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<html lang="en">
<head>
<style>
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text-align: justify; }
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list-style-position: inside; }
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<meta name="description" content="Introduction to Data Science" />
<meta name="author" content="John P. Dickerson" />
<link rel="icon" href="favicon.ico" />
<title>Introduction to Data Science</title>
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<![endif]-->
<!-- Fancy fonts for the headers -->
<link href="https://fonts.googleapis.com/css?family=Baloo+Paaji" rel="stylesheet" />
</head>
<body>
<div class="container">
<!-- Header and Navigation -->
<div class="header clearfix">
<nav>
<ul class="nav nav-pills pull-right">
<li role="presentation"><a href="#description">Description</a></li>
<li role="presentation"><a href="#schedule">Schedule</a></li>
<li role="presentation"><a href="#assignments">Deliverables</a></li>
</ul>
</nav>
<h3 class="text-muted">CMSC/DATA 320 – Spring 2026 (Section 0201) </h3>
</div>
<!-- Square with Basic Info -->
<div class="jumbotron">
<h2>Introduction to Data Science</h2>
<!-- <img class="img-responsive" src="img/ds_01.jpg" alt="Data Science!?"> <!-- image from Vanguard -->
<!-- <img class="img-responsive" src="img/ds_02.jpg" alt="Data Science!?"> <!-- image from Forbes -->
<!-- <img class="img-responsive" src="img/ds_03.jpg" alt="Data Science!?"> <!-- image from Business Intelligence -->
<!-- <img class="img-responsive" src="img/ds_04.png" alt="Data Science!?"> <!-- image from https://gcn.com/articles/2017/12/05/data-inventory.aspx -->
<!-- <img class="img-responsive" src="img/ds_05.jpeg" alt="Data Science!?" /> <!-- image from Amber; not sure of the source-->
<!-- <img class="img-responsive" src="img/ds_06.png" alt="Data Science!?" /> <!-- image from Wikipedia (Sociology) -->
<img class="img-responsive" src="img/OIG.jpeg" alt="Header image to data science course website showing a multi-colored cube or matrix" height="100"/> <!-- image from https://hbr.org/2018/10/prioritize-which-data-skills-your-company-needs-with-this-2x2-matrix -->
<p>
<address>
<!-- Aishani Edit Start -->
<strong>Instructor: </strong>
<div class="instructor">
<div class="ta-group">
<img src="img/headshots/dr_fardina.JPG" alt="Dr. Fardina" width="70" height="70">
<a href="https://www.fardinafathmiulalam.com/">Dr. Fardina Alam</a> (fardina@umd.edu)
</div>
</div>
<strong>Head Teaching Assistants (TAs): </strong>
<div class="head-tas">
<div class="ta-group">
<img src="img/headshots/aviva.JPG" alt="Aviva" width="70" height="70">
<a href="">Aviva Prins</a>
</div>
<div class="ta-group">
<img src="img/headshots/guanhong.JPG" alt="Guanhong" width="70" height="70">
<a href="">Guanhong Wang</a>
</div>
<!-- <div class="ta-group">
<img src="img/headshots/pulkit.JPG" alt="Pulkit" width="70" height="70">
<a href="">Pulkit Kumar</a>
</div> -->
</div>
<strong>Teaching Assistants (TAs): </strong>
<div class="tas-section">
<div class="ta-group">
<img src="img/headshots/yonghan.JPG" alt="Yonghan" width="70" height="70">
<a href="">Yonghan Lee</a>
</div>
<div class="ta-group">
<img src="img/headshots/shengjie.JPG" alt="Shengjie" width="70" height="70">
<a href="">Shengjie Xu</a>
</div>
<div class="ta-group">
<img src="img/headshots/sakshi.JPG"" alt="Fnu" width="70" height="70">
<a href="">Fnu Sakshi</a>
</div>
<!-- <div class="ta-group">
<img src="" alt="Bhuvanesh" width="70" height="70">
<a href="">Bhuvanesh Murali</a>
</div> -->
</div>
<div class="tas-section">
<!-- <div class="ta-group">
<img src="" alt="Weize" width="70" height="70">
<a href="">Weize Liu</a>
</div> -->
<div class="ta-group">
<img src="img/headshots/simi.JPG" alt="Taewon" width="70" height="70">
<a href="">Simi Shrivastava</a>
</div>
<div class="ta-group">
