Course Syllabus

IRB 2107

Tue-Thu 12:30-1:45pm

Course Staff

Prof. Leilani Battle, Course Instructor
email: leilani [at] cs [dot] umd [dot] edu
office hours: 2pm-3pm on Thursdays, IRB 5148

UPDATE 08/29/2019: Leilani is traveling from Oct. 19 - Oct. 27

Zehua Zeng, Teaching Assistant
email: zzeng [at] cs [dot] umd [dot] edu
office hours: 10am-11am on Fridays, Fifth floor of IRB, next to room IRB 5137

UPDATE 08/29/2019: Zehua is traveling from Sept. 21 - Sept. 29

Piazza page:


Course Management

The class will be managed from ELMS. Please look here for updates and announcements throughout the semester.

Background Expectations

There are no official prerequisites for this course. Though not required, experience from an introductory database course (example: CMSC 424), HCI course (example: CMSC 434), or visualization course (examples: CMSC 734, UW CSE 442) will be extremely helpful in completing this course. Industry experience in these topics can also be very helpful.

Learning Goals

With the continued rise of big data analytics and data science, there is an increasing need for new tools to support rapid navigation (i.e., interaction) and interpretation of massive and complex datasets. This is a research-oriented graduate course, where the focus will be on studying (and hopefully developing) innovative techniques in the design of interactive data analysis systems. This area of research is interdisciplinary by nature, involving knowledge from data management (or databases), human-computer interaction (HCI), and information visualization. By taking this course, you should be able to read, understand and hopefully critique related research articles from both the systems perspective (e.g., interactive analytics papers in conferences like SIGMOD/VLDB/ICDE) and the HCI/Vis perspective (e.g., papers in conferences like CHI/InfoVis/VAST/EuroVis).

This course will emphasize not only system performance issues (e.g., data management issues), but also human performance issues (e.g., HCI/Vis issues). Given a new application domain, you should also be able to ask the right questions to understand the key performance issues, and be able to design/suggest appropriate solutions. You should also be able to identify flaws (if any) with a proposed design or solution.

You should also have enough familiarity with how interactive analytics systems are built to be able to easily start using any of them, and reason about the observed performance of a deployed system, if only superficially.

Activities, Learning Assessments & Expectations for Students

Readings: Students are expected to complete the readings in advance, and to come to lecture prepared to discuss the readings.  All papers should be accessible using the UMD network (in thanks to the UMD Libraries). To access the papers off campus, please use the campus VPN or CS VPN.

Lectures: Participation will not be graded in this course. If we do exercises in class, they are for your own benefit (e.g., to practice for the quiz). However, to get access to the lecture slides, you must come to class. Lecture slides will not be posted online. It is the responsibility of the student to contact the course staff at least one week in advance if they will be missing lecture, however note that quizzes will not be waived or postponed.

Weekly Online Quizzes: There will be a 60 minute online quiz on ELMS each week that covers the relevant readings and lectures for the corresponding week. Quizzes are to be completed individually, and can only be taken once. The expectation is that students who keep up with the lectures and readings should need less than an hour to complete the corresponding quiz each week. The quizzes are designed with two goals in mind: 1) to give you an opportunity to test your high-level understanding of the material (lectures and readings); and 2) to help you practice brainstorming and thinking critically about the assigned topics (from lecture or the readings). See the calendar for quiz deadlines. Again, it is the responsibility of the student to contact the course staff at least one week in advance if they will be missing lecture, however quizzes will not be waived or postponed. All students will be able to drop 1 quiz from their final grades.

Assignments: There will be 4 assignments to be completed individually, to help prepare you for the final project. These assignments are designed to give you experience in creating and evaluating data analysis tools. See the course calendar for assignment dates.

Final Project: A team-based final project is due at the end of the course, with periodic milestones throughout the semester. The goal of the final project is to identify a new, interesting and challenging interactive analysis systems problem, and to apply the techniques and skills learned in class to address this problem. Some suggestions for final projects will be made available from the course Website. Check the course calendar for final project milestone deadlines.

Campus Policies

It is our shared responsibility to know and abide by the University of Maryland’s policies that relate to all courses, which include topics like:

  • Academic integrity
  • Student and instructor conduct
  • Accessibility and accommodations
  • Attendance and excused absences
  • Grades and appeals
  • Copyright and intellectual property

Please visit for the Office of Undergraduate Studies’ full list of campus-wide policies and follow up with me if you have questions.

