We are living in an age of data-intensive sciences, which is an alternative to the traditional hypothesis-driven approach. Meanwhile, there is a growing trend of using complex systems methods not just in natural sciences, but also in economics or sociology. A proof of these phenomena is the appearance of the latest journal of the European Journal of Physics family called EPJ Data Science that collects articles from all fields having common questions.
The aim of the journal is to find answers to these question using experimental data, simulations and data mining. However, the methods used contain not only traditional statistics or programming, but they are combined with machine learning and artificial intelligence.
The recent changes have pushed the job market also in a direction, there the demand for workforce with low qualifications constantly decreases. The progress of automation makes jobs that can be automated disappear, while there is an ever growing need for digitally creative people who can design and implement the automated systems. Firms and governments have begun to measure the output of all of their activities, and operate their systems and base their decisions on the enormous amount of data collected. That is why parallel to experts in several fields such as economists, bankers, sociologists, engineers, biologists etc., there is a growing need for professionals familiar with data collection, storage, modeling and analysis techniques.
Departments
Research fields
Researchers
Graduates
Besides a syllabus based on state of the art computational techniques and data analysis approaches, the programme also offers a well-structured knowledge base for those who wish to work in either corporate settings or in highly specialized academic fields. Our lecturers not only teach, but also actively research their area of expertise.
For those who plan to work in a more industrial or corporate setting
People from diverse educational backgrounds are welcome
Real research projects available for all students
The scientific data analytics and modelling programme is a specialisation track of the physics master's programme offered by Eötvös Loránd University. Enrollment into the specialisation track requires admission to the master's programme and the completion of advanced physics courses during the first semester. For more details, see the links below.
Eötvös Loránd University Faculty of Science Institute of PhysicsThe course offering spans three semesters, starting with the second semester of the two-year physics master's programme. It consists of six compulsory courses including 4 theoretical courses (supported by practical lab sessions) for 4 credits each and 2 project-oriented computer labs for 5 credits each. An additional 8 credits must be earned by completing restricted choice courses covering related fields of physics.
The material of a few compulsory BSc courses are considered basic knowledge throughout the Master's courses.
The whole syllabus of these courses are prerequisites of the Master's programme
Compulsory courses, computer labs and restricted choice courses offered by the programme
Our faculty members and researchers participate in a wide variety of research projects ranging from astronomy through biology and quantum mechanics to sociology. All fields incorporate state of the art knowledge and computational techniques, and thus are always open to enthusiastic students with creative ideas.
Modern experiments generate data in such large quantities that automatized machine learning techniques are fundamental to data analysis. Learn about how to extract knowledge from data by supervised and unsupervised machine learning methods widely used from astronomy to social sciences.
With the advance of high performance parallel computing technology, deep neural networks have become an every day tool of the data scientist. Learn about how the computer game industry helped develop hardware that drives autonomous cars, revolutionalize cancer reseach and helps determine cosmological parameter.
New generation sequencing technology has made gene sequencing an every day tool for biologists and medical researches. Learn how to process and use gene sequencing data to understand the mechanisms of cancer and help develop new drugs.
Critical behaviour in quantum mechanical systems can cause erratic behaviour of the conductance of certain giant molecules. Molecules balanced on the verge of insulation and conductance can sometimes increase the flow of current by orders of magnitude. Learn about whether this behaviour could have significance in energy production and transport in living cells.
Numerical methods allow solving Schrödinger's equation to complex molecules and periodic quantum systems such as crystal lattices. Learn how to simulate an Archimedes screw build from carbon nanotubes and how certain materials conduct only on their surfaces and how could they be used in the future in spintronic devices.
From computer networks and biochemical reaction chains to social networks, we are surrounded of various artificial and natural systems that can be modelled with graphs. Learn about the various types of networks and their statistical and dyanmical properties.
Social network analysis combines is a quantitative approach in social scienced which uses big data analytics, machine learning techniques and text mining to inverstigate the driving forces of human social behaviour. Learn about how to use data extracted from social media to map the interconnection between human populations, language use, daily activity, etc.
Multi-wavelength statistical analysis of the properties of galaxies reveals the details of galaxy formation and evolution. Understanding galaxy clustering on all scales help resolve the questions of structure formation in the universe. Sky surveys were among the first scientific projects which provided data at such scales that it could be called "big data". Learn about how statistical and machine learning methods can help uncover the secrets of the universe.
Since many cosmic processes happen on time scales of millions of year or more, cosmological simulations are the only methods to study the detailed dynamics of the universe. Simulations follow the effects of gravity and hydrodynamics on billions of particles and yield output data on unusally large scales. Learn about how to use machine learning and data mining methods to automatically extract knowledge from cosmological simulations and how new methods could speed up simulations significantly.
Databases are not only tools for data intensive science but are also subject to active research. The exponentially growing data volumes make scale-out parallelism necessary. Even by running data analysis on clusters of machines, existing algorithms cannot always be scaled up to the problems which requires new approaches. Learn about how to use distributed system to process large amounts of data and how to tweak existing relational databases into parallel data warehouses.
Photometric redshift estimation is a technique to guess the redshift of an extragalactic object (galaxy or quasar) based on its broadband magnitudes, when no spectroscopy is available. Similar techniques can be used to estimate other important physical properties of galaxies such as the start formation rate or total stellar mass.
Until recently, it has been a time-consuming, costly and arduous task to collect and analyze data about individual humans at a large scale. With the advent of the digital era, there is a growing amount of data accessible online that enables the analysis and modeling of human behavior. We aim to understand these digital data sources and the methods that connect the data to real-world outcomes.
To fight the war against terror, Law Enforcement Agencies (LEAs) are increasingly relying on social media intelligence (SOCMINT), a new field of intelligence covering a wide range of applications, techniques and capabilities analysing social media data. These techniques include as Natural Language Processing (NLP), Social Network Analysis (SNA), Artificial Intelligence (AI) and Complex Event Processing (CEP).
Our faculty members, graduate students and former members.
Research: Quantum biology, Network Science
Room: É 5.63
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Research: Cosmology, Genomics, Network Science, Data Mining
Room: É 5.103
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Research: Numerical Quantum Mechanics
Room: É 5.??
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Research: Chaotic systems
Room: É 5.53
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Research: Extragalactic Astronomy, Scientific Databases, Data Mining
Room: É 5.60
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Research: Network Science, Signal and image processing
Room: É 5.??
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Research: Network Science
Room: É 3.90
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Research: Network Science
Room: É 3.90
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Research: Numerical Quantum Mechanics
Room: É 5.??
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Research: Genomics
Room: É 5.57
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Research: Genomics
Room: É 5.57
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Research: Scientific Databases
Room: D 2.507
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Research: Information Systems
Room: D ???
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Research: Machine Learning
Room: É 5.103
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Research: Machine Learning
Room: É 5.??
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Research: Machine Learning
Room: É 5.??
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bio page
Research: Genomics, Machine Learning
Room: É 5.??
E-mail: click here
bio page
Research: Genomics
Room: É 5.57
E-mail: click here
bio page
Research: Machine Learning
Room: É 5.?
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Research: Social Network Science, Text Mining
Research: Cosmological Simulations
Research: Machine Learning
Research: Social Network Science
Research: Network Science
Research: Information Science
Research: Network Science
Research: Scientific Databases
Research: Network Science
Pázmány Péter sétány 1/a., 1117 Budapest, Hungary
