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.

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 two written midterm examinations and one final.

Final grades will be calculated as:

• 40% homeworks
• 15% midterm #1
• 15% midterm #2
• 15% final
• 15% final project

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: morawski@umd.edu!

Office Hours & Communication

We are going to use a combination of in-person office hours, as long as those are a thing, and the Piazza forum (sign up here: https://piazza.com/umd/spring2023/cmsc3200201) 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. Note that Piazza is not appropriate for things like asking for accommodation or other such issues/concerns, please email your instructor (morawski@umd.edu) with [CMSC320] in the email subject line for those things.

As mentioned above: for private correspondence or special situations (e.g., excused absences, DDS accomodations, etc), please email your instructor (morawski@umd.edu) with [CMSC320] in the email subject line.

* All hours are EDT

Instructor	Time	Location
Maksym Morawski	IRBE 2232, M/W 11:00-1:00pm, F 11:00-1:00pm; please email (morawski@umd.edu) with [CMSC320] in the email subject line.	TBA

You can find TA office hours locations for all your courses here. Unless otherwise stated, CMSC320's assigned space is AVW 4122. If TAs are hosting hours virtually, you can find the zoom link on their names.
Do keep an eye on Piazza, though; TAs will sometimes swap hours, shift hours, host hours on Zoom, and so on! Additionally, we have at least one TA explicitly covering Piazza on each weekday; all course staff will float around Piazza in general, too!

Day	Time	TAs
Christabel Acquaye	M 9:00-10:00am, T 9:00-11:00am	AVW 4122
Eric Gorner	T/Th 2:00-3:30pm	AVW 4122
NaKyung Lee	M/W/Th 9:00-10:00am	AVW 4122
Aditya Parvate Patil	M/W/Th 3:30-4:30pm	online
Vikas Thoti Reddy	M 11:00-1:00pm, W 1:00-2:00pm	AVW 4122

Weekly View of in-person and online office hours:

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. The following policies (about masks, projects, group chats, and so on) are largely directly copied and pasted from standard campus and CMSC-specific guidance, so they should not come as a surprise to anyone.

Projects/Labs: On any graded project or lab, you are NOT allowed to exchange code. We compare each student's code with every other student's code to check for similarities. Every semester, we catch an embarrassingly high number of students that engage in cheating and we have to take them to the Honor Council.

GroupMe/Other Group Chats: We encourage students to talk about course material and help each other out in group chats. However, this does NOT include graded assignments. There have been a couple instances in the past where students have posted pictures/source files of their code, or earlier sections have given away exam questions to later sections. Not only did this lower the curve for the earlier section because the later one will do better, the WHOLE group chat had to pay a visit to the Honor Council. It was an extremely ugly business. Remember that in a group of 200+, someone or the other will blow the whistle. If you happen to be an innocent person in an innocent groupchat and someone starts cheating out of the blue, leave it immediately (and better yet, say you are leaving and say you will report it).

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!)

Topic	Date	Topic	Reading	Slides	Notes
1	1/25	Introduction What is Data Science		Lecture 1	Sign up on Piazza!
2	1/27	Background	Alternate Bayes Rule video Information Entropy	My notes	Topics covered: Correlation / Covariance Bayes Rule Expected Value Uncertainty Distributions
3	2/1	Experimental Design	The Control Group is Out of Control	Rough notes
4	2/3	What is Data?		Slides (work in progress)
5	2/6	Introduction to Python, Git, SQL and Pandas		Lecture Outline	Homework 1, Homework 2, and Homework 3 out, due 2/24.
6	2/8, 2/10	Data Visualization		Data vis powerpoint
7	2/13	Summary Stats		Notes
8	2/15,2/17	Data Exploration		In-Class Notebook, Misc Notes
9	2/20	Hypothesis Testing			Slides
10	2/22, 2/24	Feature Engineering		Notes
11	3/3	Review
13	3/6	Introduction to Modeling		Notes	Homework 4, due 3/17
12	3/8	Midterm 1
14	3/17, 3/27	Classification		Slides
15	3/29	Regression		Slides
16	3/31,4/3	Image Processing		Slides	Mandatory Viewing: Networks, Convolutional Networks
17	4/5, 4/7
18	4/10
19	4/12
20	4/14
21	4/17	Communication
22	4/19	Graphs		Slides
23	4/21	Time Series Data
24	4/25	Review			Final Project Out
24	4/27	Big Data
25	4/28	Midterm 2
26		Final Review

Homeworks

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

#	Description	Date Released	Date Due	Project Link
Homework 0		Jan 30th	Feb 8th	link
Project 0		Jan 30th	Feb 13th	link
Homework 1		Feb 7th	Feb 24th	link
Homework 2		Feb 7th	Feb 24th	link
Homework 3		Feb 7th	Feb 24th	link
Homework 4		March 4th	March 17th	link
Homework 5		4/14	4/28	link

Late Policy

Each students is provided with two 48-hour extensions for the mini-projects (i.e. not the quizzes, midterm exams, or final project). These extensions will be applied automatically to the first two late submissions (i.e. students do not choose which assignments use up their late allowance). No request (via email or otherwise) is necessary. After two late submissions, no late work will be accepted (i.e. any late work will be given a 0% and will not be graded).

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