If there is ever a conflict with this syllabus and general university policies, the university policies should take precedence over our course-specific policies. Please reach out to the course staff as soon as possible, if you ever have questions or concerns about discrepancies between policies for this course and campus policies.

Course-Specific policies

Late days. You get five late days that can be used throughout the course, to be used in 24-hour blocks. You may use up to two late days on any of the assignments. Late days may not be used towards the quizzes or any final project milestone deadlines.
UPDATE: 10/09/2019: everyone now gets six late days, and can use up to three on A3 and A4
Any late submissions outside of the use of late days will not be graded (i.e., given a zero). If you ever have any concerns about submitting your work on time, please arrange to talk with the course staff in advance, preferably at office hours, or by appointment if necessary.
ELMS allows students to submit multiple times, feel free to do so. We will grade the latest submission that is made *before* the deadline.

No computers, phones or tablet devices are permitted during our class meetings. I understand and have considered arguments for permitting laptop and tablet computers in the classroom. However, in my experience (and based on the research evidence) the reality is that they present an irresistible distraction and detract from the cooperative learning environment. Researchers have found that these distractions do in fact interfere with learning and active participation. For that reason, the use of computers and phones will not be permitted during class meetings (except when required for ADS accommodations). If a computer is needed to accomplish a class objective for the day I will provide it or give you advanced notice to bring one with you.

I expect you to make the responsible and respectful decision to refrain from using your cellphone in class. If you have critical communication to attend to, please excuse yourself and return when you are ready. For more information about the science behind the policy watch: OpenPSYC - Digital Distractions

If you still choose to be on your phone, laptop, etc. in class anyway, you will get a zero for the corresponding quiz, and be asked to leave the lecture. Repeat offenders risk getting a zero for the final course grade.


The breakdown for grading is as follows:

Quizzes: 30% (the lowest quiz score is dropped)
Assignments: 25%
Final project: 35%

Participation: 10% (update 09/12/2019)

For all graded deliverables (quizzes, assignments, final project milestones), we grade in the following way:

  1. All submissions are given an initial (internal) score by the course staff.
  2. Submissions are then clustered into groups based on similar performance.
  3. The scores are augmented based on the clustering results, to improve grading outcomes, for example to ensure that grades are applied consistently and fairly across all students.

This grading scheme often results in five (but hopefully four) clusters, which we take under consideration for every graded deliverable:

  1. A to A+: the student shows initiative to not only do the bare minimum requested in the assignment, but also to show that they are thinking critically about what is being asked in the assignment and demonstrate creativity and originality in their work.
  2. B+ to A-: Solid performance, the submission covers the minimum asked in the assignment with few or no errors.
  3. C+ to B-: Submission lacks attention to assignment details, and demonstrates little critical thinking or creativity in the submission.
  4. C or lower: Something was submitted, but the submission demonstrates a clear lack of effort in completing the assignment.
  5. F (generally a score of zero): Assignment was not submitted, or was submitted late.

UPDATE 10/09/2019: The two lowest quiz scores will be dropped from final grades.

Course Evaluation

Course evaluations are important, and the department and faculty take student feedback seriously. Students can go to to complete their evaluations.

UPDATE 09/05/2019: You are also welcome to provide feedback at any time during the course at this link:

Basic Needs Security

If you have difficulty affording groceries or accessing sufficient food to eat every day, or lack a safe and stable place to live and believe this may affect your performance in this course, please visit for information about resources the campus offers you and let me know if I can help in any way.


Names/Pronouns and Self Identifications

The University of Maryland recognizes the importance of a diverse student body, and we are committed to fostering inclusive and equitable classroom environments. I invite you, if you wish, to tell us how you want to be referred to both in terms of your name and your pronouns (he/him, she/her, they/them, etc.). The pronouns someone indicates are not necessarily indicative of their gender identity. Visit to learn more.

Additionally, how you identify in terms of your gender, race, class, sexuality, religion, and dis/ability, among all aspects of your identity, is your choice whether to disclose (e.g., should it come up in classroom conversation about our experiences and perspectives) and should be self-identified, not presumed or imposed.  I will do my best to address and refer to all students accordingly, and I ask you to do the same for all of your fellow Terps.

Course Summary:

Date Details