{"id":1494,"date":"2022-11-16T18:58:44","date_gmt":"2022-11-16T18:58:44","guid":{"rendered":"https:\/\/pydata.org\/global2022\/?page_id=1494"},"modified":"2022-11-28T14:51:34","modified_gmt":"2022-11-28T14:51:34","slug":"expert-briefings","status":"publish","type":"page","link":"https:\/\/pydata.org\/global2022\/expert-briefings\/","title":{"rendered":"Expert Briefings"},"content":{"rendered":"<p>[vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_empty_space height=&#8221;200px&#8221;]<div class=\"no-padding-rl no-padding-top padding-8\">\n                <div class=\"text-center\"><h3 class=\"font-family-alt font-weight-700 letter-spacing-2 text-uppercase xs-title-small title-medium title-sideline-base-color\">Expert Briefings | November 21-29th<\/h3><\/div>\n            <\/div>[vc_column_text]Before the main PyData Global conference, the committee runs an Expert Briefings series- these are presented by individuals who are key contributors to the PyData community and have extensive experience in their respective domains. It is in the format of a short presentation (15 mins) on the state of the art in their area of expertise, and a discussion session afterward. Session info below:[\/vc_column_text][\/vc_column][\/vc_row][vc_row opacity_bg_pattern=&#8221;0.1&#8243;][vc_column width=&#8221;1\/3&#8243; css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_column_text]<\/p>\n<h2>Huda Nassar<\/h2>\n<p>Monday 28 November 3pm UTC<\/p>\n<h3><a href=\"https:\/\/numfocus-org.zoom.us\/j\/81576011447?pwd=UGpQZWFYR0N2dlRrMUIreDVTa2Fhdz09\"><span style=\"text-decoration: underline;\"><strong>Join Here<\/strong><\/span><\/a><\/h3>\n<p>[\/vc_column_text][\/vc_column][vc_column width=&#8221;2\/3&#8243;][vc_column_text]<\/p>\n<h3><span style=\"text-decoration: underline;\"><strong>Understanding problems in a highly connected world via graph analytics<\/strong><\/span><\/h3>\n<p>This talk will cover the following topics: (1) graphs that appear in everyday life and why approaching them with the graph analytics toolbox is important, (2) recent trendy ideas in the graph analytics research field, and (3) tools and software often used to address these problems.[\/vc_column_text][\/vc_column][\/vc_row][vc_row opacity_bg_pattern=&#8221;0.1&#8243;][vc_column width=&#8221;1\/3&#8243; css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_column_text]<\/p>\n<h2>Thomas Wiecki<\/h2>\n<p>Tuesday 29 November 2pm UTC<\/p>\n<h3 style=\"text-align: left;\"><a href=\"https:\/\/numfocus-org.zoom.us\/j\/87876345331?pwd=enVBTmlSQmtSUzFVNHN2b3VmMnAzUT09\"><span style=\"text-decoration: underline;\"><strong>Join Here<\/strong><\/span><\/a><\/h3>\n<p>[\/vc_column_text][\/vc_column][vc_column width=&#8221;2\/3&#8243;][vc_column_text el_id=&#8221;NaomiCedar&#8221;]<\/p>\n<h3><span style=\"text-decoration: underline;\"><strong>The State of the Art for Probabilistic Programming<\/strong><\/span><\/h3>\n<div>Probabilistic Programming as a field is moving at breakneck speed, with innovations being driven on all levels: language, algorithms, compilers, computation, hardware. In this expert briefing I will give a brief overview of where the field is today and where it is headed. One big trend is what I call The Great Decoupling: rather than monolithic PPL systems, we are seeing how various layers of abstraction are introduced and separated. This allows more interoperability, as well as innovation to occur at every level of the stack. Finally, I will talk about a convergence of Bayesian modeling and Causal Inference to a new paradigm called Bayesian Causal Inference.<\/div>\n<div class=\"adL\"><\/div>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_separator][\/vc_column][\/vc_row][vc_row opacity_bg_pattern=&#8221;0.1&#8243;][vc_column width=&#8221;1\/3&#8243; css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_column_text]<\/p>\n<h2>Ian Ozsvald<\/h2>\n<p>Wednesday 23 November 4pm UTC<\/p>\n<h3><a href=\"https:\/\/numfocus-org.zoom.us\/j\/89714978936?pwd=RjUvN2I0UldDRXIzNVljU05SdXNSdz09\"><span style=\"text-decoration: underline;\"><strong>Join Here<\/strong><\/span><\/a><\/h3>\n<p>[\/vc_column_text][\/vc_column][vc_column width=&#8221;2\/3&#8243;][vc_column_text]<\/p>\n<h3><span style=\"text-decoration: underline;\"><strong>The State of Higher Performance Python<\/strong><\/span><\/h3>\n<p>We\u2019ll review the state of the art in the data science world for common number-crunching tasks on small to big data. Topics we\u2019ll cover include profiling, compilation and data manipulation. We\u2019ll also review the near future for Python, Numba, Pandas, Dask and Polars and I\u2019ll help you make some pragmatic choices about tools you might invest time in. We\u2019ll have plenty of time to discuss your use cases and problems you might have encountered.