<img src="" alt="Siva" width="70" height="70">
<a href="">Siva Buthada</a>
</div>
<div class="ta-group">
<img src="" alt="Mahek" width="70" height="70">
<a href="">Fnu Mahek</a>
</div>
<!-- <div class="ta-group">
<img src="img/headshots/Tanya.JPG" alt="Tanya" width="70" height="70">
<a href="">Tanya Gupta</a>
</div> -->
</div>
<!-- <strong>Teaching Assistants (TAs): </strong>
<table>
<tr>
<td align="center">
<img src="img/headshots/yonghan.JPG" alt="Yonghan" width="70" height="70"><br>
<a href="">Yonghan Lee</a>
</td>
<td align="center">
<img src="img/headshots/shengjie.JPG" alt="Shengjie" width="70" height="70"><br>
<a href="">Shengjie Xu</a>
</td>
<td align="center">
<img src="" alt="Bhuvanesh" width="70" height="70"><br>
<a href="">Bhuvanesh Murali</a>
</td>
</tr>
<tr>
<td align="center">
<img src="" alt="Weize" width="70" height="70"><br>
<a href="">Weize Liu</a>
</td>
<td align="center">
<img src="" alt="Fnu" width="70" height="70"><br>
<a href="">Fnu Sakshi</a>
</td>
<td align="center">
<img src="" alt="Taewon" width="70" height="70"><br>
<a href="">Taewon Kang</a>
</td>
</tr>
</table> -->
<div class="lecture-schedule">
<strong>Lecture Schedule:</strong>
IRB 0324: 12:30 PM - 1:45 PM EDT, Tuesday & Thursday.
</div>
</address>
<style>
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}
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</p>
</div>
<!-- Aishani Edit End -->
<hr class="featurette-divider">
<!-- Big Class Description -->
<div class="row marketing" id="description">
<h1>Course Overview</h1>
<p>Data science encapsulates the interdisciplinary activities required to create data-centric products and applications that address specific scientific, socio-political, or business questions. It has drawn tremendous attention from both academia and industry and is making deep inroads in industry, government, health, and journalism.</p>
<p>This course focuses on (i) data management systems, (ii) exploratory and statistical data analysis, (iii) data and information visualization, and (iv) the presentation and communication of analysis results. It will be centered around case studies drawing extensively from applications, and will yield a publicly-available final project or tutorial that will strengthen course participants' data science portfolios.
</p>
<p>The course will primarily consist of sets of self-contained lectures and assignments that leverage real-world data science platforms when needed. As such, there is no assigned textbook, but there will be some recommended ones. Many lectures will come with links to required readings, which should be completed before or after the lecture (as declared), and, when appropriate, a list of links to other web resources.</p>
<p>
<p><strong><span style="color: red;">Note:</span></strong> <strong>This course outline is tentative and subject to modification to meet the specific needs and requirements of the students and the evolving field of data science.</strong></p>
</p>
<h3>Prerequisites</h3>
<p>
Students enrolled in the course should be comfortable with programming
(for those at UMD, passing CMSC216 will be sufficient) and have a reasonable level of mathematical maturity.
The course will primarily use the <a href="https://www.python.org/">Python</a> programming language
through <a href="https://colab.research.google.com/">Google Colab</a>
(or <a href="https://jupyter.org/">Jupyter</a> Notebook if needed),
with support from the <a href="https://conda.io/docs/">Anaconda</a> package manager.
Primer lectures on Python for data science will be provided early in the semester,
so there is no need to worry if you are new to Python.
Later lectures will introduce statistics and machine learning,
potentially incorporating basic calculus and linear algebra.
A light level of mathematical maturity, roughly equivalent to that of a junior CS student, is preferred.
</p>
<p><em>This course is aimed at junior- and senior-level Computer Science majors, but should be accessible to any student of life with some degree of mathematical and statistical maturity, reasonable experience with programming, and an interest in the topic area. If in doubt, e-mail me: fardina@umd.edu!</em></p>
<h3> Tentative Topics:</h3>
<p> Introduction to Data Science, Experiment Design, Introduction to Python, Data Types, Data collection, Git, Pandas, Database, SQL, Probability, Summary Statistics, Hypothesis Testing, Data Visualization, Data Exploration, Introduction to Machine Learning, Classifications, Decision Trees, Regression, Feature Engineering, ML Evaluation, Neural Network, Image Processing, Natural Language Processing (NLP),
Introduction to Graph(s) Theory, Recommender Systems, Data Science Ethics
</p>
<h3> Learning Outcomes: </h3>
<p> At the completion of this course, students will be able to:
<ul>
<li>Develop a foundational understanding of data science, encompassing key concepts, methodologies, and tools.</li>
<li>Proficiently use Python for data science applications, including data types, collection, and manipulation using libraries like Pandas.</li>
<li>Explore machine learning concepts, including classifications, decision trees, regression, and feature engineering.</li>
<li>Evaluate machine learning models using appropriate metrics, ensuring a comprehensive understanding of model performance.</li>
<li>Understand advanced topics such as neural networks, image processing, natural language processing (NLP) etc.</li>
<li>Understand the importance of applying ethical frameworks and guidelines to make informed decisions in real-world data scenarios.</li>
</ul>
By the end of the course, students will have acquired a versatile skill set, enabling them to tackle real-world data science challenges with confidence and ethical awareness.
</p>
<h3>Grading</h3>
<p>There will be five (to six) assignments, one semester long final group project, two written midterm exams, and one final written exams (cumulative).</p>
<p> Final grades will be calculated as:<br>
<table class="table table-striped">
<tr>
<th>Component</th>
<th>Percentages</th>
</tr>
<tr>
<td>Assignments/Mini Projects</td>
<td>35%</td>
</tr>
<tr>
<td>Mid Exam 01</td>
<td>15%</td>
</tr>
<tr>
<td>Mid Exam 02</td>
<td>15%</td>
</tr>
<tr>
<td>Final Project/Tutorial</td>
<td>15%</td>
</tr>
<tr>
<td>Final Exam</td>
<td>20%</td>
</tr>
</table>
</p>
<h4>Late Policy</h4>
<p>Late work typically receives no credit, except as specified below.</p>
<p> It is recommended to submit homework and projects on time. There will be a <strong>15% penalty for late submissions of homework</strong> and a <strong>20% penalty for late submissions of project/tutorial checkpoints 1 and 2</strong> within 24 hours after the deadline</strong>.
<strong>After this 24-hour period, no submissions will be accepted</strong>. In <strong>ELMS/Gradescope (as instructed in the assignment or project)</strong>,
you can submit multiple times, and only the last submission will be graded. The penalty for late homework will be applied automatically; no request is necessary.
This policy applies to both homework and projects, EXCEPT for the <strong>final submission/checkpoint 3 of the final project/tutorial and for any kind of BONUS work</strong>
</p>
<p>See the next section about how to contact us in special circumstances. We aim to help everyone
succeed.
</p>
<h3>Communication & Logistics Requests</h3>
<p>We are going to use a combination of in-person and online office hours and the Piazza forum (<a href="https://piazza.com/umd/spring2026/cmscdata3200201/home">sign up here</a>) for Q&A. This means that it's appropriate to use Piazza for asynchronous communication with the course instructors and other students, and also for short, high-bandwidth discussions that could usually take place before/after class.</p>
<p>Note that Piazza is not appropriate for things like asking for accommodations, extensions or other such issues/concerns.
For any logistics-related help, such as extensions or grading issues, please <strong>fill out this form</strong>: CMSC320 Logistics Request Form.
<a href="https://cmsc320.github.io/Request_form">CMSC320- Logistic Request Form</a>
However, If you do not receive a response or if your issue is not resolved within 48 hours, then please email: </p>
<ul><li><a href="mailto:cmsc320gradingissues.dr.fardina@gmail.com">cmsc320gradingissues.dr.fardina@gmail.com</a> for any grading, logistics, and extension issues</li>
<!-- <li><a href="mailto:cmsc320logistics.dr.fardina@gmail.com">cmsc320logistics.dr.fardina@gmail.com</a> for logistics and extensions</li> -->
</ul>
<p> While sending an email, at the beginning of your email subject line, include relevant tags, such as <code>[hw_extension/req]</code>, <code>[project_extension/req]</code>,<code> [gradeissue]</code>,
etc. More tags will be updated on the website. <br>
If you have a request that fits into one of these categories and you <em>don't</em> email the address given above,
then your request may not reach the right person and may not be answered in a timely manner.</p>
<p>If your correspondence does not fit into either of those two categories, please email an instructor (professor) <strong>with
<code><span style="color: red;">[CMSC320]</span></code> in the email subject line.</strong> You may also go to an instructor's office hours at the times listed below.
