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                    <a class="nav-link js-scroll-trigger" href="#about">O dniu</a>
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                    <a class="nav-link js-scroll-trigger" href="#gdzieikiedy">Gdzie i kiedy</a>
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                <h1 class="text-uppercase">
                    <strong>Data Science Day</strong>
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                <p class="text-faded mb-5">Dni otwarte społeczności Data Science (pol. danologia) oraz specjalności magisterskiej na 
                    Wydziale Informatyki i Zarządzania Politechniki Wrocławskiej</p>
                <a class="btn btn-primary btn-xl js-scroll-trigger" style="white-space: normal;" href="#about">Zostań Sherlockiem Holmesem danych</a>
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                <h2 class="section-heading text-white">Dni otwarte społeczności i specjalności Data Science! Studiuj i pracuj w Data Science!</h2>
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                <p class="text-faded mb-4">
                    Chcesz wykonywać najbardziej pożądany zawód IT XXI wieku? Wydział Informatyki i Zarządzania Politechniki Wrocławskiej już drugi rok prowadzi specjalność Data Science (pol. Danologia). To pierwsze w Polsce studia magisterskie w pełni poświęcone specjalności Data Science. Teraz organizujemy społeczność profesjonalistów z tej dziedzinie! Chcesz wiedzieć co robią studenci i firmy w Data Science - przyjdź!</p>
                <a class="btn btn-light btn-xl js-scroll-trigger" href="#services" style="margin-top: 1em">Co, gdzie, kiedy?</a>
                <a class="btn btn-light btn-xl js-scroll-trigger" style="white-space: normal; margin-top: 1em" href="http://studiujdatascience.pl">O specjalności Data Science</a>
                <a class="btn btn-light btn-xl js-scroll-trigger" style="white-space: normal; margin-top: 1em" href="https://www.facebook.com/events/736314176835931/">Data Science Day na Facebooku</a>
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                <h2 class="section-heading">Wykład Plenarny wygłosi</h2>
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                    <h3>prof. Jesse Read</h3>
                    <p class="mb-0" style="text-align: justify; margin-top:2em; line-height: 1.5;">
                    	Many problems in data science and machine learning involve multiple predictions for each input instance, such in the area of multi-label classification which includes applications as diverse as text categorization, image labeling and segmentation, time series forecasting, localization and tracking, and recommender systems. The family of methods collectively known as 'classifier chains' is one such approach to this type of learning problem.</p>
                    <p class="mb-0" style="text-align: justify; margin-top:2em; line-height: 1.5;">The main idea is to link together off-the-shelf binary classifiers in a chain structure, such that predictions become additional features for other classifiers. This general idea proved flexible and effective and variations on this method have obtained state-of-the-art performance across many datasets and under several multi-label evaluation metrics. This performance has also raised many questions about how the chaining mechanism works, and how it could be improved. This talk explores some of the studies made of chaining, its perspectives in the modern contexts of multi-label classification, as well as investigating its application in related areas such as those involving continuous output variables.
