Syllabus


Time and Location

Lectures: Tuesday 1:30PM-2:45PM; Thursday 1:30PM-2:45PM

Location: Zoom meeting room accessible through Canvas


Labs: TBD

Location: Zoom section room accessible through Canvas

Staff

Lead Instructor: David Sondak

Teaching Fellows:


New Policies for Recording Classroom Sessions Conducted via Zoom

These following policies are taken from Rules and Best Practices for the Recording of Classroom Sessions Conducted via Zoom - IT Help. The main points are summarized below.

  1. Students are not permitted to record class sessions, including by using Zoom. Links to class session recordings will only be posted in the Zoom link of the Canvas course webpage.
  2. Students must not disclose any Zoom recording URL - or any copies of the recording the student might create or obtain - to anyone outside the class.


About the Course

Learning Outcomes

After completing this course you should be able to:

Prerequisites

Students are expected to have basic programming experience, familiarity with Python and C, basic knowledge of Linux including using the command line, and basic understanding of algorithms (CS107/AC207 or CS50).

Intended Audience

The course is aimed at students with a background in a scientific discipline who will not typically have a traditional Computer Science background, though basic programming knowledge is assumed as a prerequisite. We hope to attract students from the life sciences, physical sciences, economics, social sciences, medicine, and the humanities interested in developing applications for large-scale computational or data processing.

This course is also for computer science, engineering, and undergraduate students that need to make decisions about the architecture of a system, choose tools for solving a given problem and figure out how best to apply them, or better understand the strengths and weaknesses of existing systems and tools.

Required Textbook

None.

Suggsted Textbooks

You may find the following texts helpful to supplement the content covered in the course. None of the homework problems or course content will be taken directly from any of these books. You should use them as supplementary material.


Course Format

The course is designed to study and discuss the principles (reading assignments and lectures), to develop practical skills (hands-on sessions, programming assignments and infrastructure guides), to expose students to real-world life experiences (case studies and guest lectures), and to apply the concepts to solve a real-life problem (project).

Reading Assignments

Some lectures include a required reading to ensure that you are prepared for the activities in class. You are expected to complete the required reading and answer the associated questions. The questions will be posted on Piazza. Post your comments under the Piazza note announcing the publication of the reading assignment using the “Follow-up discussion” feature. Examples of good comments:

Typically you should set aside 1-2 hours to complete each reading assignment. Even though we do not expect you to fully understand everything before coming to class, you will often have to read some passages several times to build your understanding. The goal of the reading assignments is to prepare for class, to familiarize yourself with new terminology and definitions, and to determine which part of the subject needs more attention.

Lecture Sessions

Lecture attendance is strongly encouraged. You should treat the lecture as you would in a normal, in-person semester. However, lecture attendance is not strictly mandatory to accommodate a variety of time zones. If you are in a time zone in which you are able to attend lecture, then you should do so. If your time zone precludes you from attending lecture, then you may watch the recorded lecture, which can be access via Canvas.

Lectures are organized under themes and include explanation of theoretical concepts to build a conceptual framework, and simple examples and case studies to illustrate the theory. They may also include discussion of reading assignments to develop problem-solving strategies and critical thinking. Please arrive on time. Lectures will be accompanied by concept quizzes to assess your understanding of the material and to help us identify gaps.

Hands-on Sessions

Hands-on session attendance should be treated in the same manner as lecture attendance. Hands-on sessions provide an opportunity to learn and practice the main programming models, which will be used in the programming assignments (homework) and the final project. Students should prepare the execution framework needed to do the exercises according to the guidelines provided by the instructors before coming to the session. The course includes hands-on exercises on AWS cloud and Harvard’s Cannon supercomputer.

Lab Sessions

Lab attendance is mandatory. These sessions count toward synchronous learning sessions and allow you to interact with members of the teaching staff. Lab sessions are used to allow students to become familiar with the computing and data processing infrastructure on AWS by following the infrastructure guides and to provide help with the homework (programming assignments) and the final project.

Please refer to the main page for the specific lab times. You may attend any lab session that you wish and as many sessions as you like in a given week. You may also change which lab you attend each week. There are no deliverables for the lab sessions. Your lab grade will be based on attendance.

Guest Lectures

Guest lectures are given by experts with proven expertise in the course topics to expose students to real-world life experiences about the application of the models and platforms covered in the course.

Quizzes

Quizzes will be released on Canvas by the end of the lecture sessions and the material will be based on what was discussed in the class. Each quiz will be available for 24 hours from the end of the corresponding lecture and you will have 5 minutes to take the quiz. There will be no retakes or makeup quizzes for any reason. Your lowest score on all quizzes will be dropped.

Homework

Lectures are complemented by homeworks to bridge theory and practice. Homework will mostly consist of basic programming assignments to exercise a technology or programming model. Homework assignments will be posted on the website on Mondays and will be due on a subsequent Monday (listed in the course schedule).

There are no late days. Please contact me directly in the event of an emergency.

Infrastructure Guides

Infrastructure guides help with the deployment of parallel computing and big data processing frameworks on the AWS cloud for developing, testing and evaluating the programming assignments and the final project.