</p>
<p><strong>Please note: if you don't include <code>[CMSC320]</code> in your subject line when emailing instructors, your email
may not be filtered correctly.</strong></p>
<h3>Office Hours</h3>
<p> <em>* All hours are EDT</em></p>
<h4>Instructor Office Hours and TA office hours details can be found on ELMS as well as on Piazza.
</h4>
<!-- <!-- <h4>Instructor Office Hours</h4>
<p>
<!-- Instructor Office Hour Table-->
<!-- <table class="table table-striped">
<thead>
<tr>
<th style="width: 30%">Instructor</th>
<th style="width: 45%">Time</th>
<th style="width: 25%">Location</th>
</tr>
</thead>
<tbody>
<tr>
<td>Dr. Fardina Alam</td>
<td>Time and Day: TBD</td>
<td>IRB 2222</td>
</tr>
</tbody>
</table>
</p> -->
<!-- <!-- <h4>TA Office Hours</h4> -->
<!-- <p>You can find TA office hours locations for all your courses
<a href="http://www.cs.umd.edu/class/resources/cstarooms/fallspring/">here</a>.
CMSC320's assigned space is <strong>AVW 4122</strong>, and
if TAs are hosting hours virtually, you can find the zoom link next to their names.</p> -->
<p><strong>
Do keep an eye on Piazza, though; TAs will sometimes swap hours, shift hours,
host hours on Zoom, and so on!</strong> Additionally, we have at least one TA
explicitly covering Piazza on each weekday; all course staff will float around Piazza in general, too!</p>
<!-- <p>The table below is mostly in time order.</p> -->
<strong>THERE ARE NO OFFICE HOURS FIRST WEEK (Jan 26 - Jan 30)</strong>
<hr class="featurette-divider">
<!-- Big Schedule Table -->
<div class="row marketing" id="schedule">
<h1>Schedule</h1>
<p><strong>ALL THE LECTURE SLIDES WILL BE POSTED HERE</strong></p>
<p>The schedule is subject to change as the semester progresses!</p>
<table class="table table-striped">
<caption></caption>
<thead>
<tr>
<th>#</th>
<th style="width: 17%">Date</th>
<th>Topic</th>
<th>Reading</th>
<th>Slides</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<th scope="row">1</th>
<td>Jan 27, Tu</td>
<td>
Introduction to Data Science (<strong>Chapter 1</strong>) and Data Type (<strong>Chapter 2</strong>)
</td>
<td>
Self-Study Slide:<br>
<a href="https://docs.google.com/presentation/d/1tMAcTbH0yPA4fVasPqndfbQdkSWuWLSX/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Git</a><br>
Additional Reading/Helpful slide:<br>
<a href="https://docs.google.com/presentation/d/1tOC0EOiF4Dkavjz3To07wXC_EdKW32qvZyN3XizvMlo/edit?usp=sharing">Python</a><br>
<a href="https://colab.research.google.com/drive/1xHJHDJPEAZCrUeB1-iIFm8a3Rw3od5In?usp=sharing">Demo Python</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1m5cYLPfKSNL8-ElwSjOQeWK4r1BmPtj7/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec1.1Intro</a>
<a href="https://docs.google.com/presentation/d/1j6yD2cODvn2_wIT5iiBY7LukXc0aCoAV/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec1.2DataTypes</a> (Self-Study)
</td>
<td>
Sign up on <a href="https://piazza.com/umd/spring2026/cmscdata3200201/home">Piazza!</a>
</td>
</tr>
<tr>
<th scope="row">2</th>
<td>Jan 29, Th</td>
<td><s>Experiment Design (<strong>Chapter 3</strong>)</s>
Probability, Distributions, and Summary Stats (part 1)
<strong>Chapter 6</strong>
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1VT6CTDgOVaskoVX3opTx5Qi2pmUHDCBQ/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec2.BasicProbability
</a>
<strong>(Self Study)</strong>
</td>
</tr>
<tr>
<th scope="row">3</th>
<td>Feb 3, Tu</td>
<td><s>Pandas (<strong>Chapter 4</strong>)</s>
Experiment Design (<strong>Chapter 3</strong>)
</td>
<td> <a href="http://www.stat.yale.edu/Courses/1997-98/101/expdes.htm">Experimentation</a></td>
<td>
