Description of Course

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—just ask Nate Silver!

This course focuses on (i) data management systems, (i) exploratory and statistical data analysis, (ii) 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 that will strengthen course participants' data science portfolios.

This course will consist primarily of sets of self-contained lectures and assignments that leverage real-world data science platforms when needed; as such, there is no assigned textbook. Each lecture will come with links to required reading, which should be done before that lecture, and (when appropriate) a list of links to other resources on the web.

Requirements

Students enrolled in the course should be comfortable with programming (for those at UMD, having passed CMSC216 will be good enough!) and be reasonably mathematically mature. The course itself will make heavy use of the Python scripting language by way of Jupyter Notebooks, leaning on the Anaconda package manager; we'll give some Python-for-data-science primer lectures early on, so don't worry if you haven't used Python before. Later lectures will delve into statistics and machine learning and may make use of basic calculus and basic linear algebra; light mathematical maturity is preferred at roughly the level of a junior CS student.

There will be one written, take-home (obviously, given COVID-19 and all) midterm examination. There will not be a final examination; rather, in the interest of building students' public portfolios, and in the spirit of "learning by doing", students will create a self-contained online tutorial to be posted publicly. This tutorial can be created individually or in a small group. As described here (subject to change!), the tutorial will be a publicly-accessible website that provides an end-to-end walkthrough of identifying and scraping a specific data source, performing some exploratory analysis, and providing some sort of managerial or operational insight from that data.

Final grades will be calculated as:

• 10% weekly quizzes
• 25% midterm
• 40% mini-project assignments
• 25% final tutorial to be posted publicly online (instructions, subject to change!)

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: john@cs.umd.edu!

Office Hours & Communication

For course-related questions, please use Piazza to communicate with your fellow students, the TAs, and the course instructors. For private correspondance or special situations (e.g., excused absences, DDS accomodations, etc), please email John with [CMSC320] in the email subject line.

Office Hours (all times EDT)

Human	Time	Location
Sweta Agrawal	10AM-11AM Tuesday; Piazza on Wednesday	Check ELMS/Piazza
Nitin Balachandran	3PM-5PM Monday; Piazza on Monday	Check ELMS/Piazza
Tracy Chen	2PM-4PM Thursday; Piazza on Thursday	Check ELMS/Piazza
John Dickerson	By appointment; please email John with [CMSC320] in the email subject line.	Zoom
Aviva Prina	12PM-2PM Tuesday; Piazza on Tuesday	Check ELMS/Piazza
Noor Singh	4:00-6:00PM on Wednesday; Piazza on Friday	Check ELMS/Piazza
Qingyang Tan	2PM-3PM on Friday; Piazza on Tuesday	Check ELMS/Piazza

University Policies and Resources

Policies relevant to Undergraduate Courses are found here: http://ugst.umd.edu/courserelatedpolicies.html. Topics that are addressed in these various policies include academic integrity, student and instructor conduct, accessibility and accommodations, attendance and excused absences, grades and appeals, copyright and intellectual property.

Course evaluations

Course evaluations are important and the department and faculty take student feedback seriously. Near the end of the semester, students can go to http://www.courseevalum.umd.edu to complete their evaluations.

Schedule

(Schedule subject to change as the semester progresses!)

