Difference between revisions of "Data Analysis Courses"
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[[File:Data Analysis Courses.jpg|thumb|right|Data Analysis Courses]] | |||
'''Data Analysis Course''' covers the fundamental theoretical concepts needed to answer the practical questions which quickly arise in real data analysis and is a PhD-level course which prepares students to do research in machine learning. Machine learning, or pattern recognition, or computational statistics, or data mining, is a huge field with thousands of methods and mountains of theoretical ideas - in fact statistics, the mathematics of data analysis, is by far the largest area of mathematics. | |||
This course is aimed at advanced PhD students and post-doctoral researchers who are applying or planning to apply high throughput sequencing technologies and bioinformatics methods in their research. The aim of this course is to familiarise the participants with advanced data analysis methodologies and provide hands-on training on the latest analytical approaches. | |||
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Latest revision as of 06:50, 5 February 2013
Data Analysis Course covers the fundamental theoretical concepts needed to answer the practical questions which quickly arise in real data analysis and is a PhD-level course which prepares students to do research in machine learning. Machine learning, or pattern recognition, or computational statistics, or data mining, is a huge field with thousands of methods and mountains of theoretical ideas - in fact statistics, the mathematics of data analysis, is by far the largest area of mathematics.
This course is aimed at advanced PhD students and post-doctoral researchers who are applying or planning to apply high throughput sequencing technologies and bioinformatics methods in their research. The aim of this course is to familiarise the participants with advanced data analysis methodologies and provide hands-on training on the latest analytical approaches.