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<!DOCTYPE html>
<html lang="en-us">
<head>
<meta charset="UTF-8">
<title>Smart cities connector by dsten</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheets/normalize.css" media="screen">
<link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
<link rel="stylesheet" type="text/css" href="stylesheets/stylesheet.css" media="screen">
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</head>
<body>
<section class="page-header">
<h1 class="project-name">CE 88 Data Science for Smart Cities</h1>
<h2 class="project-tagline">A Fall 2016 Data Science Connector Course </h2>
<h3>CCN: 33415 | Alexey Pozdnukhov | Monday 12:00-2:00 PM | Dwinelle 219 | Units: 2</h3>
<a href="http://data8.org/" class="btn">Data8.org</a>
<a href="http://databears.berkeley.edu/" class="btn">Databears</a>
<a href="https://piazza.com/class/ijembnba4oi3l2" class="btn">Piazza</a>
<a href="https://bcourses.berkeley.edu/courses/1413128" class="btn">bCourses</a>
</section>
<section class="main-content">
<h3>Welcome to CE 88 Data Science for Smart Cities</h3>
<p>Cities are becoming more dependent on data flows that connect users and infrastructure. Design and operation of smart, efficient, and resilient cities has come to require data science skills. This course teaches you to leverage data generated within transportation systems, power grids, communication networks, via crowd-sensing, and remote sensing technologies to build demand- and supply- side urban services based on data analytics.</p>
<p>The Data Science for Smart Cities connector is offered through the <a href="http://www.ce.berkeley.edu/">Civil and Environmental Engineering Department</a> at UC Berkeley and is taught in conjuction with the <a href="http://data8.org/">Foundations of Data Science course</a>. The Foundations of Data Science course provides a baseline of computing skills, data visualization, and statistical concepts. There are no formal pre-requisites so you can also take it independently of Data8 (backgorund of junior/senior standing in any engineering major helps). Don't forget to enroll in all 3 components (Lecture, Lab, Discussion).</p>
<h3>Syllabus</h3>
<h4>Introduction: Cities as complex systems</h4>
<b>August 29</b>
<ul>
<li>Introduction to urban systems</li><!--<a href="http://data8.org/smart-cities-connector/lecture_slides/CE88_Lecture_1.pdf"> Slides </a></li> -->
<li>Modeling principles</li>
</ul>
<b>September 12</b> - Minilab 1 - Homework 1
<!-- <a href="https://data8.berkeley.edu/hub/interact?repo=smart-cities-connector&path=demos"> Mini-lab 1 </a> - <a href="http://data8.org/smart-cities-connector/homeworks/CE88_Homework1.pdf"> Homework 1 </a>-->
<ul>
<li>Spatio-temporal nature of urban data </li>
<li>Data flow in cities </li>
</ul>
<h4>Urban data: Collection, handling and processing</h4>
<b>September 19</b> - Mini-lab 2 - Homework 2
<ul>
<li>Data acquisition </li>
<li>Census and open govenment data </li>
</ul>
<b>September 26</b> - Mini-lab 3 - Homework 3
<ul>
<li>Demand data exploration</li>
<li>Supply data exploration</li>
</ul>
<b>October 3</b> - Mini-lab 4 - Homework 4
<ul>
<li>Environmental and economic impact </li>
<li>Energy consumption in transportation</li>
</ul>
<h4>Data exploration: Interpreting and exploring data through visualization</h4>
<b>October 10</b> - Mini-lab 5 - Homework 5
<ul>
<li>Visualization and exploratory analysis</li>
<li>Multivariate data visualization</li>
</ul>
<b>October 17</b> - Mini-lab 6 - Homework 6
<ul>
<li>Travel mode choice data </li>
<li>Signal processing and data integration</li>
</ul>
<b>October 24</b> - Midterm
<ul>
<li>Midterm Introduction</li>
</ul>
<b>October 31</b> - Mini-lab 7 </a>
<ul>
<li>Predictions with linear regression</li>
</ul>
<h4>Modeling and forecasting: Regression and classification</h4>
<b>November 7</b> - Mini-lab 8 - Homework 8
<ul>
<li>Uncertainty in predictions - working with confidence intervals</li>
</ul>
<b>November 14</b> - Mini-lab 9 - Homework 9
<ul>
<li>A look at energy data - consumption and generation</li>
<li>Variability of renewable sources</li>
</ul>
<b>November 21</b> - Mini-lab 10 - Homework 10
<ul>
<li>Jobs in datascience</li>
<li>Notes on hypothesis testing</li>
</ul>
<b>November 28</b> - Final </a>
<ul>
<li>EV charging patterns</li>
<li>Introduction to clustering</li>
</ul>
<h4>Decision making: Planning and governance</h4>
<b>December 5</b>
<!-- <ul> <li>Balancing demand and supply</li> <li>Optimization</li> </ul> -->
<ul>
<li>Course overview</li>
<li>Final project discussion</li>
</ul>
</html>