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_separator][\/vc_column][\/vc_row][vc_row opacity_bg_pattern=&#8221;0.1&#8243;][vc_column width=&#8221;1\/3&#8243; css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_column_text]<\/p>\n<h2>Sole Galli<\/h2>\n<p>Thursday 24 November 3pm UTC<\/p>\n<h3><a href=\"https:\/\/numfocus-org.zoom.us\/j\/87584963905?pwd=TkMvODExeURxYkRuWUh5YkFXcUVJUT09\"><span style=\"text-decoration: underline;\"><strong>Join Here<\/strong><\/span><\/a><\/h3>\n<p>[\/vc_column_text][\/vc_column][vc_column width=&#8221;2\/3&#8243;][vc_column_text]<\/p>\n<h3><span style=\"text-decoration: underline;\"><strong>Feature engineering for machine learning with open-source<br \/>\n<\/strong><\/span><\/h3>\n<p>Feature engineering is the process of transforming variables, and extracting and creating new features from data, to train machine learning models. Data in its original format is almost never used to train machine learning models right away. Instead, data scientists devote a huge part of their time to data pre-processing.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_separator][\/vc_column][\/vc_row][vc_row opacity_bg_pattern=&#8221;0.1&#8243;][vc_column width=&#8221;1\/3&#8243; css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_column_text]<\/p>\n<h2>Marco Bonzanini<\/h2>\n<p>Thursday 24 November 5pm UTC<\/p>\n<h3><a href=\"https:\/\/numfocus-org.zoom.us\/j\/85023146206?pwd=SXdIc21JOGp2SDBmQldVYWhJRUNSZz09\"><span style=\"text-decoration: underline;\"><strong>Join Here<\/strong><\/span><\/a><\/h3>\n<p>[\/vc_column_text][\/vc_column][vc_column width=&#8221;2\/3&#8243;][vc_column_text]<\/p>\n<h3><span style=\"text-decoration: underline;\"><strong>Natural Language Processing: Trends, Challenges and Opportunities<\/strong><\/span><\/h3>\n<p>A short presentation on the state of the art in Natural Language Processing, followed by a Q&amp;A \/ round table discussion where you&#8217;ll have the opportunity to ask your burning questions on NLP.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_separator][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>[vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_empty_space height=&#8221;200px&#8221;][vc_column_text]Before the main PyData Global conference, the committee runs an Expert Briefings series- these are presented by individuals who are key contributors to the PyData community and have extensive experience in their respective domains. It is in the format of a short presentation (15 mins) on the state of [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":939,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-1494","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Expert Briefings | PyData Global 2022<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/pydata.org\/global2022\/expert-briefings\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Expert Briefings | PyData Global 2022\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_empty_space height=&#8221;200px&#8221;][vc_column_text]Before the main PyData Global conference, the committee runs an Expert Briefings series- these are presented by individuals who are key contributors to the PyData community and have extensive experience in their respective domains. It is in the format of a short presentation (15 mins) on the state of [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/pydata.org\/global2022\/expert-briefings\/\" \/>\n<meta property=\"og:site_name\" content=\"PyData Global 2022\" \/>\n<meta property=\"article:modified_time\" content=\"2022-11-28T14:51:34+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2021\/06\/cropped-logo.png\" \/>\n\t<meta property=\"og:image:width\" content=\"512\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2021\/06\/cropped-logo.png\" \/>\n<meta name=\"twitter:site\" content=\"@PyDataGlobal\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/\",\"url\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/\",\"name\":\"Expert Briefings | PyData Global 2022\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/wp-content\\\/uploads\\\/2021\\\/06\\\/logo.png\",\"datePublished\":\"2022-11-16T18:58:44+00:00\",\"dateModified\":\"2022-11-28T14:51:34+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/#primaryimage\",\"url\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/wp-content\\\/uploads\\\/2021\\\/06\\\/logo.png\",\"contentUrl\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/wp-content\\\/uploads\\\/2021\\\/06\\\/logo.png\",\"width\":1064,\"height\":213},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/expert-briefings\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Expert Briefings\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/#website\",\"url\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/\",\"name\":\"PyData