Corona aus der Data-Science Perspektive

<article class="post-detail"> <header> <h2 class="post-title"> <a href="https://python-podcast.de/show/corona/">Corona aus der Data-Science Perspektive</a> (click here to comment) </h2> <!-- link is on one line to avoid underlined whitespace --> <div class="post-card-meta"> <a href="https://python-podcast.de/show/corona/"><time datetime="2020-03-30T00:00:00+02:00">30. März 2020</time>,</a> <span class="author">Jochen</span> </div> </header> <div class="post-body"> <section class="block-overview"> <section class="block-paragraph"> Diesmal unterhalten uns mit&nbsp;<a href="http://twitter.com/twiecki">Thomas Wiecki</a>&nbsp;&uuml;ber&nbsp;<a href="https://en.wikipedia.org/wiki/Quantitative_analysis_(finance)">Quantitative Finance</a>,&nbsp;<a href="https://en.wikipedia.org/wiki/Probabilistic_programming">Probabilistic Programming</a>&nbsp;und die Corona-Pandemie. Thomas hat &uuml;brigens einen eigenen Podcast namens&nbsp;<a href="http://www.pydata-podcast.com/">PyData Deep Dive</a>&nbsp;den wir w&auml;rmstens empfehlen&nbsp; k&ouml;nnen. So ab Minute 36 wird das mit den Audio-Knacksern &uuml;brigens auch besser :).<br /> <br /> </section> <section class="block-audio"> </section> <section class="block-paragraph"> <br /> <br /> </section> </section> <section class="block-detail"> <section class="block-paragraph"> <h2>Shownotes</h2> <p>Unsere E-Mail f&uuml;r Fragen, Anregungen &amp; Kommentare: <a href="mailto:[email protected]">[email protected]</a></p> <h3>Quantitative Finance</h3> <ul> <li><a href="https://www.quantopian.com/">Quantopian</a></li> <li><a href="https://en.wikipedia.org/wiki/Backtesting">Backtesting</a></li> <li><a href="https://github.com/quantopian">Quantopian auf github</a>&nbsp;<a href="https://github.com/quantopian/zipline">zipline (backtesting library)</a></li> <li><a href="https://en.wikipedia.org/wiki/Linear_regression">Linear Regression</a></li> <li><a href="https://www.statsmodels.org/stable/index.html">statsmodels</a>&nbsp;<a href="https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average">ARIMA</a></li> </ul> <h3>Probabilistic Programming</h3> <ul> <li><a href="https://docs.pymc.io/">pymc</a></li> <li><a href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo">Markov chain Monte Carlo</a></li> <li><a href="https://twiecki.io/blog/2014/03/17/bayesian-glms-3/">The Best Of Both Worlds: Hierarchical Linear Regression in PyMC3</a></li> <li><a href="https://en.wikipedia.org/wiki/Bayesian_statistics">Bayesian Statistics</a></li> </ul> <h3>COVID-19</h3> <ul> <li>Thomas&nbsp;<a href="https://github.com/twiecki/covid19">covid-19 repository</a></li> <li><a href="https://covid19dashboards.com/">Some covid19 dashboards</a></li> <li><a href="https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology">Compartmental models in epidemiology</a></li> <li><a href="https://en.wikipedia.org/wiki/Student%27s_t-distribution">Student&#39;s t-distribution</a></li> <li>Using epidemiological models is like counting spoons&nbsp;&nbsp;<a href="https://twitter.com/nntaleb/status/1242443727366479874">tweet</a>&nbsp;by&nbsp;<a href="https://twitter.com/nntaleb">@nntaleb</a></li> <li>&quot;Thousands of lines of undocumented c code&quot;&nbsp;<a href="https://twitter.com/neil_ferguson/status/1241835454707699713">tweet</a>&nbsp;by&nbsp;<a href="https://twitter.com/neil_ferguson">@neil_ferguson</a></li> <li><a href="https://coronavirus.jhu.edu/map.html">Johns Hopkins</a>&nbsp;Daten aus WHO Pdfs</li> <li><a href="https://www.ecdc.europa.eu/en/novel-coronavirus-china">European Centre for Disease Prevention and Control</a></li> <li><a href="https://ourworldindata.org/coronavirus">Our world in data (coronavirus)</a></li> <li><a href="https://wirvsvirushackathon.org/">Hackathon Coronavirus</a></li> <li><a href="https://www.kaggle.com/c/covid19-global-forecasting-week-1/overview/open-scientific-questions?utm_medium=em&amp;utm_source=intercom&amp;utm_campaign=CORD19-forecasting-email">COVID19 Global Forecasting</a>&nbsp;Kaggle-Competition</li> <li><a href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge">COVID-19 Open Research Dataset Challenge</a>&nbsp;Kaggle-Competition</li> <li><a href="https://github.com/cwoomi/cert-covid19">CCC Cert Informationssammlung</a></li> <li><a href="https://bulletin.cert.ccc.de/">CERT Bulletin</a></li> </ul> <h3>Datasette</h3> <ul> <li><a href="https://github.com/simonw/datasette">Datasette</a></li> <li><a href="https://covid-19.datasettes.com/covid/daily_reports?country_or_region=Italy&amp;_sort_desc=confirmed#g.mark=bar&amp;g.x_column=day&amp;g.x_type=ordinal&amp;g.y_column=confirmed&amp;g.y_type=quantitative">Query f&uuml;r Italien auf der covid-19 datasette</a></li> <li><a href="https://glitch.com/">Glitch</a></li> </ul> <h3><a href="https://konektom.org/tags/68464/" style="font-size: 13px;">&Ouml;ffentliches Tag auf konektom</a></h3> </section> </section> </div> </article>