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<!DOCTYPE html>
<html lang="en">
<head>
<title>Week 2 What, abstracting the data. MIDS W209 Information Visualization Slides</title>
<meta charset="utf-8">
<meta name="author" content="John Alexis Guerra Gómez">
<meta name="description" content="Week 2 What, abstracting the data. MIDS W209 Information Visualization Slides">
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<link href="../css/reveal.css" rel="stylesheet">
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<link href="../css/style.css" rel="stylesheet">
</head>
<body>
<div class="reveal">
<div class="slides">
<section>
<h1 class="title">What: abstracting the data<br><small>MIDS W209: Information Visualization</small></h1>
<div class="r-stretch"></div>
<div class="tiny"><a href="https://johnguerra.co/" target="_blank"><strong> John Alexis Guerra Gómez</strong></a><span> | john.guerra[at]gmail.com</span><a href="https://twitter.com/duto_guerra"> | @duto_guerra</a><br><a href="https://andyreagan.com/" target="_blank"><strong> Andy Reagan</strong></a><span> | andy[at]andyreagan.com</span><a href="https://twitter.com/andyreagan"> | @andyreagan</a><br><a href="https://johnguerra.co/lectures/MIDS_W209_Information_Visualization/02_What/" target="_blank">https://johnguerra.co/lectures/MIDS_W209_Information_Visualization/02_What/</a></div>
<div class="logo"><a href="https://datascience.berkeley.edu/"><img class="logo" src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></a></div>
<div class="tiny">Partially based on<a href="https://www.cs.ubc.ca/~tmm/talks.html#minicourse14"> slides from Tamara Munzner</a></div>
</section>
<section id="outline">
<section class="full">
<h2>What we are going to learn</h2>
<ul class="small">
<li class="fragment">Abstracting the data</li>
<li class="fragment">Types of datasets</li>
<li class="fragment">Types of attributes</li>
<li class="fragment">Sequential, diverging, cyclical</li>
<li class="fragment">Data aggregation (temporal and geo)</li>
<li class="fragment">Deriving new data</li>
</ul>
</section>
<section>
<div><img src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></div>
</section>
</section>
<section id="abstractingData">
<section>
<h1>Abstracting the data</h1>
</section>
<section data-background-size="contain" data-background="../shared_images/what_sheet.png" alt="What: data abstraction summary sheet" id="what"></section>
<section>
<h2>Why abstract the data?</h2>
<ul>
<li>Different attribute types 👉 different representations</li>
<li class="fragment">Different dataset types 👉 different idioms available</li>
</ul>
</section>
<section>
<h2>What do you need to abstract?</h2>
<div class="flex">
<div class="half"><span>
<svg width="100%" height="100%" viewBox="0 0 2146 1498" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<!-- Generator: Sketch 62 (91390) - https://sketch.com -->
<title>What_highlighted</title>
<desc>Created with Sketch.</desc>
<image href="../shared_images/what_sheet.png" width="100%" height="100%"/>
<g class="What_highlighted" stroke="none" stroke-width="1" fill="none" fill-rule="evenodd">
<path d="M622,644 L1663,644 L1663,1204 L1106.86647,1204 L1106.86647,1481 L619.745819,1481 L622,644 Z" class="fragment" stroke="#F5A623" stroke-width="10" stroke-dasharray="8,10" data-fragment-index="1"></path>
<path d="M1682,137 L2086,137 L2086,644 L1682,644 L1682,137 Z" class="fragment" stroke="#F5A623" stroke-width="10" stroke-dasharray="8,10" data-fragment-index="2"></path>
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</g>
</svg>
</span></div>
<div class="half">
<ul class="tiny">
<li class="fragment" data-fragment-index="1"><strong>Dataset type</strong>: (e.g., table, network, temporal ...)</li>