#	Date	Topic	Reading	Slides	Lecturer	Notes
1	9/1	Introduction	What the Fox Knows.	pdf, pptx	Dickerson	Sign up on Piazza!
2	9/3	What is Data & Lightning Python Overview	Anaconda's Test Drive.	pdf, pptx	Dickerson
3	9/8	Scraping Data (with Python) I	"What happens when you type google.com into your browser's address bar?"	pdf, pptx	Dickerson	PDF download script from class: link; Extra reading/quick tutorial on using BeautifulSoup: link
4	9/10	Scraping Data (with Python) II		pdf, pptx	Dickerson	Regex helper sites: regexr.com, pythex.org, regex101.com, rubular.com (thanks to J Helperin, J Martinez, M Mohades, & R Amor)
5	9/15	NumPy & SciPy, & Best Practices	Introduction to pandas.	pdf, pptx	Dickerson	Pandas tutorials: link
6	9/17	Data Wrangling I: Pandas & Tidy Data	Hadley Wickham. "Tidy Data."	pdf, pptx	Dickerson	Hould's Tidy Data for Python
7	9/22	Data Wrangling II: Tidy data & SQL	Derman & Wilmott's "Financial Modelers' Manifesto."	pdf, pptx	Dickerson	SQLite: link; pandasql library: link
8	9/24	Version Control & Git		pdf, pptx	Dickerson
9	9/29	Version Conrol Wrap-up, & Graphs	Introduction to GraphQL: link	pdf, pptx	Dickerson	NetworkX: link
10	10/1	Graphs, & Summary Statistics and Transformations	Backstrom & Kleinberg. "Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook," CSCW-14. arXiv link.	pdf, pptx	Dickerson
11	10/6	Summary Statistics and Transformations, & Missing Data I		pdf, pptx	Dickerson
12	10/8	Missing Data II	Pandas tutorial on working with missing data.	pdf, pptx	Dickerson	Scikit-learn's imputation functionality: link
13	10/13	Missing Data III, & Data Wrangling Wrap-Up: Data Integration, Data Warehousing, Entity Resolution	Data Cleaning: Problems and Current Approaches (Note: this is a reference piece; please don't read the whole thing!)	pdf, pptx	Dickerson	Wikipdia article on outliers
14	10/15	Natural Language I: Syntax & Semantics	NLTK Book.	pdf, pptx	Dickerson	Python Natural Language Toolkit (NLTK): link; Criticisms of the Turing Test: link
15	10/20	Natural Language II: Representation	Continued from last class ...	pdf, pptx	Dickerson	Continued from last class ...
16	10/22	Natural Language III: Embeddings & Similarity	Continued from last class ...	pdf, pptx	Dickerson	Pre-recorded class; John will not be available during the live lecture period for this class.
17	10/27	Midterm Review & TBD	—	Midterm review: pdf, pptx; Lecture slides: pdf, pptx	Dickerson	New material from this lecture will not be included on the midterm.
18	10/29	Midterm	—	—	Dickerson
19	11/3	Vote!	—	—	Dickerson	Election day!
20	11/5	Introduction to Machine Learning	Hal Daumé III. A Course in Machine Learning.	pdf, pptx	Dickerson
21	11/10	Decision Trees and Random Forests	Russell & Norvig's Chapter 18 lecture slides:	pdf, pptx	Dickerson	Scikit-learn's basic decision tree functionality: link; Bart Selman's CS4700: link
22	11/12	Random Forests, K-NN	—	pdf, pptx	Dickerson
23	11/17	Practical Issues I: Overfitting, Cross-validation, Regularization		pdf, pptx	Dickerson	xkcd on overfitting: link; Polynomial features/Interaction terms in Scikit: link
24	11/19	Practical Issues II: Feature Engineering, PCA, Clustering, Association Rules	Nguyen & Holmes. "Ten quick tips for effective dimensionality reduction," PLoS Computational Biology.	pdf, pptx	Dickerson	Wikipedia article on the confusion matrix: link
25	11/24	Practical Issues III: Recommender Systems and Association Rules	Best Practices for Recommender Systems (from Microsoft).	pdf, pptx	Dickerson
—	11/26	Thanksgiving Break	—	—	—	—
26	12/1	Scaling It Up	Dean & Ghemawat. "MapReduce: Simplified Data Processing on Large Clusters," CACM.	pdf, pptx	Dickerson	Wikipedia on SGD: link
27	12/3	Data Science Ethics & Best Practices I	The Atlantic. "Everything We Know About Facebook's Secret Mood Manipulation Experiment"	pdf, pptx	Dickerson	What is GDPR? (link)
28	12/8	Data Science Ethics & Best Practices II	Apple's brief overview of differential privacy: ; Barocas, Hardt, & Narayanan. Fairness in Machine Learning.	pdf, pptx	Dickerson	SIGCOMM paper that passed IRB review but is widely seen as unethical: link
29	12/10	Debugging Data Science, & Data Science in Industry		pdf, pptx	Dickerson	Additional discussion of debugging models (from Cornell): link
Final	12/21	Final Exam Date	Final versions of tutorials must be posted by 4:00PM, the exam time.			Instructions & rubric: link

Mini-Projects né Homework

In addition to the tutorial to be posted publicly at the end of the semester, there will be four "mini-projects" assigned over the course of the semester (plus one simple setup assignment that will walk you through using git, Docker, and Jupyter). The best way to learn is by doing, so these will largely be applied assignments that provide hands-on experience with the basic skills a data scientist needs in industry.

