Overview

Businesses and organizations today collect and store unprecedented quantities of data. In order to make informed decisions with such a massive amount of the accumulated data, organizations seek to adopt and utilize data mining and machine learning techniques. Applying advanced techniques must be preceded by a careful examination of the raw data. This step becomes increasingly important and also easily overlooked as the amount of data increases because human examination is prone to fail without adequate tools to describe a large dataset. Another growing challenge is to communicate a large dataset and complicated models with human decision makers. Exploratory data analysis, and visualizations in particular, helps find patterns in the data and communicate the insights in an effective manner. This course aims to equip students with methods and techniques to summarize and communicate the underlying patterns of different types of data. In addition to creating high-quality static visualizations, this course teaches students how to build an interactive visual analysis system.

Objectives

By the end of this course, students should successfully be able to:

• Explain pros and cons of various visual representations depending on the context and form of data.
• Choose appropriate visual representations for special forms of data such as geospatial and network data.
• Compose a visual dashboard composed of interactive visual artifacts.
• Create high-quality static visualizations.
• Plan and implement a customized interactive visual analysis system.

Prerequisite

• Prior experience with Python
• While no experience in web programming (HTML, JavaScript, CSS) is assumed, students should be a self-learner to pick up the pace unless they have experience in web programming.
• Or, permission of the instructor

Weekly Schedule

Please note this schedule is subject to change.

Grade Component Breakdown

Late submissions will not be accepted.

Grading Policy

Homework (30%; 6% each)

• No late submission is accepted unless otherwise notified.
• All submissions must be individual work.
• Direct sharing of the code for homework is expressly and strictly prohibited and constitutes an academic misconduct.
• A high-level discussion is permitted and encouraged.

Group Project (30%)

• A team of people will work together to build an interactive visual analysis system using d3.js.
• I will form groups based on Class Survey AND your requests.
• Please send your group formation request separately. Your request for group formation may not be fully accommodated.
• Your final output may be showcased online.
• Free-riders will be punished by peer evaluation. A student with an extremely and consistently bad peer evaluation may receive a 0 for Group Project, even if the group score is high.
• Deliverables
  • Project proposal (a pdf document)
  • Working interactive visual analysis system
  • System demo video
  • 4+ screenshots highlighting the features of your visual analysis system
  • Peer evaluation

Participation (20%)

• Class Survey (5%; Due 9/14 before class 12:30pm KST)
  • You will answer survey questions about yourself and your interest.
  • It will be conducted using Google Forms.
  • Survey answers will be compiled into a dataset to be used for this class.
  • Survey link will be sent out by this week.
  • Completing this survey properly is 5% of the course grade. (Some deductions may be applied if instructions were not followed.)
• Attendance (8%)
  • Attendance will be checked using a combination of (1) Zoom attendance report, (2) Socrative participation, and (3) question sheet on Google Docs for each lecture.
  • (1) Zoom attendance report: You will be marked attended if the total length of stay is longer than 50 minutes.
  • (2) Socrative participation: If you answer at least one Socrative quiz question for the lecture, you will be marked attended.
  • (3) Question sheet on Google Docs: If you post a question on the question sheet for the lecture with your real name, you will be marked attended.
  • Each lecture attendance is worth 0.5% of the course grade. You will have to only attend 16 lectures to attain the maximum attendance score.
  • By implication, you may miss almost 10+ classes without penalty. This is to accommodate cases where you cannot come to lecture for some reason including ANY life events and technical issues. DO NOT send an email asking permission to miss classes.
• Online Q&A Activities (7%)
  • Content-related questions should be first posted on the eTL Q&A board, so that other students can answer for you.
  • A private question or a question containing private information may be posted on the eTL Q&A board with "secret" option checked or you may send an email to me.
  • You earn 1% for each question or answer you post.
  • Only questions and answers posted with real name will be counted towards Online Q&A Activities grade.
  • An article posted on the eTL Discussion board will also be counted towards Online Q&A Activities grade.
  • You earn credit for maximum one question or answer per day.
  • Spammers and trollers may receive a 0, regardless of their past activities.

Other Course Policy

• Email: Start your email subject with [DAV] or [DAV-Fall21] for my email filter to correctly label incoming emails.
• Grade appeal: You have total three grade appeal opportunities for a grade item. Final course grade is not negotiable for any reason. All grade appeal must be submitted in writing (i.e., email) within two weeks after the grade for the item is released.
• Office hours: By appointment only. No one-on-one hand-holding sessions. Briefly state your agenda in email to request an office hours session. An office hours session is 15 minutes by default.