class: center, middle, inverse, title-slide

# Willkommen
### Explorative Datenanalyse mit R<br> <a href='https://therbootcamp.github.io'>The R Bootcamp</a> <br> <a href='https://therbootcamp.github.io/EDA_2022Mar/'> <i class='fas fa-clock' style='font-size:.9em;'></i> </a>  <a href='https://therbootcamp.github.io'> <i class='fas fa-home' style='font-size:.9em;' ></i> </a>  <a href='mailto:therbootcamp@gmail.com'> <i class='fas fa-envelope' style='font-size: .9em;'></i> </a>  <a href='https://www.linkedin.com/company/basel-r-bootcamp/'> <i class='fab fa-linkedin' style='font-size: .9em;'></i> </a>
### März 2022

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          www.therbootcamp.com
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    <a href="https://therbootcamp.github.io/">
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       Explorative Datenanalyse mit R | März 2022
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<br><br><br><br><br>

# Ziel

>###Das Ziel dieses Kurses ist Euch den kompetenten Umgang mit R für die Aufbereitung, Exploration, und Visualisierung von Daten zu vermitteln.

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<br><br><br><br>
<p align="center"><img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/target.png" height="350"></p>

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# Der Data Science Kreislauf

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.pull-right7[
<br>
<p align = "center">
<img src="https://i.pinimg.com/originals/17/17/87/1717877375f75969a93d9fc1eb79866a.jpg" height="540"><br>
<font style="font-size:10px">from <a href="http://sudeep.co/">http://sudeep.co/</a></font>
</p>
]

<!---

# Data Science Rollen

<br><br>
<p align = "center">
<img src="image/data_science_jobs.png"><br>
<font style="font-size:10px">from <a href="https://news.efinancialcareers.com/sg-en/3001517/data-science-careers-finance">efinancialcareers.com</a></font>
</p>

# Data Science’s missverstandener Held

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<i>"Your analyst is the sprinter; their ability to quickly help you see and summarize what-is-here is a superpower for your process."</i>

<i>"The only roles every business needs are decision-makers and analysts. If you lose your analysts, who will help you figure out which problems are worth solving?"</i>

<a href="https://hbr.org/2018/12/what-great-data-analysts-do-and-why-every-organization-needs-them">Harvard Business Review</a><br><a href="https://towardsdatascience.com/secret-paragraphs-from-hbrs-analytics-ddd2ead761d4">Towards Data Science</a>

<br><br><br><br><br><br>
<b>Cassie Kozyrkov</b><br>
Chief Decision Scientist at Google

]

.pull-right45[

<p align = "center">
<img src="image/cassie.png"><br>
<font style="font-size:10px">from <a href="https://medium.com/@kozyrkov">medium.com</a></font>
</p>

]

# Exploration in Maschinellem Lernen

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<p align = "center">
<img src="image/ml_robot.jpg"><br>
<font style="font-size:10px">from <a href="https://medium.com/@dkwok94/machine-learning-for-my-grandma-ca242e97ef62">medium.com</a></font>
</p>

]

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<i>"…some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used."</i>

<b>Pedro Domingos</b>, Professor for Computer Science, Uni Washington

<br>
<i>"The algorithms we used are very standard for Kagglers. […] We spent most of our efforts in feature engineering."</i>

<b>Xavier Conort</b>, Chief Data Scientist, Data Robot

<br>
<i>"Coming up with features is difficult, time-consuming, requires expert knowledge. "Applied machine learning" is basically feature engineering."</i>

<b>Andrew Ng</b>, Founder and CEO of Landing AI

]

--->

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<br>
# Agenda

<ul>
  <li class="m1"><span><high>Workshop</high></span></li>
  <ul class="level">
    <li><span>1 Block zu Data IO</span></li>
    <li><span>2 Blöcke zu Wrangling</span></li>
    <li><span>3 Blöcke zu Plotting</span></li>
  </ul><br>
  <li class="m2"><span><high>Block</high></span></li>
  <ul class="level">
    <li><span>Folien-basierten Einführung</span></li>
    <li><span>Viele Übungen</span></li>
    <li><span>Interaktiven Zusammenfassung</span></li>
  </ul>
  </span></li>
</ul>

]

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<br><br>
<img src="image/schedule.png" height="525" align="center">

]

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# Einführung

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<br2>

<ul>
  <li class="m1"><span><high>Einführung</high>
  <br><br>
  <ul class="level">
    <li><span>30-45 min</span></li>
    <li><span>Konzepte & Code Beispiele</span></li>
  </ul>
  </span></li>
  <li class="m2"><span><high>Materialien</high>
  <br><br>
  <ul class="level">
    <li><span>Immer <a href="https://therbootcamp.github.io/EDA_2020Feb/">online verfügbar</a></span></li>
  </ul>
  </span></li>
</ul>

]

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<p align="center">
<img src="image/present.jpg" style="width:350px">
<br>
<font style="font-size:10px">from <a href="www.Freepik.com">Freepik.com</a></font>
</p>

]

