Course Goals

• Learn visual and computation techniques and tools, for typical data types
  • Learn how to complement each kind of methods
  • Gain a breadth of knowledge
• Work on real datasets and problems
• Learn practical know-how (useful for jobs, research) through significant hands-on programming assignments

Announcements and Discussion

The fastest way to get help with homework assignments is to post your questions on Piazza. That way, not only our TAs and instructor can help, your peers can too.

If you prefer that your question addresses to only our TAs and the instructor, you can use the private post feature (i.e., check the "Individual Students(s) / Instructors(s)" radio box).

While we welcome everyone to share their experiences in tackling issues and helping each other out, but please do not post your answers, as that may affect the learning experience of your fellow classmates.

For special cases such as failed submissions due to system errors, missing grades, failed file uploads, emergencies that prevent you from submitting, personal issues, you can contact the staff using a private Piazza post.

Course Staff & Office Hours

TAs plan to hold office hours starting week 2, except on Georgia Tech holidays (e.g., thanksgiving, MLK day, spring break). Each office hour session will be run by at least one TA, and is 1 hour long. See GT’s academic calendar for the full list of holidays (https://registrar.gatech.edu/calendar). We will spread the office hours across weekdays, and across time of the day. We will announce the office hour times.

We will hold office hours via Slack, where the TA running the office hour will be responsive. We will share information about how to join the appropriate Slack group.

Please note that you are always welcome to ask questions on Piazza. Office hours supplement Piazza, and do not replace it.

Polo's and Mahdi's office hours (click to sign up) are on Tuesdays 4:45 to 5:15 pm Atlanta time (i.e., after class), starting week 2, focusing on lectures and projects. For assignemnt related questions, please use Piazza and TA office hours.

This course can be very tough for many!

The amounts of time students spend on this class greatly vary, based on their backgrounds, and what they may already know. Some former students told us they spent about 40-60 hours on each homework assignment (we have 4 big assignments, and no exams), and some reported much less. For example, for the homework assignment about D3 visualization programming, students who are completely new to javascript, css, and html likely will spend significantly more time than their peers who have already tried them before. Some former students who do not have a computer science background found the homework assignments challenging, would take significant time and effort, but were rewarding, fun, and "do-able."

Students have at least 3 weeks to complete each homework assignment. Some students waited until the last week, and could not finish. It is critical to plan ahead and prepare for the significant time needed.

Some programming assignments involve high-level languages or scripting (e.g., Python, Java, SQL etc.). Some assignments involve web programming and D3 (e.g., Javascript, CSS, HTML). For example, an assignment on Hadoop and Spark may require you to learn some basic Java and Scala quickly, which should not be too challenging if you already know another high-level language like Python or C++. It is unlikely that you all know tools/skills needed in the programing tasks, so you are expected to learn many of them on the fly.

Basic linear algebra, probability and statistics knowledge is also expected.

Minimum Computer Requirements

• 8GB RAM (16GB recommended)
• 512GB disk (SSD recommended). Some assignments use data files that are more than a few GBs, and some uses virtual machines that can easily take up more than tens of GBs. It is typical for some project teams to use large datasets that are more than a few or tens of GBs.
• Dual-core Core i5 (8th generation or better recommended)

Accessing Course Materials Outside of US

Homework

• [10%] HW1: Collecting & visualizing data, SQLite, D3 warmup, OpenRefine
• [15%] HW2: D3 Graphs and Visualization
• [15%] HW3: Spark, Docker, DataBricks, AWS and GCP
• [10%] HW4: Scalable PageRank via Virtual Memory (MMap), Random Forest, Scikit-Learn

Project

Distance Learning Students

1. [Not applicable in Fall 2020] Present in class without 3-day lag; or
2. Submit a video presentation with 3-day lag (e.g., screen capture)

Grading Policy

1. Proposal (7.5% of course grade)
2. Proposal presentation (5%) (video recording)
3. Progress report (5%)
4. Final poster presentation (7.5%) (video recording)
5. Final report (25%)

Plagiarism, Collaboration Policy, and Student Honor Code

• All course participants (myself, teaching assistants, and learners) are expected to know and abide by the Georgia Tech Academic Honor Code.
• Ethical behavior is extremely important in all facets of life.

1. Plagiarism is a serious offense. You are responsible for completing your own work. You are not allowed to copy and paste, or paraphrase, or submit materials created or published by others, as if you created the materials. All materials submitted must be your own.
2. You may discuss high-level ideas with other students at the "whiteboard" level (e.g., how cross validation works, use hashmap instead of array) and review any relevant materials online. However, each student must write up and submit his or her own answers.
3. You must not put your code on public domain (e.g., public GitHub), because a (future) student could copy your code. That student obviously violates the honor code, and you may also be implicated.
4. All incidents of suspected dishonesty, plagiarism, or violations of the Georgia Tech Honor Code will be subject to the institute’s Academic Integrity procedures (e.g., reported to and directly handled by the Office of Student Integrity (OSI)). Consequences can be severe, e.g., academic probation or dismissal, grade penalties, a 0 grade for assignments concerned, and prohibition from withdrawing from the class.

Late Policy and Due Dates

• All homework and project deliverables are due at the times shown in the Course Schedule. These times are subject to change so please check back often. Convert the times to your local times using a Time Zone Converter.
• Every homework assignment deliverable and every project deliverable comes with a 48-hour "grace period". You do not need to ask before using this grace period.
  
  Your deliverable may be submitted (and resubmitted) up to 48 hours after the official deadline without penalty, but Canvas will mark your submission as "late".
  
  Canvas automatically appends a "version number" to files that you re-submit. You do not need to worry about these version numbers, and there is no need to delete old submissions. We will only grade the most recent submission.
• Any deliverable submitted after the grace period will get 0 credit. We recommend that you submit your work before the grace period begins.
• We will not consider late submission of any missing parts of a deliverable. To make sure you have submitted everything, download your submitted files to double check. If your submitting large files, you are responsible for making sure they get uploaded to the system in time. You have 48 hours to verify your submissions!
• No penalties for medical reasons or emergencies. And should they arise, you must contact the Dean of Students office. Doctor's notes, medical documentation, explanation of emergencies, etc. should be submitted to the Dean’s office. After their office receives the information, they will notify me on your behalf.

Timing Policy

• The course videos follow a logical sequence that includes knowledge-building and experience-building (assignments).
• Assignments should be completed by their due dates, in order for timely peer assessment. Peer assessments should also be completed by their due dates, to give timely feedback.
• You will have access to the course content for the scheduled duration of the course.

Attendance Policy

• This semester, this course runs as remote synchronous, which means this course is delivered completely online and with designated meeting times.
• We hope to provide you with experiences that are close to in-person learning. We welcome your suggestions on Piazza.

COVID-19 Policy