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.

There will be a weekly quiz on ELMS relating to the material from that week. Of these weekly quizzes (about 7), you must take 5. The lowest 2 will be dropped. Because only 5 are required, there is no need to worry if you miss a quiz! Just make sure that you take at least 5 of them over the whole term. If you take more than 5 of the quizzes, we will only count the 5 best scores.

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: jmct@umd.edu!

Office Hours & Communication

We are going to use Discord as a replacement for the physical space that we don't have access to during COVID. This means that it's appropriate to use Discord for office hours and or short high-bandwidth discussions that would usually take place before/after class. Note that Discord is not appropriate for things like asking for accommodation or other such issues/concerns, please email José (jmct@umd.edu) with [CMSC320] in the email subject line.for those things.

Discord, while useful, can be very 'stream of conscious' and does not allows for threading when several students have the same concern. Therefore for course-related questions, please use Piazza. As mentioned above: for private correspondence or special situations (e.g., excused absences, DDS accomodations, etc), please email José (jmct@umd.edu) with [CMSC320] in the email subject line.

Office Hours (all times EDT)

Human	Time	Location
Anubhav	MTuWThF: 11:30-13:00 EDT	Online
José Manuel Calderón Trilla	By appointment; please email José (jmct@umd.edu) with [CMSC320] in the email subject line.	Zoom
Julian Vanecek	MTuWThF: 16:00-17:30 EDT	Online