Posting solutions publicly online without the staff's express consent is a direct violation of our academic integrity policy. Late assignments will not be accepted.

(Assignments will appear over the course of the semester.)

#	Description	Date Released	Date Due	Project Link
0	Setting Things Up	September 1	September 8	link
1	Fly Me To The Moon	September 15	September 29	link
2	Moneyball	October 2	October 19 October 22	link
3	Fact Tank	November 6	November 23 December 3	link
4	Baltimore Crime	December 3	December 10	link

Final Tutorials

In the spirit of "learning by doing," students created a self-contained online tutorial to be posted publicly. Tutorials could be created individually or in a small group. The intention was to create a publicly-accessible website that provides an end-to-end walkthrough of identifying and scraping a specific data source, performing some exploratory analysis, and providing some sort of managerial or operational insight from that data. Below is a list of (most of) the tutorials created in the Fall 2020 version of CMSC320.

Most links lead to a public GitHub Page created by a student or small group in the Fall 2020 CMSC320 course; some links lead to students' personal websites or to a Notebook hosted on Google Colab. Project creators: if a link is missing or incorrect, please get in touch with John!

Project Title	URL
2020 Presidential Election: From a Data Science Perspective	link
A Closer look at the NFL Draft	link
A Data Science Walkthrough Using Global Happiness Data	link
A Data Scientist's Guide to the S&P 500	link
A March Madness Analysis	link
A Pandemic: An Analysis of COVID-19	link
American Music Awards Tweets	link
An Analysis of Amazon's Top 50 Bestselling Books	link
An Analysis of Heart Diseases and Attributes Leading to Heart Disease	link
An Analysis of Metrics in Predicting Economic Performance based on the Modern Portfolio Theory	link
An Analysis of Salaries and Cost of Living in Different US Cities	—
An Analysis of the Impact of COVID-19 on Crime in College Park, MD	link
An Analysis of the Prevalence of US Events on Reddit	link
An Introduction to Genome Analysis in Python (Data Science Tutorial)	link
Analysis of Amazon's Top 50 Bestselling Books	link
Analysis of Book Data from Amazon	link
Analysis of COVID Data and Politicial Outcomes for the United States	link
Analysis of COVID-19 data in United States	link
Analysis of Crime in Maryland	link
Analysis of Homelessness in Maryland	link
Analysis of NFL Games	link
Analysis of the Coronavirus by Coninent	link
Analysis of the Covid-19 Pandemic	link
Analysis of the Google Play Store	link
Analysis of Tournament Matches in Super Smash Bros. Melee	link
Analysis of Traffic Violations in Montgomery County Maryland	link
Analysis on the Potential of Life on Exoplanets	link
Analysis on Voter Turnout Data from 2020 General Election	link
Analysis San Francisco Criminal Records (CMSC320 Final Project)	link
Analyze 2020 Election Data	link
Analyzing Avocado Prices and Consumption in the U.S.	link
Analyzing Football Clubs in the U.K.	link
Analyzing Global Suicide Rate from 1985 to 2016	link
Analyzing Retail Investors with Robinhood Data	link
Analyzing the Prices of Boston Airbnb Rentals: What Affects Prices and Have Prices Changed Since the Pandemic?	link
Analyzing the relationship between home matches and match wins in the English Premier League (Soccer)	link
Analyzing the Top Spotify Songs of the 2010s	link
Aspects of Trending Videos on YouTube	link
Attempting to predict the outcome of a hit baseball	link
Black Lives Matter movement	link
Breaking down the Grammy Award for Record of the Year - An Analysis	link
BREAKING DOWN THE TOP TRENDING YOUTUBE VIDEOS (U.S. & CA)	link
Chicago Burglaries	link
Citi Bike Ridership & Public Safety During COVID-19	link
Classifying Pokemon Competitively	link
CMSC320 Final Project - Spotify Data Analysis	link
CMSC320 Final Project: Steven Struglia, Michael Strobel, HtetMyat Aung (Stock Market)	link
CMSC320 Final Tutorial	link
Cooking Recipes: An analysis of Ratings, Nutrition, and Tags	link
Coronavirus Exploratory Data Analysis	—
Countering a Dangerous Problem	link
COVID-19, An Analysis	link
COVID-19: Modeling The Relative Impact on US States	link
COVID-19's effect on Twitch & which games are the best to stream	link
COVID's Effect on Music Trends	link
COVID19 and state demographics: Finding which factors might affect COVID19 rates	link
Critical and Commercial Success in Music of the 2010 Decade	link
Data Analysis on FAANG Stocks from 2013 to 2020	link