Project

A major component of the course is a final programming project. Your final project is to solve a compute or data intensive scientific problem using the platforms, tools and systems introduced in the course. You will collect the data, implement the tool, and analyze the performance of an end to end application. You are required to form teams and to partition the work among the team members. The final project has six milestones: 1. Team formation 2. Rough draft of project proposal 3. An in-class presentation of your project proposal 4. An in-class presentation of your progress with the design of the project 5. Submission of project deliverables 6. Submission and final presentation to teaching staff.

Further details about the project will be updated under the Projects page.

Exams

We will not have standard midterm or final exams. Instead, we will have in-class quizzes, two in-class presentations on project proposal and progress, and a final project presentation during the scheduled final exam period.

Piazza

We'll be using Piazza for online class discussions. We will also use Piazza for all course announcements. Piazza is your main venue to ask questions, discuss problems, and help each other out. It should always be your first recourse for seeking answers to your questions about the course, lecture or reading material, or the assignments. Participation on Piazza will factor into your participation grade for the course.

Office Hours

The instructors and the teaching fellows hold weekly office hours. Office hour times and locations are listed on the class homepage. Office hours provide you with an opportunity to review and discuss course materials as well as provide further guidance for your homework in a more intimate environment, with only your teaching fellow and maybe a handful of classmates present.


Grading

Relative Weighting

You will be graded on homework assignments, a final project, in-class quizzes, and participation. There will be no exams. The final grade will be composed as follows:

Homework Grading

Homework will be graded based on 1) how correct your code is (the code should compile and run, we are not troubleshooting code), and 2) how you have interpreted the results in a report. Your work will be evaluated holistically beyond mechanical correctness and focus on the overall quality of the work.

Homework Regrading

It is very important to us that all assignments are properly graded. If you believe there is an error in your assignment grading, please submit an explanation via email to us within 7 days of receiving the grade. No regrade requests will be accepted orally and no regrade requests will be accepted more than 7 days after you receive the grade for the assignment. Also, note that requesting a regrade applies to the entire assignment.

Late Days

No homework assignments or project milestones will be accepted for credit after the deadline. If you have a verifiable medical condition or other special circumstances that interfere with your coursework please let us know as soon as possible.


Policies

Contingency Plan

In the event of a prolonged Zoom outage, the course lecture will be recorded offline and posted to Canvas as soon as possible.

Accessibility

Any student receiving accommodations through the Accessible Education Office should present their AEO letter as soon as possible. Failure to do so may prevent us from making appropriate arrangements.

Devices in Class

We will use laptops throughout the term to facilitate activities and project work in-class. However, research and student feedback clearly shows that using devices on non-class related activities not only harms your own learning, but other students’ learning as well. Therefore, we only allow device usage during activities that require devices. At all other times, you should not be using your device. We may help you remember this by announcing when to bring devices out and when to put them away. This will be hard to enforce in a Zoom class, but we ask for your cooperation in this matter.

Participation

Helping each other out and discussing the reading assignments and lectures is a key aspect of this course. All students are expected to contribute online on Piazza and during lectures. Participation on Piazza will contribute to the final grade.

Collaboration

You are welcome to discuss the course's material and homework with others in order to better understand it, but the work you turn in must be your own (with some exceptions, e.g., the final project, where work is explicitly shared). You are encouraged to discuss programming assignments with classmates, but should be open about such cooperation, and should attribute anyone you collaborated with in your homework.

There is a balance to be struck between submitting your own work (to demonstrate you're learning the material) and discussion/collaboration with others (to enhance learning through mutual assistance). In general, avoid sharing actual code (particularly code you hand in), but feel free to discuss, diagram, use pseudocode, and even share small amounts of code ("snippets"). If you are in doubt as to the appropriateness of some level of collaboration with other students, contact the course instructor.

The class staff will be using codeanalysis tools to compare students work; plagiarism will not be tolerated, and students may be asked to work more independently if their work is too similar. You may not submit the same or similar work to this course that you have submitted or will submit to another, without permission. You must acknowledge any source code that was not written by you by mentioning the original author(s) directly in your source code (comment or header), or in a README.txt file accompanying your submission. Do not remove any original copyright notices and headers. All forms of academic dishonesty will be forwarded to the Harvard College. For more information please consult the Harvard academic integrity guidelines: Academic Integrity and Academic Dishonesty.


Credits

The lecture material is adapted from the books and online research resources relevant to the course topics. Please contact us if you find materials where the credit is missing or that you would rather have removed.

Diversity and Inclusion Statement

Computer science, like many fields of science, has historically only been represented by a small portion of the population. This is despite some of the pioneers in computer science being from groups that are historically and presently underrepresented. Whenever possible, I will try to highlight the contributions that people have been from a variety of backgrounds. To start, here is a list of some really nice references.:

I welcome any additions to this list you may have!

In an ongoing effort to foster a more inclusive environment in computer science, recent initiatives have attempted to overcome some barriers to entry for underrepresented groups:

Like the first list above, this list is not enhaustive, but I welcome any additions and suggestions you may have.

I would like to attempt to discuss diversity in computer science from time to time where appropriate and possible.

Please contact me (in person or electronically) or submit anonymous feedback if you have any suggestions to improve the quality of the course materials. The best way to provide anonymous feedback is to use Piazza, which allows you to provide comments anonymously.

Furthermore, I would like to create a learning environment for my students that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion, ability, etc.) To help accomplish this: