{"650556":{"#nid":"650556","#data":{"type":"event","title":"Materials Informatics 101 Virtual Course","body":[{"value":"\u003Ch3\u003EMaterials Informatics 101 Virtual Course\u003C\/h3\u003E\r\n\r\n\u003Ch5\u003EFriday, October 8, 2021 | 9:00 AM EST - Monday, October 11, 2021 | 4:00 PM EST\u003Cbr \/\u003E\r\nLocation: Virtual Via Georgia Tech Zoom | Cost: $10 Administrative Fee\u003C\/h5\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECourse Description:\u003C\/strong\u003E Informatics is the use of computational tools (e.g. AI, machine learning) to access and make sense of data. Materials Informatics has the potential to accelerate scientific inquiry and support novel insights. However, informatics tools are typically marketed to the biological sciences, financial sector, and \u0026ldquo;Big Tech\u0026rdquo;---it is not immediately clear how to use \u0026ldquo;cat-detector technology\u0026rdquo; to support serious materials science! This hands-on workshop will help material scientists use programming-based informatics tools to create reproducible data workflows, automate tedious data extraction, visualize data for exploration, and apply AI\/ML models for predictive capabilities.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETopics Covered: \u003C\/strong\u003E(Coding) Python \u0026amp; Jupyter notebooks; (Data extraction) Tabula, WebPlotDigitizer; (Data handling) Pandas \/ Grama; (Visualization) Plotnine \/ Plotly; (Modeling) Over\/under-fitting, cross-validation, domain of applicability, active learning.\u003C\/p\u003E\r\n\r\n\u003Ch4\u003E\u003Cstrong\u003ERegister at: \u003C\/strong\u003E\u003Ca href=\u0022https:\/\/tinyurl.com\/DataMats101\u0022\u003Ehttps:\/\/tinyurl.com\/DataMats101\u003C\/a\u003E\u003C\/h4\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"This hands-on workshop will help material scientists use programming-based informatics tools to create reproducible data workflows, automate tedious data extraction, visualize data for exploration, and apply AI\/ML models for predictive capabilities."}],"uid":"27863","created_gmt":"2021-09-08 17:16:54","changed_gmt":"2021-09-08 17:16:54","author":"Christa Ernst","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-10-08T10:00:00-04:00","event_time_end":"2021-10-11T17:00:00-04:00","event_time_end_last":"2021-10-11T17:00:00-04:00","gmt_time_start":"2021-10-08 14:00:00","gmt_time_end":"2021-10-11 21:00:00","gmt_time_end_last":"2021-10-11 21:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1278","name":"College of Sciences"},{"id":"217141","name":"Georgia Tech Materials Institute"},{"id":"545781","name":"Institute for Data Engineering and Science"},{"id":"197261","name":"Institute for Electronics and Nanotechnology"},{"id":"1271","name":"NanoTECH"}],"categories":[],"keywords":[{"id":"186870","name":"go-imat"},{"id":"187023","name":"go-data"},{"id":"12377","name":"Materials Engineering"},{"id":"181980","name":"data informatics"},{"id":"38921","name":"data visualization"},{"id":"188809","name":"data modeling"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"26411","name":"Training\/Workshop"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Cstrong\u003ECecelia Jones\u003C\/strong\u003E\u003Cbr \/\u003E\r\nGeorgia Institute of Technology\u003Cbr \/\u003E\r\n404.894.7769\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}