Data Visualization and Analysis of COVID-19	link
Determining Buzz Words on Reddit	link
Do Masks Help in the Prevention of Covid-19?	link
Do Professional Wine Reviewers Know What They're Doing?	link
Drink To Forget: An Analysis of Drinking Habits During the COVID-19 Pandemic	link
Evaluating Chess Positions	link
Expected Value, Win Probability, and why "Common Knowledge" Hurts Sports Teams	link
Film Genre and Popularity Trends	link
Final Tutorial	link
Final Tutorial	link
Finding Your Ideal Wine	link
Formula 1: A brief look through history	link
Formula One Racing	link
From 2016 to 2020, How Politics Have Changed In America	link
Get a formula to predict the sale price of houses in Ames, Iowa	link
Global Food Waste Analysis	link
Gold Prices: Driven by Inflation, Volatility, or Treasury Yields?	link
Happiness in the World	link
Happiness Within Countries	link
Hospital Wait Times in The U.S.	link
How Corona Started	link
How Happy is Our World?	link
How has Music Changed over Time - A Spotify Data Analysis	link
How Height has effected Win % in 1980 and 2020	link
How much better can the 2019 NBA Draft class get?	link
How to Beat Better Rated Chess Players	link
How to Make a Successful Game on Steam?	link
How well do our police departments represent the populations they serve?	link
Individual and Comparative Analysis of Pop, Hip Hop, and Rock Song Structures	link
Is College Tuition Infected? Diagnosing Baumol's Cost Disease	link
Is Joe Flacco an Elite Quarterback?	link
Is The Electoral College Misleading?	—
Leicester's Unprecedented Title Win	link
Mental Health in the Tech Industry	link
Missing Migrants - An Analysis on the Risk of Seeking Asylum	link
Movie Genre Popularity and Economic Activity	link
Music attributes and its effect on popularity	link
Music Over the Decades	link
Music Throughout the Decades: An Analysis	link
My Brother, My Brother and Me and the McElroy Brand	link
NBA 2020 Season Statistics Analysis	—
NBA Project	link
Netflix Movie/Tv Show Trends	link
NTSB Investigations of Aircraft Incursions in the USA	link
Pokemon Type Analysis	link
Predicting 2020-2021 English Premier League Table Results Using Machine Learning	link
Predicting an MLB Player's Performance In Fantasy Baseball	link
Predicting Average Salaries for all Proffessor Ranks	link
Predicting Car Prices	link
Predicting Chess Wins based off Openings	link
Predicting Current Quarterback Win Rates	link
Predicting Dementia and Alzheimer's	link
Predicting Gaon Digital Streams through Spotify Audio Features	link
Predicting Gross Domestic Investment in the United States	link
Predicting House Prices in King County, Washington	link
Predicting NBA Players' Salaries	link
Predicting Student Performance In High School	link
Predicting the Chance of Winning in League of Legends (Given the First 10 Minutes of Data)	link
Predicting the Popularity of a Book on Project Gutenberg	link
Predicting Winning Play Styles in Texas Hold'em	link
Predicting Wins at the Highest Level in the NBA	link
Prediction of Diabetes Melitus from Patients Medical Records	link
Predictive Power of 3-Pointer for Team Win% in 21st Century NBA	link
Presidential Election 2020 Voter Turnout Analysis	link
Relationships Among Crime Rate, Gini Coefficient, and Median Income in US	link
Reverse Line Movement	link
Small Ball	link
Smart, not fair: An analysis of CS:GO metagame tactics	link
Testing a stock buying strategy versus buying and holding a stock	link
The Best Times Of The Year To Puchase Tech Stocks	link
The Evolution of the 3 point shot	link
The Future of Console Gaming in a PC World	link
The NBA Draft	link
The Probabilities and Financial Impact of Gacha Games	link
The Trends of Happiness	link
Trends in Seattle Crimes	link
Twitter's Climate Tide: An Analysis of Tweets About Climate Change	link
Ultimate Fighter Championship Data Analysis	link
Using Data Analysis & Visualization to Understand the Performance of NCAA Division 1 College Basketball Teams in March Madness	link
Venmo Transactions Analysis	link
Video Game Sales	link
Visualization and Analysis of Liquor Sales in Iowa	link
Visualizing Ocean Data on Reconstructed pH and Coral Bleaching Reports	link
Wall Street Bets Sentiment Analysis	link
What Determines A Soccer Player's Salary?	link
What Factors Help Predict the Outcome of the 2020 Election?	link
What Make You Happy?	link
What makes an ArchiveOfOurOwn story successful?	link
What Review Scores Mean for Games	link
What's the movie score	link
Will You Accept This Analysis?	link
Winter is Coming: An Analysis of Sunshine vs Depression	link
World Happiness	link