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# Übungen

<ul>
  <li class="m1"><span><high>Selber Programmieren</high>
  <br><br>
  <ul class="level">
    <li><span>20 - 50 Aufgaben</span></li>
    <li><span>Zu Beginn einfach dann zunehmend schwieriger.</span></li>
    <li><span>Folgt Eurem eigenen Tempo.</span></li>
    <li><span>Antworten kommen später.</span></li>
  </ul>
  </span></li>
</ul>

]

.pull-right5[
<br>

<iframe src="https://therbootcamp.github.io/EDA_2022Mar/_sessions/Data/Data_practical.html" height="480px" width = "500px"></iframe>

Beispiel:<a href="https://therbootcamp.github.io/EDA_2022Mar/_sessions/Data/Data_practical.html"> Daten </a>

]

<!---

# Cheatsheets

<table width="100%" style="cellspacing:0; cellpadding:0; border:none">
  <tr>    
  <td>
  <p align = 'center'>RStudio<br><br>
  <a href="image/rstudio-ide.pdf"><img border="0" alt="W3Schools" src="image/rstudio.png" height="180"></a></p>
  </td>
 
  <td>
   <p align = 'center'>Base R<br><br>
  <a href="image/base-r.pdf"><img border="0" alt="W3Schools" src="image/baser.png" height="180"></a></p>
  </td>  
  
  <td> 
   <p align = 'center'>Data Import<br><br>
  <a href="image/data-import.pdf" download><img border="0" alt="W3Schools" src="image/import.png" height="180"></a></p>
  </td>
  
  <td>
  <p align = 'center'>Data Wrangling<br><br>
  <a href="image/data-transformation.pdf"><img border="0" alt="W3Schools" src="image/wrangling.png" height="180"></a></p>
  </td>

<td>
  <p align = 'center'>Data Visualization<br><br>
  <a href="image/data-visualization-2.1.pdf"><img border="0" alt="W3Schools" src="image/plotting.png" height="180" ></a></p>
  </td>

</tr>
</table>
<br>

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<br><br>
# Pausen

<ul>
  <li class="m1"><span>Mach <high>jederzeit</high> Pausen.</span></li>
  <li class="m2"><span>Bedient euch an der <high>Pausenverpflegung</high>.</span></li>
  <li class="m3"><span>Lunch alleine oder begleitet uns zum <high>Tibits</high>.</span></li>
</ul>

]

.pull-right6[

<p align='center'><br><br><br><br>
<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/tibits.jpg" height="400" vspace="10"><br>
<font style="font-size:10px">from <a href="https://www.tibits.ch/de/restaurants.html#tibits-basel">Tibits.ch</a></font>
</p>

]

# Photos

<p align="center">
<img src="image/camera.png" height="400px">
</p>

--->

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# Vorstellung

.pull-left5[

<ul>
  <li class="m1"><span>Wie heisst Du?</span></li>
  <li class="m2"><span>Was ist dein Beruf?</span></li>
  <li class="m3"><span>Hast du Programmiererfahrung mit R oder anderen Programmiersprachen?</span></li>
  <li class="m4"><span>Welche Probleme möchtest du mit R lösen?</span></li>
</ul>

]

.pull-right45[

<p align="center">
<img src="image/vorstellung.jpg" height="360px">
<br>
<font style="font-size:10px">from <a href="www.artofmanliness.com">artofmanliness.com</a></font>
</p>

]

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class: middle, center

<h1><a href=https://therbootcamp.github.io/EDA_2022Mar/index.html>Agenda</a></h1>

Notes for current slide

Notes for next slide

Willkommen

Explorative Datenanalyse mit R
The R Bootcamp
     

März 2022

1 / 8

Ziel

Das Ziel dieses Kurses ist Euch den kompetenten Umgang mit R für die Aufbereitung, Exploration, und Visualisierung von Daten zu vermitteln.

2 / 8

Der Data Science Kreislauf

from http://sudeep.co/

3 / 8

Agenda

Workshop

1 Block zu Data IO
2 Blöcke zu Wrangling
3 Blöcke zu Plotting

Block

Folien-basierten Einführung
Viele Übungen
Interaktiven Zusammenfassung

4 / 8

Einführung

Einführung
- 30-45 min
- Konzepte & Code Beispiele
Materialien
- Immer online verfügbar

from Freepik.com

5 / 8

Übungen

Selber Programmieren
- 20 - 50 Aufgaben
- Zu Beginn einfach dann zunehmend schwieriger.
- Folgt Eurem eigenen Tempo.
- Antworten kommen später.

Beispiel: Daten

6 / 8

Vorstellung

Wie heisst Du?
Was ist dein Beruf?
Hast du Programmiererfahrung mit R oder anderen Programmiersprachen?
Welche Probleme möchtest du mit R lösen?

from artofmanliness.com

7 / 8

Agenda

8 / 8

Ziel

Das Ziel dieses Kurses ist Euch den kompetenten Umgang mit R für die Aufbereitung, Exploration, und Visualisierung von Daten zu vermitteln.

2 / 8

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