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SUMMARY:[Workshop] Julia for High Performance Data Analysis
DESCRIPTION:Register here\n\n\n\n\n\n\nOverview\n\n\n\nJulia is a modern high-level programming language that is fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab. It thus solves the two-language problem\, i.e. when prototype code in a high-level language needs to be combined with or rewritten in a lower-level language to improve performance. Although Julia is a general-purpose language\, many of its features are particularly useful for numerical scientific computation\, and a wide range of both domain-specific and general libraries are available for statistics\, machine learning\, and numerical modeling. \n\n\n\nJoin us for Julia for High Performance Data Analysis\, a hands-on workshop designed to equip you with practical skills for working with large datasets\, optimizing code\, and leveraging Julia’s rich ecosystem of libraries. You’ll explore real-world applications in data analysis\, numerical computation\, and machine learning\, all while discovering how Julia can streamline your workflow and elevate your performance without sacrificing code readability. \n\n\n\nWho is this workshop for?\n\n\n\nThis workshop is aimed at students\, researchers\, and developers who: \n\n\n\n\nAre already familiar with one or more programming languages such as Julia\, Python\, R\, C/C++\, Fortran\, or Matlab.\n\n\n\nWork with large datasets or need to perform computationally intensive modeling and analysis.\n\n\n\nWant to develop high-performance data science applications while staying within a productive\, high-level programming environment.\n\n\n\n\nPrerequisites\n\n\n\n\nExperience with one or more programming languages.\n\n\n\nFamiliarity with basic concepts in linear algebra and machine learning.\n\n\n\nBasic experience working in a terminal is helpful.\n\n\n\n\nKey takeaways\n\n\n\nThis online workshop will start by briefly covering the basics of Julia’s syntax and features\, and then introduce methods and libraries which are useful for writing high-performance code for modern HPC systems. After attending the workshop\, you will: \n\n\n\n\nBe comfortable with Julia’s syntax\, built-in package manager\, and development tools.\n\n\n\nUnderstand core language features like its type system\, multiple dispatch\, and composability.\n\n\n\nBe able to write your own Julia packages from scratch.\n\n\n\nKnow how to perform various linear algebra analysis on datasets.\n\n\n\nBe productive in analyzing and visualizing large datasets in Julia using dataframes and visualization packages.\n\n\n\nBe familiar with several Julia libraries for visualization and machine learning.\n\n\n\nUnderstand how to analyze large datasets efficiently in Julia using statistical methods.\n\n\n\n\nTentative Agenda\n\n\n\nTime (9:00-12:00) (CET)ContentsMay 26Motivation\, julia syntax\, special Julia features\, developing in Julia\, package ecosystemMay 27Motivation (julia for data analysis)\, data formats and dataframes\, linear algebra\, machine learning (data part)May 28Machine learning\, clustering and classification\, deep learningMay 29Non-linear regression\, scientific machine learning\, conclusions and outlook\n\n\n\nRegulations\n\n\n\nDue to EuroCC2 regulations\, we CAN NOT ACCEPT generic or private email addresses. Please use your official university or company email address for registration. \n\n\n\nThis training is intended for users established in the European Union or a country associated with Horizon 2020. You can read more about the countries associated with Horizon2020 HERE. \n\n\n\nContact\n\n\n\nFor questions regarding this workshop or general questions about ENCCS training events\, please contact training@enccs.se.
URL:https://enccs.se/events/workshop-julia-for-high-performance-data-analysis/
CATEGORIES:ENCCS Event
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