<a href="https://docs.google.com/presentation/d/1MF4sWMMliKUJ-xsyeqLPF0oqH-JvrI3n/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec3.Experimental Design</a>
</td>
<td></td>
</tr>
<tr>
<th scope="row">4</th>
<td>Feb 5, Th</td>
<td> <s>SQL (<strong>Chapter 5</strong>)</s>
Pandas (<strong>Chapter 4</strong>)
</td>
<td></td>
<td><a href="https://docs.google.com/presentation/d/1l9mGXfXn83syisg9OAn_2jiN_YBC0RNq/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec4_Pandas</a></td>
<td>
HW 1 Out<br>
Final Project/Tutorial Instruction Out
</td>
</tr>
<tr>
<th scope="row">5</th>
<td>Feb 10, Tu</td>
<td> <s>Probability, Distributions, and Summary Stats (part 1)</s>
SQL (<strong>Chapter 5</strong>)
</td>
<td>
<a href="https://docs.google.com/presentation/d/1vIWsTgfI9d8EokzXry7ZJ76ueU2hvPOJop2sRwSAYHI/edit?usp=sharing">SQL_addional</a>
</td>
<td><a href="https://docs.google.com/presentation/d/1yMZ_GsD8wL4DKiUeTvcOczG6jmYFXD95/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec5_SQL</a>
</td>
<td></td>
</tr>
<tr>
<th scope="row">6</th>
<td>Feb 12, Th</td>
<td>Probability cont. (part 2) <strong>(Chapter 7)</strong></td>
<td>
<a href="https://towardsdatascience.com/clearly-explained-the-mighty-central-limit-theorem-b8152b94258">Central Limit Thm</a><br>
<a href="https://docs.google.com/document/d/1jGm4OSf2UMKjJ9M9ZVYTY4qt9KmXfbrdeDbA_titviA/edit?usp=sharing">Shared Description Material</a>
</td>
<td><a href="https://docs.google.com/presentation/d/1xhgfdhOX9rxCx5At8-7PitmONkIsjl7T/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec6_Summary_Statistics</a></td>
<td></td>
</tr>
<tr>
<th scope="row">7</th>
<td>Feb 17, Tu</td>
<td>Hypothesis Testing <strong>(Chapter 8)</strong></td>
<td>
<a href="https://vitalflux.com/data-science-how-to-formulate-hypothesis-for-hypothesis-testing/">Hypo Testing Steps and Examples</a><br>
<a href="https://towardsdatascience.com/what-is-p-value-370056b8244d">P-Value Explanation</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/121sfnXPB7g3vPakKQEhekkyYqYL9P6kq/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec7_HypothesisTesting_Part1</a>
<a href="https://docs.google.com/presentation/d/12fOa9LncMW9QxQyQHqYNg_4zSuyrTukD/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec7_HypothesisTesting_Part2</a>
<a href="https://docs.google.com/presentation/d/1ZYyYwtOKmsAhpsEHz9cVNBe4zfG_84MV/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec7_HypothesisTesting_Part3</a>
</td>
<td>HW 2 Out (Feb 18, Wed)</td>
</tr>
<tr>
<th scope="row">8</th>
<td>Feb 19, Th</td>
<td>Data Visualization</td>
<td></td>
<td><a href="https://docs.google.com/presentation/d/15QJpJy72RWJI2kfoA1OrTO5dPtTDw6IN/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec8_Data Visualization</a></a></td>
<td>HW 1 Due</td>
</tr>
<tr>
<th scope="row">9</th>
<td>Feb 24, Tu</td>
<td>Data Exploration <strong>(Chapter 9)</strong></td>
<td></td>
<td><a href="https://docs.google.com/presentation/d/1B4BRG0lAJpa7zoWYbSRsK81ItR94rw0v/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec9_Data Exploration</a></td>
<td></td>
</tr>
<tr>
<th scope="row">10</th>
<td>Feb 26, Th</td>
<td>Data Cleaning</td>
<td></td>
<td><a href="https://docs.google.com/presentation/d/1ucMOirOOqwTgLy6ML3xCyo7gcShMJdFc/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec10_DataCleaning_P1</a></td>
<td>
Project First Checkpoint Due<br>
HW 3 Out
</td>
</tr>
<!-- (content continues identically structured...) -->
<tr>
<th scope="row">11</th>
<td>Mar 03, Tu</td>
<td>Data Cleaning</td>
<td>
<a href="https://www.shiksha.com/online-courses/articles/handling-missing-data-mean-median-mode/">
HandlingMissingData