Global 2022\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/#organization\",\"name\":\"PyData Global 2022\",\"url\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/wp-content\\\/uploads\\\/2022\\\/11\\\/PyDataGlobal-OpenGraph.png\",\"contentUrl\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/wp-content\\\/uploads\\\/2022\\\/11\\\/PyDataGlobal-OpenGraph.png\",\"width\":1200,\"height\":630,\"caption\":\"PyData Global 2022\"},\"image\":{\"@id\":\"https:\\\/\\\/pydata.org\\\/global2022\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/x.com\\\/PyDataGlobal\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/pydata-global\\\/\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Expert Briefings | PyData Global 2022","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/pydata.org\/global2022\/expert-briefings\/","og_locale":"en_US","og_type":"article","og_title":"Expert Briefings | PyData Global 2022","og_description":"[vc_row][vc_column css=&#8221;.vc_custom_1617310084578{margin-right: 0px !important;margin-left: 0px !important;}&#8221;][vc_empty_space height=&#8221;200px&#8221;][vc_column_text]Before the main PyData Global conference, the committee runs an Expert Briefings series- these are presented by individuals who are key contributors to the PyData community and have extensive experience in their respective domains. It is in the format of a short presentation (15 mins) on the state of [&hellip;]","og_url":"https:\/\/pydata.org\/global2022\/expert-briefings\/","og_site_name":"PyData Global 2022","article_modified_time":"2022-11-28T14:51:34+00:00","og_image":[{"width":512,"height":512,"url":"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2021\/06\/cropped-logo.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_image":"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2021\/06\/cropped-logo.png","twitter_site":"@PyDataGlobal","twitter_misc":{"Est. reading time":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/pydata.org\/global2022\/expert-briefings\/","url":"https:\/\/pydata.org\/global2022\/expert-briefings\/","name":"Expert Briefings | PyData Global 2022","isPartOf":{"@id":"https:\/\/pydata.org\/global2022\/#website"},"primaryImageOfPage":{"@id":"https:\/\/pydata.org\/global2022\/expert-briefings\/#primaryimage"},"image":{"@id":"https:\/\/pydata.org\/global2022\/expert-briefings\/#primaryimage"},"thumbnailUrl":"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2021\/06\/logo.png","datePublished":"2022-11-16T18:58:44+00:00","dateModified":"2022-11-28T14:51:34+00:00","breadcrumb":{"@id":"https:\/\/pydata.org\/global2022\/expert-briefings\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/pydata.org\/global2022\/expert-briefings\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/pydata.org\/global2022\/expert-briefings\/#primaryimage","url":"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2021\/06\/logo.png","contentUrl":"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2021\/06\/logo.png","width":1064,"height":213},{"@type":"BreadcrumbList","@id":"https:\/\/pydata.org\/global2022\/expert-briefings\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/pydata.org\/global2022\/"},{"@type":"ListItem","position":2,"name":"Expert Briefings"}]},{"@type":"WebSite","@id":"https:\/\/pydata.org\/global2022\/#website","url":"https:\/\/pydata.org\/global2022\/","name":"PyData Global 2022","description":"","publisher":{"@id":"https:\/\/pydata.org\/global2022\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/pydata.org\/global2022\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/pydata.org\/global2022\/#organization","name":"PyData Global 2022","url":"https:\/\/pydata.org\/global2022\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/pydata.org\/global2022\/#\/schema\/logo\/image\/","url":"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2022\/11\/PyDataGlobal-OpenGraph.png","contentUrl":"https:\/\/pydata.org\/global2022\/wp-content\/uploads\/2022\/11\/PyDataGlobal-OpenGraph.png","width":1200,"height":630,"caption":"PyData Global 2022"},"image":{"@id":"https:\/\/pydata.org\/global2022\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/PyDataGlobal","https:\/\/www.linkedin.com\/company\/pydata-global\/"]}]}},"_links":{"self":[{"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/pages\/1494","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/comments?post=1494"}],"version-history":[{"count":0,"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/pages\/1494\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/media\/939"}],"wp:attachment":[{"href":"https:\/\/pydata.org\/global2022\/wp-json\/wp\/v2\/media?parent=1494"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}