<li class="fragment" data-fragment-index="2"><strong>Attribute types</strong>: (e.g. categorical, ordinal, quantitative)</li>
<li class="fragment" data-fragment-index="3"><strong>Ordering direction</strong>: (e.g. sequential, diverging, cyclical)</li>
<li class="fragment" data-fragment-index="4"><strong>Data availability</strong>: (e.g. dynamic, static)</li>
</ul>
</div>
</div>
</section>
<section>
<div><img src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></div>
</section>
</section>
<section id="datasets">
<section>
<h1>Types of Datasets</h1>
</section>
<section><img src="../shared_images/vad_book_alldiagrams/dataset_types.png" alt="Dataset Types: tables, network/trees fields geometry">
<div><strong class="fragment">+ Temporal!</strong></div>
</section>
<section>
<h2>Tables</h2>
<div class="flex small">
<div class="half">
<ul>
<li>The vast majority of datasets</li>
<li class="fragment">Attributes (columns) + items (rows)</li>
<li class="fragment">Other dataset types are usually stored as a combination of tables</li>
</ul>
</div>
<div class="half tiny vcentered"><span class="tiny"><table>
<thead>
<tr>
<th>price</th>
<th>brand</th>
<th>model</th>
<th>title_status</th>
<th>mileage</th>
<th>color</th>
<th>lot</th>
</tr>
</thead>
<tbody>
<tr>
<td>21500</td>
<td>ford</td>
<td>f-150</td>
<td>clean vehicle</td>
<td>76876</td>
<td>gray</td>
<td>167783132</td>
</tr>
<tr>
<td>7500</td>
<td>nissan</td>
<td>door</td>
<td>clean vehicle</td>
<td>58126</td>
<td>black</td>
<td>167598085</td>
</tr>
<tr>
<td>10700</td>
<td>chevrolet</td>
<td>trax</td>
<td>clean vehicle</td>
<td>44280</td>
<td>red</td>
<td>167792666</td>
</tr>
<tr>
<td>23100</td>
<td>dodge</td>
<td>charger</td>
<td>clean vehicle</td>
<td>12265</td>
<td>silver</td>
<td>167735423</td>
</tr>
<tr>
<td>33800</td>
<td>dodge</td>
<td>durango</td>
<td>clean vehicle</td>
<td>15003</td>
<td>black</td>
<td>167734879</td>
</tr>
<tr>
<td>4140</td>
<td>ford</td>
<td>door</td>
<td>clean vehicle</td>
<td>40747</td>
<td>white</td>
<td>167656519</td>
</tr>
<tr>
<td>13900</td>
<td>nissan</td>
<td>rogue</td>
<td>clean vehicle</td>
<td>38760</td>
<td>white</td>
<td>167762102</td>
</tr>
<tr>
<td>15700</td>
<td>ford</td>
<td>cutaway</td>
<td>clean vehicle</td>
<td>75862</td>
<td>white</td>
<td>167780452</td>
</tr>
<tr>
<td>1150</td>
<td>ford</td>
<td>door</td>
<td>salvage insurance</td>
<td>123349</td>
<td>red</td>
<td>167652717</td>
</tr>
<tr>
<td>26100</td>
<td>ford</td>
<td>f-150</td>
<td>clean vehicle</td>
<td>32149</td>
<td>white</td>
<td>167741409</td>
</tr>
<tr>
<td>14000</td>
<td>ford</td>
<td>fusion</td>
<td>clean vehicle</td>
<td>50513</td>
<td>white</td>
<td>167749355</td>
</tr>
<tr>
<td>22500</td>
<td>ford</td>
<td>door</td>
<td>clean vehicle</td>
<td>43646</td>
<td>black</td>
<td>167780692</td>
</tr>
</tbody>
</table></span>
<div class="reference"><a href="https://www.kaggle.com/doaaalsenani/usa-cers-dataset">Sample data from Kaggle's US Cars dataset</a></div>
</div>
</div>
</section>
<section class="full">
<h2>File formats</h2>
<div class="flex tiny">
<div class="half tiny">
<h3>Comma Separated File (CSV)</h3>
<pre class="small"><code>price,brand,model,title_status,mileage,color,lot
21500,ford,f-150,clean vehicle,76876,gray,167783132
7500,nissan,door,clean vehicle,58126,black,167598085
10700,chevrolet,trax,clean vehicle,44280,red,167792666
23100,dodge,charger,clean vehicle,12265,silver,167735423
33800,dodge,durango,clean vehicle,15003,black,167734879
4140,ford,door,clean vehicle,40747,white,167656519
13900,nissan,rogue,clean vehicle,38760,white,167762102
15700,ford,cutaway,clean vehicle,75862,white,167780452
1150,ford,door,salvage insurance,123349,red,167652717
26100,ford,f-150,clean vehicle,32149,white,167741409
14000,ford,fusion,clean vehicle,50513,white,167749355
22500,ford,door,clean vehicle,43646,black,167780692</code></pre>
