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Image Applied Data Analysis School - call for applications

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Applied Data Analysis School - call for applications

 

We are pleased to annouce the first edition of the Applied Data Analysis School that consists of a series of 8 courses which can be taken individually or as a whole as required. Courses are taught interactively using a blend of theory, follow-along demonstrations and exercises. The courses will be taught using R, RStudio, Jupyter Notebooks and JupyterLab.

The courses are designed for academic researchers, including Master/PhD students, who have a basic knowledge of statistics and econometrics, and who deal with different types of data and projects in their day-to-day work. The course is also recommended to non-academic staff with an interest in data analysis from an econometric perspective. Professionals who are interested to learn different techniques and raise their awareness of possible methodologies that can be used in their current or future projects will greatly benefit from this course. The courses will be taught in English.

The instructors have extensive experience teaching statistics, economics and applied econometrics. Applicants to this course are encouraged to bring their research questions, as they will benefit from the instructors’ wide collaborations with different researchers, across several countries, as well as their involvement supervising graduate students.

detailed info at: https://www.gades-solutions.com/project/data-analysis-school/

pdf Call for papers: Workshop in Machine Learning In Labor, Education, And Health Economics, 19-20 November 2020 Popular

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Machine Learning In Labor, Education, And Health Economics - International workshop

19-20 November 2020, IAB - Germany

The Institute of Employment Research (IAB), the Friedrich-Alexander-Universität Erlangen-Nürnberg
(FAU), and the Labour and Socio-Economic Research Center (LASER) are pleased to announce a
workshop on machine learning in economics. Empirical research in economics typically focuses on
the unbiased estimation of causal effects. In contrast, statistics and computer science place more
value on prediction (especially out-of-sample) and data-driven selection of models and variables.
So far, only few studies apply these methods in empirical economic research, but their importance
is growing. This holds in particular with the increasing availability of big data for economic research.
The two-day workshop seeks to bring together researchers who apply machine learning methods in
the following fields: Labor economics, economics of education and health economics.

For more information download the Call in attachment.