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20 changes: 20 additions & 0 deletions 404.html
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<!DOCTYPE html>
<html lang="en">
<head>
<title>404 Not Found | Data 8</title>
</head>

<body id="index" class="home">
<div class="head">
<h1>
404 Not Found
</h1>
<h1>
Return to
<a href="http://data8.org/">
<span class="coursename">Data 8</span>
</a>
</h1>
</body>

</html>
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data8.org
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<!DOCTYPE html>
<html lang="en">
<head>
<meta name="description" content ="STAT 94: Foundations of Data Science" />
<meta name="keywords" content ="DS8, Data Science, Berkeley" />
<meta name="author" content ="Ani Adhikari, John DeNero, Michael I. Jordan, Tapan Parikh, David Wagner, Henry Milner, Ross Boczar, Sam Lau" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<meta name="viewport" content="width=device-width">

<link href="https://fonts.googleapis.com/css?family=Lato" rel="stylesheet" type="text/css">
<link rel="stylesheet" type="text/css" href="./assets/css/style.css">
<link rel="icon" href="./assets/images/favicon.ico">
<link rel="stylesheet" type="text/css" href="assets/css/article.css"/>

<title>Course Information | STAT 94 Fall 2015</title>

</head>

<body id="index" class="home">
<div class="head">
<h2>
<a href="http://data8.org/">
<span class="coursename">STAT 94</span>
</a>
</h2>
<p> Foundations of Data Science <br> Fall 2015</p>
<p>
<div class="instructors">
<span class="category">Principal Instructor:</span> <br> Ani Adhikari
</div>
<div class="instructors">
<span class="category">Co-Instructors:</span><br>
John DeNero<br>Michael I. Jordan<br>Tapan Parikh<br>David Wagner
</div>
</p>
<hr/>
<ul class="navigation">
<a href="./"><li>Calendar</li></a>
<a href="./resources.html"><li>Resources</li></a>

</ul>
<hr/>
<ul class="navigation">
<a href="./about.html"><li>Course Info</li></a>
<a href="./staff.html"><li>Staff</li></a>
<a href="./datascience/"><li><code>datascience</code> Reference</li></a>

<a href="https://piazza.com/berkeley/fall2015/ds10"><li>Piazza</li></a>
<a href="https://bcourses.berkeley.edu/courses/1377166"><li>bCourses</li></a>

</ul>
<hr/>
</div>

<div id="content">
<h2 id="course-information-and-policies">Course Information and Policies</h2>


<p>This introductory course in data science is built on three interrelated
perspectives: inferential thinking, computational thinking, and real-world
relevance. Given data arising from some real-world phenomenon, how does one
analyze that data so as to understand that phenomenon? How does one collect
data to answer questions that one is interested in? Inferential thinking
refers to an ability to connect data to underlying phenomena and to the ability
to think critically about the conclusions that are drawn from data analysis.
Computational thinking refers to the ability to conceive of the abstractions
and processes that allow inferential procedures to be embodied in computer
programs, and to ensure that such programs are scalable, robust and
understandable. In addition to teaching basic skills in computer programming
and statistical inference, the course will also involve the hands-on analysis
of a variety of real-world datasets, including economic data, document
collections, geographical data and social networks, and it will delve into
social and legal issues surrounding data analysis, including issues of privacy
and data ownership.</p>


<h2 id="course-format">Course Format</h2>


<p>The course includes many events: lecture, lab, office hours, and review
sessions. Weekly lab and lecture are typically the most valuable events to
attend.</p>

<p><strong>Lecture</strong>: The course includes three 50-minute lectures per week.</p>

<p><strong>Lab</strong>: The course includes one laboratory section each week. These sections
are run by an amazing group of Graduate Student Instructors. Getting to
know your GSI is an excellent way to succeed in this course. Participation in
lab is tracked but not required.</p>

<p><strong>Office Hours</strong>: Attending office hours is another excellent way to succeed in this course. Office hours are held by GSIs and the instructor each week. A schedule appears on the staff page of the course website.</p>

<p>In office hours, you can ask questions about the material, receive guidance on
assignments, and work with peers and course staff in a small group setting.</p>


<h2 id="grading">Grading</h2>


<p>Grades for this course are assigned using the following sources.</p>

<ul>
<li>30% Projects</li>
<li>20% Homework</li>
<li>20% Midterm</li>
<li>30% Final</li>
</ul>

<p>The midterm exam will be held in class on Monday 10/19. The final exam will be
held on Monday 12/14 from 8am to 11am, location to be announced.</p>


<h2 id="materials">Materials</h2>


<p>The primary text for this course is
<a href="http://data8.org/text">Computational and Inferential Thinking</a>, an online
textbook.</p>

<p>Homework will be distributed each week on paper (typically at lecture on
Friday) and posted online. You need to complete them in the spaces given on
the page where they appear, <strong>not</strong> electronically or on a separate piece of
paper. (Having a single format helps us grade them and forces you to write
concisely.) If you print your own copy, please print it double-sided.</p>

<p>Homework assignments are due in your lab section the week after they are handed out. (For example, the homework handed out on Friday, 8/28 is due in lab on Wednesday 9/2 or, for those in the Thursday lab, Thursday 9/3.)</p>


<h2 id="computing-resources">Computing Resources</h2>


<p>All computing assignments in this course will be completed on
<a href="http://ds8.berkeley.edu">ds8.berkeley.edu</a>. You can complete all computing
labs and projects using any computer (or device) that has a web browser.</p>

<p>The lab room for the course is 105 Cory. If you would like to use a lab
computer to work on an assignment, all students will have 24 hour access
to this room whenever there is not another course using it. By enrolling in
the course, you will have card-key access to this room using your Cal ID card.</p>
</div>
</body>

</html>
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/*
:Author: John DeNero
:Contact: [email protected]
:Copyright: This stylesheet has been placed in the public domain.
Stylesheet for use with the Data Science 8 website.
Fonts adopted from Mark Pilgrim's diveintopython3.org.
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/* Stylesheet for Data Science 8 resources */

html {
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