</div>
<div class="half tiny">
<h3>JSON</h3>
<pre class="small"><code>[
{
"price": 21500,
"brand": "ford",
"model": "f-150",
"title_status": "clean vehicle",
"mileage": 76876,
"color": "gray",
"lot": 167783132
},
{
"price": 7500,
"brand": "nissan",
"model": "door",
"title_status": "clean vehicle",
"mileage": 58126,
"color": "black",
"lot": 167598085
},
{
"price": 10700,
"brand": "chevrolet",
"model": "trax",
"title_status": "clean vehicle",
"mileage": 44280,
"color": "red",
"lot": 167792666
},
{
"price": 23100,
"brand": "dodge",
"model": "charger",
"title_status": "clean vehicle",
"mileage": 12265,
"color": "silver",
"lot": 167735423
},
{
"price": 33800,
"brand": "dodge",
"model": "durango",
"title_status": "clean vehicle",
"mileage": 15003,
"color": "black",
"lot": 167734879
},
{
"price": 4140,
"brand": "ford",
"model": "door",
"title_status": "clean vehicle",
"mileage": 40747,
"color": "white",
"lot": 167656519
},
{
"price": 13900,
"brand": "nissan",
"model": "rogue",
"title_status": "clean vehicle",
"mileage": 38760,
"color": "white",
"lot": 167762102
},
{
"price": 15700,
"brand": "ford",
"model": "cutaway",
"title_status": "clean vehicle",
"mileage": 75862,
"color": "white",
"lot": 167780452
},
{
"price": 1150,
"brand": "ford",
"model": "door",
"title_status": "salvage insurance",
"mileage": 123349,
"color": "red",
"lot": 167652717
},
{
"price": 26100,
"brand": "ford",
"model": "f-150",
"title_status": "clean vehicle",
"mileage": 32149,
"color": "white",
"lot": 167741409
},
{
"price": 14000,
"brand": "ford",
"model": "fusion",
"title_status": "clean vehicle",
"mileage": 50513,
"color": "white",
"lot": 167749355
},
{
"price": 22500,
"brand": "ford",
"model": "door",
"title_status": "clean vehicle",
"mileage": 43646,
"color": "black",
"lot": 167780692
}
]
</code></pre>
</div>
</div>
</section>
<section>
<h2>Temporal</h2>
<ul>
<li>Tabular + time-related attribute</li>
<li class="fragment">Aggregations</li>
<li class="fragment">Cyclical</li>
<li class="fragment">Events or intervals</li>
<li class="fragment">Seasonality patterns</li>
</ul>
</section>
<section>
<h2>Networks</h2>
<ul>
<li>Nodes + links</li>
<li class="fragment">Attributes on both</li>
<li class="fragment">Network idioms</li>
<li class="fragment">Network analytics</li>
</ul>
</section>
<section>
<h2>How are networks stored?</h2>
<ul>
<li>Two files</li>
<li class="fragment">JSON file</li>
<li class="fragment">Adjacency matrix</li>
<li class="fragment">Generated from any data</li>
</ul>
</section>
<section>
<h2>Trees</h2>
<ul>
<li>Hierarchical data</li>
<li class="fragment">A subset of networks</li>
<li class="fragment">Deserves its own class!</li>
<li class="fragment">Tree idioms</li>
<li class="fragment"><a href="https://observablehq.com/@john-guerra/treemap-from-table?collection=@john-guerra/d3-tutorials">Can be created out of tables</a></li>
</ul>
</section>
<section>
<h2>How are trees stored?</h2>
<ul>
<li>Table with path attribute</li>
<li class="fragment">JSON file</li>
<li class="fragment">Generated from any data</li>
</ul>
</section>
<section>
<h2>Geometry</h2>
<ul>
<li>Location data (usually latitutde/longitude)</li>
<li class="fragment">2D or 3D</li>
<li class="fragment">Inherent shape</li>
<li class="fragment">Points, routes, shapes</li>
</ul>
</section>
<section>
<h2>Fields</h2>
<ul>
<li>Continuous</li>
<li class="fragment">Sampled</li>
<li class="fragment">Example: brain scan</li>
</ul>
</section>
<section>
<h2>Data availability</h2>
<ul>
<li>Static 👉 data don't change</li>
<li class="fragment">Dynamic 👉 data are constantly updating (e.g., stock prices)</li>
<li class="fragment">For dynamic data, you will need a data endpoint</li>
</ul>
</section>
<section>
<div><img src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></div>
