50業子

The digital revolution has seen data science and artificial intelligence (AI) become crucial elements of everyday life, for tasks ranging from translation and self-driving vehicles to logistics and healthcare.

Machine learning and the technologies and methodologies for processing enormous amounts of data are also creating a wealth of new opportunities. Consequently, skilled data scientists and AI engineers are in huge demand.

This master’s programme in Data Science and AI will train you to undertake a wide variety of challenges in handling and analysing data, using, and developing software in complex applications. You will graduate with a solid foundation in machine learning, resulting in a fantastically wide range of options.

Data Science and AI:

With the digital revolution, Data science and Artificial Intelligence (AI) has become an important part of our lives and society as a whole. In addition, the quickly emerging technologies for processing large-scale data and machine learning is creating a wealth of opportunities where automated decision-making is becoming a reality. Skilled data scientists and AI engineers are in high demand everywhere. With a solid foundation in machine learning, you will have a wide range of career opportunities.

Data science is a highly cross-disciplinary field concerned with how to extract useful knowledge from data, for deeper understanding and decision support. It is based on a blend of methods in statistics and machine learning, together with computational techniques and algorithms for handling large-scale data. Examples of application areas include biology and other sciences, healthcare, business, finance, and different kinds of internet data. Computational methods range from algorithms for collecting and handling large-scale data, statistical methods such as Bayesian modelling, to machine learning techniques such as deep neural networks.

AI is about building intelligent systems and is currently a rapidly evolving field thanks to recent advances in machine learning with large-scale data, where machine learning enables the computer to perform complex tasks without being explicitly programmed. Successful examples of this approach include machine translation, computer vision, game playing and self-driving vehicles.

Through their use of large-scale data and machine learning, the fields of Data science and AI are closely connected. They are also connected in their application since it is common to first collect and analyse data to better understand the problem, and then to build algorithms and systems for decision support and autonomous decision-making. Therefore, with an increased demand for advanced information systems and computer applications in a wide range of areas, Data science, and AI are becoming necessary ingredients in software development in general.

Topics covered:

The subjects of artificial intelligence, design of AI systems and stochastic processes���are fundamental areas in the Data science and AI master’s programme.���The courses included in the programme���plan handle topics such as machine learning, databases and algorithms.

The overall aim of the programme is to educate engineers who can undertake a wide variety of challenges in handling and analysing different kinds of data, and who are able to use and develop software in complex data-intensive and AI-related applications. This requires a good understanding of both theory and practice, including the possibilities and limitations of existing and evolving technologies, and how to responsibly apply these in various situations.

The courses of the programme will provide a solid foundation in machine learning, statistics, and optimization, with an in-depth understanding of the mathematical modelling techniques used for extracting information from large sets of complex data, and with the computational skills and algorithms for working with such data. You will also gain familiarity with a range of common problems within Data science and AI which can be solved with such techniques.

Through a combination of theory and practice in the courses of the programme, you will gain an understanding of how and why certain models and algorithms work and will be able to identify their possibilities and limitations. You will be able to approach a real-world problem in a specific problem domain, combining existing and new methods to create an efficient solution. You will be able to continuously learn in these rapidly evolving fields, communicate with experts and non-experts in specific problem domains, and apply these technologies responsibly. You will also gain the insights to be able to understand and influence the role of Data science and AI in society.

Career outcomes:

There is a huge demand for engineers with a solid foundation in Data science and AI, and as the computational power and the amount of data available rapidly increase, the need will only continue to grow. The programme will lead to a wide range of career opportunities within many different application domains, e.g. virtually every other engineering discipline, as well as within medicine and finance. You will be well equipped to pursue a career in industry or government, as well as for further doctoral studies and an academic career.

Any organization that works with data analysis, and or the development of computational tools, either as their actual end product or as means for further improvement of the internal work, require both data scientists and AI engineers.

Such processes are often iterative, and both data science and AI engineering skills are needed in each step:

• Data management: gathering, cleaning, transforming and storing data
• Data analysis: identify trends, patterns and relationships in large data sets.
• Tool development: use, develop and improve intelligent computer algorithms and tools to be robust, flexible and scalable
• Machine learning: train and test tools and applications on relevant, clean data
• Communication: interpret, visualize and communicate essential findings from the data analysis
• Decision-making: support and improve the decision-making process