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, in-class 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

Human	Time	Location
Nitin Balachandran	Monday 3PM-5PM	AVW 1120
Brian Brubach	Wednesday 10AM-12PM	AVW 1120
Mucong Ding	Friday 10AM-12PM	AVW 1120
John Dickerson	By appointment; please email John with [CMSC320] in the email subject line.	IRB 4128
Samuel Dooley	Tuesday and Thursday 2PM-3PM	AVW 1120
Saeed Hadadan	Monday 12PM-2PM	AVW 1120
Vishal Hundal	Monday 11AM-12PM	AVW 1120
Alexander Mendelsohn	Wednesday 4PM-6PM	AVW 1120
Vedant Nanda	Friday 12PM-2PM	AVW 1120
Aviva Prins	Thursday 8:30AM-10:30AM	AVW 1120
Candice Schumann	Monday 9AM-11AM	AVW 1120
Hanyu Wang	Wednesday 12PM-2PM	AVW 1120
Yukun Zheng	Tuesday and Thursday 3PM-4PM	AVW 1120

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	8/27	Introduction	What the Fox Knows.	pdf, pptx	Dickerson	Sign up on Piazza!
Part I: Data Collection, Storage, & Management
2	8/29	What is Data & Lightning Python Overview	Anaconda's Test Drive.	pdf, pptx	Dickerson
3	9/3	Scraping Data with Python	"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	9/5	NumPy & SciPy, & Best Practices	Introduction to pandas.	pdf, pptx	Dickerson	Pandas tutorials: link
5	9/10	Data Wrangling I: Pandas & Tidy Data	Hadley Wickham. "Tidy Data."	pdf, pptx	Dickerson	Hould's Tidy Data for Python
6	9/12	Data Wrangling II: Tidy data & SQL	Derman & Wilmott's "Financial Modelers' Manifesto."	pdf, pptx	Dickerson	SQLite: link; pandasql library: link
7	9/17	Version Control & Git		pdf, pptx	Schumann	Simons Institute
8	9/19	Missing Data	Pandas tutorial on working with missing data.	pdf, pptx	Dickerson	Scikit-learn's imputation functionality: link
9	9/24	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
10	9/26	Exploratory Data Analysis: Summary Statistics, Transformations, & Visualization	John W. Tukey: His Life and Professional Contributions.	pdf, pptx	Dickerson	Seaborn visualization library for Python: link
11	10/1	Data Wrangling Wrap-Up: Summary Statistics, Transformations, & Graphs	Introduction to GraphQL: link	pdf, pptx	Dickerson	NetworkX: link; Rosh Hashanah (9/29–10/1)
12	10/3	Project Day!	Rubric for final project: link	pdf, pptx	—	Simons Institute; No formal lecture held!
13	10/8	Graphs	—	pdf, pptx	Dickerson	Yom Kippur (10/8–10/9); GraphQL language: link
14	10/10	Natural Language I	NLTK Book.	pdf, pptx	Dickerson	Python Natural Language Toolkit (NLTK): link; Criticisms of the Turing Test: link
15	10/15	Natural Language II	Continued from last class ...	pdf, pptx	Dickerson	Continued from last class ...
16	10/17	Midterm Review & Introduction to Machine Learning I		Midterm review: pdf, pptx; Lecture slides: pdf, pptx	Dickerson	This is the last lecture with content that can be included on the midterm.
17	10/22	Automatically Learning how to Avoid Censorship	Bock et al. Geneva: Evolving Censorship Evasion Strategies.	—	Levin	INFORMS Annual Meeting
18	10/24	Midterm	—	—	Dickerson	Bring a pen!
19	10/29	Introduction to Machine Learning II	Hal Daumé III. A Course in Machine Learning.	pdf, pptx	Dickerson	Scikit-learn cheat sheet: link
20	10/31	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
21	11/5	Random Forests, K-NN	—	pdf, pptx	Dickerson
22	11/7	Practical Issues I: Overfitting, Cross-validation, Regularization		pdf, pptx	Dickerson	xkcd on overfitting: link
23	11/12	Practical Issues II: Feature Engineering, PCA, Clustering, Association Rules		pdf, pptx	Dickerson
24	11/14	Scaling It Up I	—	pdf, pptx	Dickerson
25	11/19	Scaling It Up II; Hypothesis Testing Recap; Data Science Ethics & Best Practices I	Clarifications about p-values.	pdf, pptx	Dickerson
26	11/21	Data Science Ethics & Best Practices II	The Atlantic. "Everything We Know About Facebook's Secret Mood Manipulation Experiment"	pdf, pptx	Dickerson	SIGCOMM paper that passed IRB review but is widely seen as unethical: link
27	11/26	Data Science Ethics & Best Practices III	Apple's brief overview of differential privacy:	pdf, pptx	Dickerson
—	11/28	Thanksgiving Break	—	—	—	—
28	12/3		Barocas, Hardt, & Narayanan. Fairness in Machine Learning.		Dickerson
29	12/5	Debugging Data Science, & Data Science in Industry		pdf, pptx	Dickerson	Last class :(.
Final	12/16	Final Exam Date	Final versions of tuturials 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	August 27	September 4	link
1	Fly Me To The Moon	September 10	September 25	link
2	Moneyball	September 27	October 16	link
3	Fact Tank	October 31	November 20 November 25	link
4	Baltimore Crime	November 21	December 6	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.