</section>
</section>
<section id="datasetStorage">
<section>
<h1>Dataset Storage</h1>
</section>
<section>
<h3>Compute vs Store</h3>
<ul>
<li>Always a balancing act.</li>
<li>Front end computation is faster than you may expect, while load times are user-dependent.</li>
</ul>
</section>
<section>
<h3>API basics</h3>
<ul>
<li>API's are just HTTP/HTTPS endpoints like any other web page.</li>
<li>Response headers can indicate expected file type returned.</li>
<li>Demo: hit <a href="http://hedonometer.org/api/v1/happiness/?format=json&title=en_all&date__gt=2020-05-01&limit=10">hedonometer.org/api/v1/happiness/?format=json&title=en_all&date__gt=2020-05-01&limit=10</a></li>
</ul>
</section>
<section>
<h3>Connecting to a database</h3>
<ul>
<li>D3 needs an API (Flask or likewise).</li>
<li>Python/R: many options (sqlalchemy, native clients, etc).</li>
<li>Tableau built-in: live vs extract.</li>
</ul>
</section>
<section>
<h3>Case studies for complex data processing</h3>
<ul>
<li>MapD.</li>
<li>Hedonometer.</li>
<li>imMens.</li>
<li>Crossfilter.</li>
</ul>
</section>
<section>
<h3>MapD (now Omni-Sci)</h3>
<ul>
<li>Original <a href="http://go.mapd.com/resources_whitepaper_mapd_lp.html">white paper</a>.</li>
<ul>
<li>Computation done server side on custom hardware: GPU memory.</li>
<li>Interacts with front end via API.</li>
<li>Fast on a scale that's unimaginable in the front-end alone.</li>
</ul>
</ul><img src="../shared_images/mapd.png" alt="MapD">
</section>
<section>
<h3>Hedonometer</h3>
<ul>
<li>Based on 10% decahose feed of all Tweets, ~250-500GB uncompressed per day.</li>
<ul>
<li>Processing of Tweets to word count dictionaries done in parallel on 200+ node cluster.</li>
<li>Jobs in 15 minute increments, run hourly.</li>
<li>Webserver consumes these dictionaries, sums daily (Jenkins).</li>
<li>Time series data is precomputed, fixed-length word count vectors precomputed for each day.</li>
</ul>
</ul>
</section>
<section>
<h3>Hedonometer</h3><img src="../shared_images/hedonometer-webserver.png" alt="MapD">
</section>
<section>
<h3>imMens</h3>
<ul>
<li>Based on binned aggregation.</li>
<li>Precomputes multivariate data projections and store these as data tiles.</li>
<li>Front-end dynamically loads appropriate data tiles and uses WebGL for data processing and rendering.</li>
</ul>
<div>
<iframe src="https://player.vimeo.com/video/68736161" width="640" height="400" frameborder="0" allow="autoplay; fullscreen" allowfullscreen></iframe>
<p><a href="https://vimeo.com/68736161">imMens: Real-time Visual Querying of Big Data</a> from <a href="https://vimeo.com/stanfordvis">Stanford Visualization Group</a> on <a href="https://vimeo.com">Vimeo</a>.</p>
</div>
<div class="reference"><a href="http://vis.stanford.edu/projects/immens/">imMens by Stanford</a></div>
</section>
<section>
<h3>Tiled backend</h3><img src="../shared_images/tiled.png" alt="Tiled">
</section>
<section>
<h3>Crossfilter</h3>
<ul>
<li>Purely front-end.</li>
<li>Hashed lookup optimized for filter operations in-memory.</li>
<li>Scales to 100's of thousands of records.</li>
</ul>
<div class="reference"><a href="https://crossfilter.github.io/crossfilter/">Crossfilter Demo </a></div>
</section>
<section>
<h3>Summary: questions to ask</h3>
<ul>
<li>Where is the data?</li>
<li>What format is it in?</li>
<li>How big is the data?</li>
<li>How complex or computationally intensive is the processing needed?</li>
<li>Never forget ethics: was it collected with consent for this use?</li>
</ul>
</section>
</section>
<section id="attributes">
<section>
<h1>Types of Attributes</h1>
</section>
<section><img src="../shared_images/vad_book_alldiagrams/data_attribute_types.png" alt="Data attribute types: categorical, ordinal and quantitative"></section>
<section>
<h2>Categorical</h2>