</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1qG0E65vu4BzgJZGST9rAyPUhbdmbJvt0/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec1DataCleaning_Part02</a>
</td>
<td></td>
</tr>
<tr>
<th scope="row">12</th>
<td>Mar 05, Th</td>
<td>Introduction to Machine Learning</td>
<td></td>
<td>
<a href="https://docs.google.com/presentation/d/1hW7-OePRoMdV13Z9K_rbM0xEjezWgwhX/edit?usp=sharing">Lec12_IntroToML</a>
</td>
<td>HW 2 Due</td>
</tr>
<tr>
<th scope="row">13</th>
<td><strong>Mar 10, Tu</strong></td>
<td><strong>Mid Exam I</strong></td>
<td></td>
<td>
<!-- Topic upto Class no. 11 (Data Cleaning) -->
</td>
<td></td>
</tr>
<tr>
<th scope="row">14</th>
<td>Mar 12, Th</td>
<td>Feature Engineering</td>
<td></td>
<td>
<a href="https://docs.google.com/presentation/d/1DlfLwobRaIlWkGJLLwqpRbGN6FaDlUqh/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec13_FeatureEngineering
</a>
</td>
<td></td>
</tr>
<tr>
<th scope="row">15</th>
<td><strong>Mar 17, Tu</strong></td>
<td><strong>Spring Break</strong></td>
<td></td>
<td>(No Class)</td>
<td></td>
</tr>
<tr>
<th scope="row">16</th>
<td><strong>Mar 19, Th</strong></td>
<td><strong>Spring Break</strong></td>
<td></td>
<td>(No Class)</td>
<td></td>
</tr>
<tr>
<th scope="row">17</th>
<td>Mar 24, Tu</td>
<td>ML Evaluation</td>
<td>
<a href="https://indhumathychelliah.com/2020/12/23/confusion-matrix%E2%80%8A-%E2%80%8Aclearly-explained/">
Confusion Matrix
</a><br>
<a href="https://youtu.be/fSytzGwwBVw?si=khWbup2E19JGFvQJ">
CrossValidationVideo
</a>
<a href="https://www.youtube.com/watch?v=kituDjzXwfE">
KFoldVideo
</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1PgzjeWi4SV9tKtomLS8waAfJTbPgsiPt/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec14_ML_Evaluation</a>
</td>
<td>HW4 Out, HW 3 Due</td>
</tr>
<tr>
<th scope="row">18</th>
<td>Mar 26, Th</td>
<td>Decision Tree</td>
<td>
<a href="https://victorzhou.com/blog/information-gain/?fbclid=IwAR0_tE6FkK7nGDXohbSEXxqFDKVotHhB0pJwob4OGbKfj-m8n-QOCDBI8M4">
DecisionTree-Calculate-Entropy-InfoGain
</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1mImlLeiTzA77eFmd5EC2YDFrX7Xj8bCI/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec15_DecisionTree
</a>
</td>
<td></td>
</tr>
<tr>
<th scope="row">19</th>
<td>Mar 31, Tu</td>
<td>Classifications</td>
<td>
Reading materials are given at end of the Classification lecture slide
</td>
<td>
<a href="https://docs.google.com/presentation/d/15W_Tr3b-K6F2L1-7qdqOXeM5O3PKIxIv/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lecc16_Classification</a>
</td>
<td>Project Second Checkpoint Due</td>
</tr>
<tr>
<th scope="row">20</th>
<td>Apr 2, Th</td>
<td>Regression (& Unsupervised Learning)</td>
<td>
<a href="https://drive.google.com/file/d/1wdRweYLoMjNF5kPgt7T0vV2xnqaBQm0N/view?usp=sharing">
Simple Linear Regression
</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1IULFrC8uyjgYFaT0iVY-CqGROByJDPHE/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec16Regression
</a>
</td>
<td>HW 5 Out</td>
</tr>
<tr>
<th scope="row">21</th>
<td>Apr 7, Th</td>
<td>Unsupervised Learning & Dimensionality Reduction</td>
<td>
<a href="https://docs.google.com/presentation/d/1DkqltXbHqYS6HDmm8HQfzfvNT2ni6ib3Lx_O-w3EmvA/edit?usp=sharing">
PCA_short_slides
</a>
</td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1MoMIjeiXyGiQ91l6A4GUA_o1HCzC8bXg/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec17.1_DimReduction
</a>
-->
<a href="https://docs.google.com/presentation/d/1ALzQebun1BeNkmDCw0tXDvAkIkKmjr-V/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec17.2_UnsupervisedLearning
</a>
</td>
<td></td>
</tr>
<tr>
<th scope="row">22</th>
<td>Apr 9, Th</td>
<td>Introduction to Neural Network</td>
