![]() ![]() I have recently become a certified trainer in Data and Software Carpentry organizations that aim to teach fundamental Data Science skills needed to conduct research. Active participant of Kaggle competitions, one of which could be used as a project work.įields of interests: Data Mining, Machine Learning, Bioinformatics, Image Recognition, Deep Learning, Advanced Algorithms.īioinformatics researcher at the University of TartuĬurrently, I work as a bioinformatics researcher at Quretec Ltd., and as a junior bioinformatics researcher at the Institute of Computer Science, University of Tartu, Estonia. Currently, member of UPEER organisation that aims to contribute to development of local scientific societies. Has been a program committee member in the Summer School AACIMP. Recently became a certified trainer in Data and Software Carpentry organisations that aim to skills Data Science to scientists from different areas. Now Dmytro is a PhD student at the University of Tartu, working in the field of bioinformatics.ĭmytro has experience teaching Data Mining, Machine Learning, Bioinformatics, Advanced and Text Algorithms courses in the University of Tartu to post-graduate students. Received Bachelor’s degree from the National University of Ukraine (KPI), and Master’s degree from the University of Tartu (Estonia). LecturersĪ PhD student in the field of Bioinformatics at the University of TartuĪffiliation: University of Tartu/Quretec Ltd. Lecture III: Clustering, visualization and web tools (online practice)īasic statistics and math, no previous experience with R is required.Lecture II: prediction of phenotype with different ML algorithms.Lecture I: data pre-processing, feature selection techniques.Moreover, the course will be based on real-life data sets to give participants an idea of problems that they might face when carrying out analysis of their own data. R has a very handful user interface – RStudio. R is one of the most popular programming languages used in data science for data exploration, visualization and statistical analysis. Additionally, we will introduce publicly available web tools applied for analysis and visualization.Īs we aim to provide hands-on practical skills, all lectures will be followed by the practice sessions in R. In this course will start with an introduction to data pre-processing techniques in Bioinformatics, then we will learn how to apply various Machine Learning and Data Mining methods to analyze different types of biological data and predict biological signal of interest. ![]() Course topicsīioinformatics – is an interdisciplinary field, which aims to analyze and interpret biological data using data science methods. We are going to look into basic methods, that are extensively used in the field of Bioinformatics and Biostatistics using real-life examples. Introduction to Bioinformatics 2016 Course Description ![]()
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