<ul>
<li>No order</li>
<li class="fragment">E.g., names, countries, types</li>
<li class="fragment">Must be represented with visual channels that don't convey order</li>
</ul>
</section>
<section>
<h2>Ordinal</h2>
<ul>
<li>Has implicit order</li>
<li class="fragment">But, you can't do arithmetic</li>
<li class="fragment">Can even be numerical</li>
<li class="fragment">E.g., t-shirt sizes, school years, rankings</li>
</ul>
</section>
<section>
<h2>Quantitative</h2>
<ul>
<li>Also ordered</li>
<li class="fragment">You can perform arithmetic</li>
<li class="fragment">Can be divergent or sequential</li>
<li class="fragment">E.g., age, temperature, earnings</li>
</ul>
</section>
<section>
<div><img src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></div>
</section>
</section>
<section id="quantitative">
<section>
<h1>Ordering direction</h1>
</section>
<section><img src="../shared_images/vad_book_alldiagrams/data_ordering_types.png" alt="Data ordering types: sequential, diverging and Cyclical"></section>
<section>
<h2>Sequential</h2>
<ul>
<li>The is a full range with a clear minimum</li>
<li class="fragment">You can perform arithmetic</li>
<li class="fragment">E.g., age, goals ⚽️, price 💰</li>
</ul>
</section>
<section>
<h2>Divergent</h2>
<ul>
<li>There is a middle point</li>
<li class="fragment">And two opposite directions</li>
<li class="fragment">Sometimes the middle point is not zero</li>
<li class="fragment">E.g., temperature, earnings, political affiliation index</li>
</ul>
</section>
<section>
<h2>Cyclical</h2>
<ul>
<li>There is a cycle in the data</li>
<li class="fragment">No starting point (?)</li>
<li class="fragment">Can be represented with cyclical channels</li>
<li class="fragment">E.g., Days of the week, hours in the day</li>
</ul>
</section>
<section>
<div><img src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></div>
</section>
</section>
<section id="dataAggregation">
<section>
<h1>Data Aggregation</h1>
</section>
<section>
<h2>Aggregating Spatial data</h2>
<div class="small">E.g., count votes by:</div>
<ul class="tiny">
<li>County</li>
<li class="fragment">City</li>
<li class="fragment">State</li>
<li class="fragment">Country</li>
<li class="fragment">Zipcode? <span>Can span multiple counties 🤷</span></li>
<li class="fragment">Custom areas</li>
</ul>
</section>
<section>
<h2>Aggregating Temporal data</h2>
<div>By truncating the date</div>
<ul>
<li>2020 June 27 5:25PM</li>
<li class="fragment">2020 June 27 5:00PM</li>
<li class="fragment">2020 June 27</li>
<li class="fragment">2020 June</li>
<li class="fragment">2020 Q1</li>
<li class="fragment">2020</li>
</ul>
<div class="fragment">Take a look at the <a href="https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Date">date object</a></div>
</section>
<section>
<h2>Aggregating Temporal data</h2>
<div>By extracting parts of the date</div>
<ul>
<li>2020 June 27 5:25PM</li>
<li class="fragment">Week 26 of the year</li>
<li class="fragment">5:00 - 6:00 PM</li>
<li class="fragment">Saturday</li>
<li class="fragment">June</li>
</ul>
</section>
<section>
<div><img src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></div>
</section>
</section>
<section id="practice">
<section>
<h1>Practice</h1>
</section>
</section>
<section id="introToJS">
<section>
<h2>Introduction to JS</h2>
</section>
</section>
<section id="JSONvsCSV">
<section>
<h2>JSON vs. CSV</h2>
</section>
<div class="flex tiny">
<div class="half tiny">
<h3>Comma Separated File (CSV)</h3>
<pre class="small"><code>price,brand,model,title_status,mileage,color,lot
21500,ford,f-150,clean vehicle,76876,gray,167783132
7500,nissan,door,clean vehicle,58126,black,167598085
10700,chevrolet,trax,clean vehicle,44280,red,167792666
23100,dodge,charger,clean vehicle,12265,silver,167735423