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	06/01	Introduction	What the Fox Knows.	pdf	Calderón Trilla	Sign up on Piazza!
2	06/02	What is Data & Lightning Python Overview	Anaconda's Test Drive.	pdf, pptx	Calderón Trilla
3	06/03	Scraping Data (with Python) I	"What happens when you type google.com into your browser's address bar?"	pdf	Calderón Trilla
4	06/04	Scraping Data (with Python) II	Think about data link	pdf	Calderón Trilla
4	06/07	Scraping Data (with Python) III	Think about data (extended edition!) link	pdf, notebook	Calderón Trilla	Example APIs: WMATA API, NOAA API, Library Docs: Requests, JSON, Beautiful Soup
5	06/08	NumPy & SciPy, & Best Practices	Introduction to pandas.	pdf, simple web-scrapping notebook, numpy notebook	Calderón Trilla	Pandas tutorials: link
6	06/09	Data Wrangling I: Pandas & Tidy Data	Hadley Wickham. "Tidy Data."	pdf, Notebook from class	Calderón Trilla	Hould's Tidy Data for Python
7	06/10	Data Wrangling II: Tidy data & SQL	Python SQLite Tutorial — The Ultimate Guide	pdf, web2pandas Notebook	Calderón Trilla	SQLite: link; pandasql library: link
8	06/11	Git demo	Introduction to GraphQL: link	No slides, see the lecture	Calderón Trilla	NetworkX: link
9	06/14	Graphs	Backstrom & Kleinberg. "Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook," CSCW-14. arXiv link.	pdf	Calderón Trilla
10	06/15	Summary Statistics and Transformations		pdf, .ipynb	Calderón Trilla
11	06/16	Missing Data I	Pandas tutorial on working with missing data.	pdf	Calderón Trilla	Scikit-learn's imputation functionality: link
12	06/17	Missing Data II, & 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	Calderón Trilla	Wikipdia article on outliers
14	06/18	Juneteenth	—	—	Nobody	Celebrate Emancipation and reflect on how we can continue to improve
13	06/21	Missing Data III: Syntax & Semantics	NLTK Book.	pdf, .ipynb	Calderón Trilla	Python Natural Language Toolkit (NLTK): link; Criticisms of the Turing Test: link
16	06/22	Natural Language I	NLTK Book.	pdf	Calderón Trilla	Python Natural Language Toolkit (NLTK): link; Criticisms of the Turing Test: link, Previous Semester's exam link
17	06/23	Natural Language II	Continued from last class ...	pdf	Calderón Trilla	Continued from last class ...
18	06/24	Midterm Review & TBD	—	Midterm review	Calderón Trilla	New material from this lecture will not be included on the midterm.
19	06/25	Midterm	—	—	Calderón Trilla
20	06/28	Dicussion of Final project (Looking toward the rest of the term)		Final Tutorial Instructions+Rubric	Calderón Trilla
21	06/29	Introduction to Machine Learning	Hal Daumé III. A Course in Machine Learning.	pdf	Calderón Trilla
22	06/30	Introduction to Machine Learning II	Russell & Norvig's Chapter 18 lecture slides:	pdf	Calderón Trilla	Scikit-learn's basic decision tree functionality: link; Bart Selman's CS4700: link
23	07/01	Introduction to Machine Learning III	—	notebook	Calderón Trilla
24	07/02	Introduction to Machine Learning (cont.)		pdf	Calderón Trilla	xkcd on overfitting: link; Polynomial features/Interaction terms in Scikit: link
14	07/05	Observation of Independence Day	—	—	Nobody	BBQ? Cookout? Let me know if I'm invited.
25	07/06	Introduction to Machine Learning (cont.)	Nguyen & Holmes. "Ten quick tips for effective dimensionality reduction," PLoS Computational Biology.	pdf	Calderón Trilla	Wikipedia article on the confusion matrix: link
26	07/07	Introduction to Machine Learning (cont.)	Best Practices for Recommender Systems (from Microsoft).	pdf	Calderón Trilla
27	07/08	Scaling It Up (Cancelled, make up lecture will go online)	Dean & Ghemawat. "MapReduce: Simplified Data Processing on Large Clusters," CACM.	pdf	Calderón Trilla	Wikipedia on SGD: link Using AWK and R to parse 25tb: link Command-line Tools can be 235x Faster than your Hadoop Cluster link
28	07/09	Introduction to Machine Learning (cont.)		pdf	Calderón Trilla
29	07/12	Non-linear Regressions, SVM, k-Means		pdf	Calderón Trilla
29	07/13	Dimensionality Reduction and Collaborative Filtering		pdf	Calderón Trilla
29	07/14	Scaling and Data Science Ethics	The Atlantic. "Everything We Know About Facebook's Secret Mood Manipulation Experiment" Apple's brief overview of differential privacy: ; Barocas, Hardt, & Narayanan. Fairness in Machine Learning.	scaling pdf, ethics pdf	Calderón Trilla	What is GDPR? (link), A SIGCOMM paper that passed IRB review but is widely seen as unethical: link
30	07/14	Debugging Data Science, & Data Science in Industry		pdf, pptx	Calderón Trilla	Additional discussion of debugging models (from Cornell): link
Final	07/21	Final Project Due Date	Final versions of tutorials must be posted by 5:00PM			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	June 2	June 4	link
1	Solar Power	June 10	June 21	link
2	Moneyball	June 29	July 6	link
3	Gap Minder	July 9	July 15	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 Summer 2021 version of CMSC320.

Most links lead to a public GitHub Page created by a student or small group in the Summer 2021 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 José!

Project Title	URL

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.

Miscellaneous Resources

As we go through the course sometimes I will mention additional resources or next steps. None of this is required for the course, but students have asked for me to keep a record of which texts/websites I mention.

• Python for Data Analysis covers some of the same topics that we cover in this class, but in textbook form.
• Data Science From Scratch this book is meant to give you a grounding in how some of the statistics and math could be implemented. Mostly, data scientists use off-the-shelf libraries for their mathematical routines/functionality, this books works through how you would implement some of those libraries. The idea is that this might give you a deeper understanding of what is going on. The implementations in this book are not meant to be high-performance, or industrial strength, but illustrative.
• Web Scraping 101 is a good overview to how web scraping works.
• Previous iterations of this course can be found for the Fall 2019 and Fall 2020 semesters.