Additional Administrative Information

Excused Absences

Missing an exam for reasons such as illness, religious observance, participation in required university activities, or family or personal emergency (such as a serious automobile accident or close relative’s funeral) will be excused so long as the absence is requested in writing at least 2 days in advance and the student includes documentation that shows the absence qualifies as excused; a self-signed note is not sufficient as exams are Major Scheduled Grading Events. For this class, such events are the final project assessment and midterms, which will be due on the dates listed in the schedule above. The final exam is scheduled according to the University Registrar.

For medical absences, you must furnish documentation from the health care professional who treated you. This documentation must verify dates of treatment and indicate the timeframe that the student was unable to meet academic responsibilities. In addition, it must contain the name and phone number of the medical service provider to be used if verification is needed. No diagnostic information will ever be requested. Note that simply being seen by a health care professional does not constitute an excused absence; it must be clear that you were unable to perform your academic duties.

It is the University’s policy to provide accommodations for students with religious observances conflicting with exams, but it is the your responsibility to inform the instructor in advance of intended religious observances. If you have a conflict with a planned exam, you must inform the instructor prior to the end of the first two weeks of the class.

The policies for excused absences do not apply to project assignments. Projects will be assigned with sufficient time to be completed by students who have a reasonable understanding of the necessary material and begin promptly. In cases of extremely serious documented illness of lengthy duration or other protracted, severe emergency situations, the instructor may consider extensions on project assignments, depending upon the specific circumstances.

Besides the policies in this syllabus, the University’s policies apply during the semester. Various policies that may be relevant appear in the Undergraduate Catalog.

If you experience difficulty during the semester keeping up with the academic demands of your courses, you may consider contacting the Learning Assistance Service in 2201 Shoemaker Building at (301) 314-7693. Their educational counselors can help with time management issues, reading, note-taking, and exam preparation skills.

Right to Change Information

Although every effort has been made to be complete and accurate, unforeseen circumstances arising during the semester could require the adjustment of any material given here. Consequently, given due notice to students, the instructors reserve the right to change any information on this syllabus or in other course materials. Such changes will be announced and prominently displayed at the top of the syllabus.

University of Maryland Policies for Undergraduate Students

Please read the university’s guide on Course Related Policies, which provides you with resources and information relevant to your participation in a UMD course.