33800,dodge,durango,clean vehicle,15003,black,167734879
4140,ford,door,clean vehicle,40747,white,167656519
13900,nissan,rogue,clean vehicle,38760,white,167762102
15700,ford,cutaway,clean vehicle,75862,white,167780452
1150,ford,door,salvage insurance,123349,red,167652717
26100,ford,f-150,clean vehicle,32149,white,167741409
14000,ford,fusion,clean vehicle,50513,white,167749355
22500,ford,door,clean vehicle,43646,black,167780692</code></pre>
</div>
<div class="half tiny">
<h3>JSON</h3>
<pre class="small"><code>[
{
"price": 21500,
"brand": "ford",
"model": "f-150",
"title_status": "clean vehicle",
"mileage": 76876,
"color": "gray",
"lot": 167783132
},
{
"price": 7500,
"brand": "nissan",
"model": "door",
"title_status": "clean vehicle",
"mileage": 58126,
"color": "black",
"lot": 167598085
},
{
"price": 10700,
"brand": "chevrolet",
"model": "trax",
"title_status": "clean vehicle",
"mileage": 44280,
"color": "red",
"lot": 167792666
},
{
"price": 23100,
"brand": "dodge",
"model": "charger",
"title_status": "clean vehicle",
"mileage": 12265,
"color": "silver",
"lot": 167735423
},
{
"price": 33800,
"brand": "dodge",
"model": "durango",
"title_status": "clean vehicle",
"mileage": 15003,
"color": "black",
"lot": 167734879
},
{
"price": 4140,
"brand": "ford",
"model": "door",
"title_status": "clean vehicle",
"mileage": 40747,
"color": "white",
"lot": 167656519
},
{
"price": 13900,
"brand": "nissan",
"model": "rogue",
"title_status": "clean vehicle",
"mileage": 38760,
"color": "white",
"lot": 167762102
},
{
"price": 15700,
"brand": "ford",
"model": "cutaway",
"title_status": "clean vehicle",
"mileage": 75862,
"color": "white",
"lot": 167780452
},
{
"price": 1150,
"brand": "ford",
"model": "door",
"title_status": "salvage insurance",
"mileage": 123349,
"color": "red",
"lot": 167652717
},
{
"price": 26100,
"brand": "ford",
"model": "f-150",
"title_status": "clean vehicle",
"mileage": 32149,
"color": "white",
"lot": 167741409
},
{
"price": 14000,
"brand": "ford",
"model": "fusion",
"title_status": "clean vehicle",
"mileage": 50513,
"color": "white",
"lot": 167749355
},
{
"price": 22500,
"brand": "ford",
"model": "door",
"title_status": "clean vehicle",
"mileage": 43646,
"color": "black",
"lot": 167780692
}
]
</code></pre>
</div><a href="http://learnjsdata.com/read_data.html">http://learnjsdata.com/read_data.html</a>
</div>
</section>
<section id="ArrayFunctions">
<section>
<h2>Array functions (map, filter, sort)</h2><a href="http://learnjsdata.com/group_data.html">http://learnjsdata.com/group_data.html</a>
</section>
</section>
<section id="nestingJS">
<section>
<h2>Nesting, Folding, Pivoting, etc. in JS</h2><a href="http://learnjsdata.com/group_data.html">http://learnjsdata.com/group_data.html</a>
</section>
</section>
<section id="nestingPython">
<section>
<h2>Nesting, Folding, Pivoting, etc. in Python</h2>
</section>
</section>
<section id="nestingTableau">
<section>
<h2>Nesting, Folding, Pivoting, etc. in Tableau?</h2>
</section>
</section>
<section id="flask">
<section>
<h2>Serving data from the back-end (Introduction to Flask)</h2>
</section>
</section>
<section id="review">
<section>
<h1>What we learned</h1>
</section>
<section data-background-size="contain" data-background="../shared_images/what_sheet.png" id="what"></section>
<section>
<h2>What we learned</h2>
<ul>
<li>Abstracting the data</li>
<li class="fragment">Types of datasets</li>
<li class="fragment">Types of attributes</li>
<li class="fragment">Sequential, diverging, cyclical</li>
<li class="fragment">Data aggregation (temporal and geo)</li>
</ul>
</section>
<section>
<div><img src="../shared_images/UC_Berkeley_wordmark_cal_gold.png" alt="University Of California at Berkeley logo"></div>
</section>
</section>
</div>
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