                    </p>
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<section id="services">
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                <h2 class="section-heading">Drzwi otwarte Data Science Day</h2>
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                    <h3 class="mb-3">Zastosowania</h3>
                    <p class="text-muted mb-0">Poznaj przykłady zastosowań systemów i aplikacji opartych na Data Science realizowane w firmach.</p>
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                    <h3 class="mb-3">Nowe przedmioty</h3>
                    <p class="text-muted mb-0">Obejrzyj program studiów magisterskich na specjalności Data Science
                        (pol. Danologia). </p>
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                    <h3 class="mb-3">Praktyka</h3>
                    <p class="text-muted mb-0">Przekonaj się, jak możesz zrealizować własny biznes będąc specjalistą Data Science.</p>
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                    <h3 class="mb-3">Inwestycja</h3>
                    <p class="text-muted mb-0">Poznaj inwestorów w start-upy z Data Science.</p>
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                    <h3 class="mb-3">Nauka</h3>
                    <p class="text-muted mb-0">Posłuchaj na czym polega ścieżka rozwoju naukowego po specjalności Data
                        Science.</p>
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                    <h3 class="mb-3">Ludzie</h3>
                    <p class="text-muted mb-0">Poznaj ludzi z pasją, którzy wprowadzą cię w świat Data Science</p>
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</section>
<section id="gdzieikiedy" class="bg-light">
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                <h2 class="section-heading">Gdzie i kiedy</h2>
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                    <h3 class="mb-3">Gdzie</h3>
                    <p class="text-muted mb-0">Budynek D-21 (Strefa Otwartej Nauki), Politechnika Wrocławska <br/>
                        plac Grunwaldzki 11, Wrocław 50-377<br/>
                        sala 007 (parter), wejście A</p>
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                    <h3 class="mb-3">Kiedy</h3>
                    <p class="text-muted mb-0">14.11.2019<br/>
                        czwartek<br/>
                        godz. 9:00-13:00 w tym lunch
                    </p>
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</section>
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                <h2 class="section-heading">Program</h2>
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                        <th>Godzina</th>
                        <th>Wystąpienie</th>
                    </tr>
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                    <tbody>
                    <tr>
                        <td>09:00</td>
                        <td>Rozpoczęcie, przywitanie gości</td>
                    </tr>
                    <tr>
                        <td>09:05-10:00</td>
                        <td>Keynote Lecture (ANG): Multi-output Learning with Chaining by <a href="https://jmread.github.io/">prof. Jesse Read</a> (<a href="https://www.lix.polytechnique.fr/">Ecole Polytechnique</a>, Paryż)</td>
                    </tr>
                    <tr>
                        <td>10:00-10:30</td>
                        <td>Poczęstunek dla uczestników wydarzenia</td>
                    </tr>
                    <tr>
                        <td>10:30 - 12:30</td>
                        <td>Prezentacje studentów, absolwentów, przedsiębiorców, doktorantów, prowadzących ze specjalności Danologia (PL):</td>
                    </tr>
                    <tr>
                    	<td></td>
                    	<td style="padding-left: 1.5em;">1. Trasee, AI startup - który powstał w trakcie studiów na Danologii, Piotr Zieliński (absolwent danologii, doktorant PWr, przedsiębiorca) i Mateusz Zimoch (absolwent danologii, przedsiębiorca)</td>
                    </tr>
                    <tr>
                        <td></td>
                        <td style="padding-left: 1.5em;">2. Mapowanie emocji z social media do modeli 3D budynków, Piot Sajewicz (obecny student danologii)</td>
                    </tr>
                        <td></td>
                        <td style="padding-left: 1.5em;">3. Projekty naukowe dla biznesu - Modelowanie przepływów finansowych dla Tradeteq, Piotr Bielak (absolwent danologii, doktorant PWr)</td>
                    </tr>
                        <td></td>
                        <td style="padding-left: 1.5em;">4. Katedra Inteligencji Obliczeniowej jako miejsce nauki, badań i rozwoju, prof. dr hab. inż. Halina Kwaśnicka (kierownik Katedry Inteligencji Obliczeniowej)</td>
                    </tr>
                        <td></td>
                        <td style="padding-left: 1.5em;">5. Kim będziesz po Danologii? - dr inż. Tomasz Kajdanowicz (opiekun specjalności Danologia)</td>
                    </tr>
                    </tr>
                        <td></td>
                    <td style="padding-left: 1.5em;">6. Pytania i odpowiedzi? - prof. dr hab. inż. Przemysław Kazienko (kierownik Centrum <a href="http://engine.pwr.wroc.pl/pl/">Engine</a>)</td>
                    </tr>
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                <a class="btn btn-primary btn-xl js-scroll-trigger" style="white-space: normal" href="http://studiujdatascience.pl">Więcej o specjalności Data Science na PWr</a>
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