<td></td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1kfP6KuBqQH3qVLj2mpka4CQ3Z5sMqzQL/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec18.1_IntroToNeuralNetwork
</a>
<a href="https://docs.google.com/presentation/d/1pDO_XYVKM5sDf1mwc0MRWsFXDdMqu78W/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
LEc18.2_NN_Training
</a>
-->
</td>
<td>HW 4 Due</td>
</tr>
<tr>
<th scope="row">23</th>
<td>Apr 14, Tu</td>
<td>Image Processing with CNN</td>
<td></td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1xgUivRXMnyKjeJxBOm0TdSd-MGcUJYuc/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec19CNN_ImageProcessing
</a>
-->
</td>
<td></td>
</tr>
<tr>
<th scope="row">24</th>
<td>Apr 16, Th</td>
<td>CNN continue and Intro to Natural Language Processing (NLP)</td>
<td></td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/13T8oLRdIg162BWYYOULSWBHHvm7gCbcP/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec20IntroToNLP
</a>
-->
</td>
<td></td>
</tr>
<tr>
<th scope="row">25</th>
<td>Apr 21, Tu</td>
<td>Intro to Natural Language Processing (NLP) cont.</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th scope="row">26</th>
<td><strong>Apr 23, Th</strong></td>
<td><strong>Mid Exam II</strong></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<th scope="row">27</th>
<td>Apr 28, Tu</td>
<td>Introduction to Graph Theory</td>
<td>
<a href="https://networkx.org/">NetworkX</a><br>
<a href="https://graphql.org/learn/">Intro to GraphQL</a><br>
These two materials are given for self-exploration if someone is interested.
</td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1UWScaSOGJ3wUw3CcLC0HmyN9Avb3JIAY/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec21_GraphTheory
</a>
-->
</td>
<td></td>
</tr>
<tr>
<th scope="row">28</th>
<td>April 30, Tu</td>
<td>Introduction to Graph Theory cont.</td>
<td>
<a href="https://drive.google.com/file/d/1UQsL6YxzbwcA_q6UDv_p0UislonKfbBt/view?usp=sharing">
ClassNote_Girvan-NewmanAlgorithm
</a><br>
<a href="https://cmsc320.github.io/2021fall/files/girvan-newman.pdf">
Girvan & Newman. "Community structure in social and biological networks," PNAS-02.
</a>
</td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1CQmpWH6xIdQOvxhw6TwfQXq2fKUrZATF/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec22_GraphTheory_Lecture2
</a>
-->
</td>
<td>HW 5 Due</td>
</tr>
<tr>
<th scope="row">29</th>
<td>May 5, Tu</td>
<td>Recommender System</td>
<td></td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1LS-0H0nxd991mVNf1JaHnPDYILaFtIjq/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec23_RecommendationSystem_Lecture01
</a>
-->
</td>
<td></td>
</tr>
<tr>
<th scope="row">30</th>
<td>May 7, Th</td>
<td>Recommender System cont. and Data Ethics (self reading)</td>
<td></td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1n-icgXxOa5sjo9U_03I8frZ-WCYclKf2/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec24_RecommendationSystem_Lecture02
</a>
<a href="https://docs.google.com/presentation/d/1kQmXAGiPB_TqGIFL8cKaQSoKzf4qMb5D/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">
Lec25DataEthics (SELF STUDY)
</a>
-->
</td>
<td>Project Final Checkpoint Due (May 8, Fri)</td>
</tr>
<tr>
<th scope="row"></th>
<td>Final Exam Week</td>
<td><strong>Final Exam</strong></td>
<td></td>
<td>
<strong>
Tentative: Friday, May 15 (4 p.m. – 6 p.m)<br>
Reference:
<a href="https://registrar.umd.edu/registration/register-classes/final-exams/spring">
https://registrar.umd.edu/registration/register-classes/final-exams/spring
</a>
</strong>
</td>
<td>
This date is subject to change based on the official UMD schedule. Until then, please treat this as the working date and plan your end-of-semester activities accordingly.
