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 and be reasonably mathematically mature. I understand that the class makeup will be diverse in terms of both academic and work experience—please talk to me about any worries or issues! 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 a bit of machine learning (although the bulk of that will be covered in CMSC643) and may make use of basic calculus and basic linear algebra; light mathematical maturity is preferred at roughly the level of a junior CS/Math student.

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
• 60% mini-project assignments
• 30% final tutorial to be posted publicly online (instructions, subject to change!)
• 5% course, Piazza, and office hours participation (extra credit!)

You can earn full credit for class participation in three ways:

1. Lecture participation, asking questions and answering your peers questions;
2. Piazza participation, asking and answering questions on Piazza; and
3. Regular attendance at office hours.

To earn full credit you should aim to ask or answer a question at least once every two weeks in lecture or on Piazza; or attend office hours at least once a month (this can include just going to my office hours to chat about computer science, data, science, software engineering, etc.).

This course 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 [CMSC641] in the email subject line.

Office Hours

Human	Time	Location
John Dickerson	6–7pm on Wednesdays (i.e., right before lecture). Also by appointment; please email John with [CMSC641] in the email subject line.	AVW 3217

University Policies and Resources

Policies relevant to Graduate Courses are found here: https://gradschool.umd.edu/policies, while those 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	8/29	Introduction	What the Fox Knows.	pdf, pptx	Dickerson	Sign up on Piazza!
2	9/5	Scraping Data with Python	Anaconda's Test Drive.	pdf, pptx	Dickerson	PDF download script from class: link
3	9/12	NumPy, SciPy, & DataFrames	Introduction to pandas.	pdf, pptx	Dickerson
4	9/19	Data Wrangling I & II: Pandas, Tidy Data, & SQL	Hadley Wickham. "Tidy Data." ; Derman & Wilmott's "Financial Modelers' Manifesto."	pdf, pptx	Dickerson	Hould's Tidy Data for Python; SQLite: link; pandasql library: link
5	9/26	Missing Data	Pandas tutorial on working with missing data.	pdf, pptx	Dickerson	Scikit-learn's imputation functionality: link
6	10/3	Data Wrangling Wrap-Up: Data Integration, Data Warehousing, Entity Resolution; & EDA I: Summary Statistics, Transformations	Data Cleaning: Problems and Current Approaches (Note: this is a reference piece; please don't read the whole thing!); John W. Tukey: His Life and Professional Contributions.	pdf, pptx	Dickerson	Wikipdia article on outliers
7	10/10	Graphs	—	pdf, pptx	Dickerson	GraphQL language: link
8	10/17	Graphs, & Natural Language	NLTK Book.	pdf, pptx	Dickerson
9	10/24	Natural Language, & Visualization	NLTK Book.	pdf, pptx	Dickerson	Python Natural Language Toolkit (NLTK): link; Criticisms of the Turing Test: link
10	10/31	Visualization	Edward R. Tufte. The Visual Display of Quantitative Information (examples.)	pdf, pptx	Dickerson	Seaborn visualization library for Python: link
11	11/7	Hypothesis Testing; Data Science Ethics & Best Practices I	—	pdf, pptx	Dickerson	Got through slide 43 ...
12	11/14	Data Science Ethics & Best Practices II	The Atlantic. "Everything We Know About Facebook's Secret Mood Manipulation Experiment"	pdf, pptx	Dickerson	Got through slide 110 ...; SIGCOMM paper that passed IRB review but is widely seen as unethical: link
—	11/21	Thanksgiving Break	—	—	—	—
13	11/28	Data Science Ethics & Best Practices III	Apple's brief overview of differential privacy:	pdf, pptx	Dickerson
14	12/5	Class Presentations & Wrap-Up	—	pdf, pptx	Everyone!
Final	12/14	Final Exam Date	Final versions of tuturials must be posted by 11:59PM.			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 7	link
1	Fly Me To The Moon	September 12	October 3	link
2	Moneyball	October 3	October 24	link
3	Baltimore Crime	October 28	November 21	link