16-24 August 2019
The Data Science Summer School is organized for the second time in Romania by the Bucharest University of Economic Studies – the International Relations Office, the Faculty of Cybernetics, Statistics and Informatics, and the Faculty of Finance and Banking, together with renowned specialists from the USA, Norway, Spain and Austria.
The goal of the summer school is to familiarize students with relevant state of the art topics in data science. The program will cover fundamentals of data science and focus on the following key data science topics:
- Data Analytics and statistics
- Machine learning
- Text processing
- Data management and visualization
The program will consist of a combination of lecture-style talks introducing various data science paradigms and methods, hands-on sessions, and student projects.
The summer school aims to have a practical orientation, with Python and Jupyter Notebooks being used to exemplify many of the topics covered at the summer school.
At the end of the summer school, the students are expected to have an understanding of key paradigms used in data science and be able to practically apply them in data science projects.
- Familiarity with computer programming and basic knowledge about Python, interest in working with data, enthusiasm, and willingness to learn new things!
- Basic knowledge of linear algebra, probability theory, knowledge representation would be useful, though not strictly necessary.
- English, at least B2 (according to European language levels – Self Assessment Grid)
- In order to ensure a sufficient level of knowledge, the Summer School is aimed at prospective students with at least a Bachelor’s Degree.
|Statistics for data science||Statistical learning|
|Machine Learning (hands-on)||Intro to text processing||Data modeling and databases (incl. hands-on)||Data cleaning and preprocessing (incl. hands-on)||Data visualization (incl. hands-on)|
|Data science with Python (hands-on)|
|Intro to Machine Learning||Deep Learning with Neural Networks|
(intro and hands-on)
Text processing (hands-on)
Use case: Intellectual Property
|Social event||Graph analytics (incl. hands-on)||Individual group/project work|
Projects presentations and final discussions
|18:00-19:00||Individual group/project work and/or free time||Individual group/project work and/or free time|
Dan Nicolae is Professor and Chair of Statistics at University of Chicago where he is also Professor in the Department of Medicine, Section of Genetic Medicine. Originally from Craiova, Dan Nicolae graduated from “Facultatea de Matematica” of University of Bucharest in 1995, and has obtained his PhD in Statistics from University of Chicago in 1999. He has held visiting positions at deCode Genetics in Iceland, University of Oxford and UCLA. His research focus is on developing statistical and computational methods for understanding the human genetic variation and its influence on the risk for complex traits, with an emphasis on asthma related phenotypes. The methodology developed in his group is based on foundations in high-dimensional inference, machine learning and data science. The current focus in his statistical genetics research is centered on data integration and system-level approaches using large datasets that include clinical and environmental data as well as various genetics/genomics data types: DNA variation, gene expression (RNA-seq), methylation and microbiome. Dan Nicolae has advised more than 50 researchers at all levels (Master, PhD and Post-Doctoral) and has published more than 130 articles in scientific journals. He has been member of numerous Advisory and Editorial Boards and has served on panels in United States, Canada, UK and European Union.
Razvan C. Bunescu is a Professor in the School of Electrical Engineering and Computer Science at Ohio University. He received the PhD degree in computer science from the University of Texas at Austin in 2007, with a thesis on machine learning methods for information extraction. His research interests lie in the general area of machine learning, with a focus on applications in computational linguistics, music information retrieval, biomedical informatics, computer architecture, and more recently computational creativity. His work has been consistently funded by grants from the National Science Foundation and the National Institutes of Health.
Carlos A. Iglesias received the telecommunications engineering degree and the PhD degree in telecommunications, both from the Universidad Politécnica de Madrid (UPM), in 1993 and 1998, respectively. He is an associate professor in the Telecommunications Engineering School, UPM, Spain and Head of the Intelligent Systems Research Group, since 2014. He has been a principal investigator on numerous research grants and contracts in the field of advanced social and IoT systems, funded by the regional, national and European bodies. His main research interests include social computing, multiagent systems, information retrieval, sentiment and emotion analysis, linked data, and web engineering. He participates in the Big Data Value Association in the groups of Data Visualization and Finance.
Mihai Lupu is the Coordinator of the Lighthouse project of the Austrian Federal Ministry of Transport, Infrastructure, and Technology, Data Market Austria, as well as Scientific Coordinator of the recently granted H2020 Safe-DEED Research and Innovation Action. Mihai Lupu has over 10 years of experience in Search Technologies, Artificial Intelligence and Machine Learning, with over 100 publications in these fields. He is also Associate Editor of World Patent Information Journal and of the MDPI Information Journal.
Dumitru Roman works as a Senior Research Scientist at SINTEF (Norway) – the largest independent research organization in Scandinavia. He has wide experience with initiating, leading, and carrying out data-driven and research-intensive projects, participating in dozens of large international projects during the past 14 years in which he has collaborated with large numbers of private companies, public sector organizations, universities, and research institutes. He is currently active in the data management field, focusing on innovation projects enabling data-driven business products and services. He holds an adjunct associate professorship at the University of Oslo, Norway.