Data Science

Machine learning or deep learning are tools that are increasingly used in scientific research and are a perfect complement to traditional econometric models. Machine learning algorithms allow researchers to build predictive models and identify relationships between socio-economic phenomena without imposing a specific shape of the relationship in advance. Thanks to this, they often allow to explain the modeled phenomenon with greater accuracy. To better understand the modeling results, so-called explainable artificial intelligence (XAI) tools are used at the global (model) and local (single observations) levels.

Huge and constantly growing resources of information are available in the form of data that cannot be used directly in econometric modeling. This is data in the form of text, images, video or audio recordings. Their processing and use in modeling requires specialized analytical tools. In turn, processing and integrating various types of data for modeling purposes requires advanced programming skills.

Data Science is a concept that refers to the combination of statistical, analytical and programming competencies. A data scientist is a person who is not only able to process traditional tabular data and perform statistical analyzes on them, but is also able to analyze and process huge amounts of data of various types, integrate them for modeling purposes, apply various machine learning algorithms and make inferences based on them.

The Data Science Department brings together researchers and educators with this type of competences.

Department goals:

• integration of researchers from the Faculty who have Data Science competences and use them in scientific research on microeconomic, macroeconomic, spatial, financial and other issues
• using various types of data in research: tabular, text, images, video, audio, etc.
• using machine learning tools in research, both supervised, unsupervised and reinforced
• using deep learning tools in research, including specialized neural networks for analyzing text, images, video and audio recordings, etc.
• using spatial machine learning tools in research
• using explainable artificial intelligence tools in research
• development of the tools mentioned above
• didactics related to the topics described above
   

Head of the Department

Doctor habilitatus Wójcik Piotr

Data Science Lab
Department of Data Science

Associate Professor

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The staff

Doctor habilitatus Kopczewska Katarzyna

Department of Data Science
Research Group Spatial Warsaw

Associate Professor

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Doctor habilitatus Kopczewski Tomasz

Laboratory of Experimental Economics
Department of Data Science

Associate Professor

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Ph.D. Chlebus Marcin

Department of Data Science

Assistant Professor

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Ph.D. Kuligowska Karolina

Department of Data Science

Assistant Professor

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M.A. Świtała Maciej

Department of Data Science

Specialist

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PhD Candidate

M.A. Buczyński Mateusz

Department of Data Science

PhD Student

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M.A. Grądzki Przemysław

Department of Data Science

PhD Student

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M.A. Kubara Maria

Department of Data Science
Research Group Spatial Warsaw

Assistant
PhD Student

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M.A. Künstler Michał

Department of Data Science

PhD Student

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M.A. Li Zhen

Department of Data Science

PhD Student

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M.A. Lisicki Jan

Department of Data Science

PhD Student

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M.A. Usman Muhammad

Department of Data Science
Research Group Spatial Warsaw

PhD Student

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