</td>
</tr>
</tbody>
</table>
</div>
<hr class="featurette-divider">
<!-- Deliverables Section -->
<div class="row marketing" id="assignments">
<h1>Deliverables</h1>
<h3>Assignments</h3>
<p>All the Assignments will be <strong>posted on Piazza/ ELMS.</strong> Instructions will appear over the course of the semester. Most assignments get released
one or two days before the lecture material gets presented and are due one or two weeks after that.
<p><strong>Weights:</strong> HW1 — 5%, HW2 — 5%, HW3 — 6%, HW4 — 7%, HW5 — 12% (Total 35%)</p>
<table class="table table-striped">
<thead>
<tr>
<th>#</th>
<th>Description</th>
<th>Date Released</th>
<th>Date Due</th>
<!-- <th>Project Link</th> -->
</tr>
</thead>
<tbody>
<tr>
<th>Homework 1</th>
<td>Git, Pandas, and SQL</td>
<td>Feb 5, Th</td>
<td>Feb 19, Th</td>
</tr>
<tr>
<th>Homework 2</th>
<td>Python & Statistics & Hypothesis Testing</td>
<td>Feb 18, Wed</td>
<td>Mar 5, Th</td>
</tr>
<tr>
<th>Homework 3</th>
<td>Data Exploration & Data Cleaning & Missing Data</td>
<td>Feb 26, Th</td>
<td>Mar 24, Tu</td>
</tr>
<tr>
<th>Homework 4</th>
<td>Machine Learning: Classification and Clustering </td>
<td>Mar 24, Tu</td>
<td>Apr 9, Th</td>
</tr>
<tr>
<th>Homework 5</th>
<td>Regression, Gradient Descent, Neural Network, CNN & NLP</td>
<td>Apr 2, Th</td>
<td>April 30, Th</td>
</tr>
<!-- <tr>
<th>Homework 6</th>
<td>NLP/GraphTheory/Recommendation System/CNN (any mix) </td>
<td>Nov 20</td>
<td>Dec <s>04</s> 08</td>
</tr> -->
</tbody>
</table>
<h3>Final Project/Tutorial</h3>
<p>There will be a group final project/tutorial with <strong>four to six (four recommended) persons</strong> in each group. Keep in mind that this is a semester-long group project/tutorial for this course, and you should strive to make it your best work. It will be graded to a higher standard than the rest of the homework, considering that you have had the opportunity to practice these skills beforehand. Project details will be <strong>posted on Piazza/ELMS. It will be <strong>your responsibility</strong> to select the project topic as well as your project partners.</strong></p>
<table class="table table-striped">
<thead>
<tr>
<th>#</th>
<th>Description</th>
<th>Date Released</th>
<th>Date Due</th>
</tr>
</thead>
<tbody>
<tr>
<th>Checkpoint 1</th>
<td>Group Formation and Choosing Dataset</td>
<td>Feb 5, Th</td>
<td>Feb 26, Th</td>
</tr>
<tr>
<tr>
<th>Checkpoint 2</th>
<td>Data preprocessing and Exploration</td>
<td>Feb 5, Th</td>
<td>Mar 31, Tu</td>
</tr>
<tr>
<th>Final Checkpoint </th>
<td>Final deliverable of DS Project</td>
<td>Feb 5, Th</td>
<td>May 8, Fri </strong>(<code>NO EXTENSION/LATE SUBMISSION IS ALLOWED)</code>)</strong></td>
</tr>
</tbody>
</table>