garbage collectors, og kodegenerering for virtuelle maskiner og maskinkode (fx x86).

This hands-on course teaches you how the design, implementation, and implications, of computer systems influence your task as an application programmer. You will get an in-depth understanding of how hardware and the operating system work; in the process, you will learn how computers actually work, and what actually happens at the lowest level when you run an application. This enables you to fully exploit underlying hardware and systems software to write high-quality software.

Today we rarely use programs that are developed to fit one specific customer. When we play games, use business systems for administration, or download apps to our phones, we use software that is part of software ecosystems: software products that are configured to fit a specific business context; platforms like game engines that allow the development and sharing of software; or software as a service. Examples of software ecosystems have developed around software products, like game engines, or enterprise resource planning (ERP or CRM) software, integrated development environments like visual studio and platforms like the Android operating system.

In this course you explore software ecosystems from UX design, organizational and technical perspectives. To this end you will analyse a software ecosystem together, propose an improvement related to the ecosystem, and support your proposal with a prototype and / or other design artefacts.

This course enables students to understand the effects of automated digital systems on the way a welfare society is governed and experienced by social groups and individuals.

The course aims at giving the students an introduction to the complexities of determining how processes of digitalisation and green transitions are entangled.

The course aims at giving the students an introduction to the complexities of determining how processes of digitalisation and green transitions are entangled.

Students attending this course will study how to design game code that is scalable and takes into account hardware and software resources.

This course examines organizations in light of contemporary challenges with digitalization. It explores organizational perspectives on digitalization and cover theories that can help us better understand, and critically reflect upon, such profound challenges, while also offering hands-on approaches to managing them. Furthermore, it investigates questions of the changing nature of work, and themes such as leadership, management, and organizing in order to make students both knowledgeable about, and skilled in the practice of, and engaging with, digital transformation.

The course gives an in-depth introduction to fundamental principles of probability theory.

The course aims to teach the understanding, design, implementation, and use of state-of-the-art artificial intelligence (AI) techniques applied to games and other simulation environments. We mainly cover methods to control agents (NPC, bots, robots, etc.), focusing on neural network-based approaches but also tree search techniques such as MCTS. Additionally, this course covers generative AI methods and their use for environment/game level generation, in addition to exploring the use of Large Language Models for games. 

The course is for students looking for an introduction to strategic management to be able to understand and navigate todays and tomorrows business world.

The student will be given a toolbox of academic frameworks and models to be able to analyze current business situations and propose strategic changes. Through case studies and group work the student will become familiar with the business language which will help to bridge the gap between business and technology.