Computation and Visualization for Analytics
IE6600 • Fall 2022 • Northeastern University • Vancouver
IE6600 covers basic of the R, and R Shiny for data preprocessing, and visualization. It introduces students to static and interactive visualization, dashboard, and platform that reveal information, patterns, interactions, and comparisons by paying attention to details such as color encoding, a shape selection, spatial layout, and annotation. Based on these fundamentals of analytical and creative thinking, the course then focuses on data visualization techniques and the use of the most current popular software tools that support data exploration, analytics-based storytelling and knowledge discovery, and decision-making in engineering, healthcare operations, manufacturing, and related applications.
- Class: Monday 01:30 - 05:00 PM (PT)
- Office hour: Wed 9:00 AM - 10:00 AM (PT) on Zoom
- Location: Room 1425, West Georgia
- Dates: 09/07/2022 – 12/17/2022
- Administration platform: All questions, discussion or notes will be only posted via Campuswire. See sign up link on Canvas.
- Guideline: Please see the post How to ask a good question before posting any questions or discussions.
- HW submission: Canvas
- Course notes: Notes
- Teaching style: There’s no speed limit.
- Instructor Zhenyuan Lu
- Email:
- Office hours: Wed 9:00 AM - 10:00 AM (PT) on Zoom
- TA Sai Gowtham Amburi
- Email:
- Office hours: Tue 10:00 AM - 11:00 AM (PT) on Zoom
- TA Yong Shi
- Email:
- Office hours: During the class meeting
Table of contents
- Table of contents
- Course goals
- Textbooks
- R-related Materials
- Schedule
- Policies
- Accommodations for Students with Disabilities
- Take care of yourself
- Homework
- Projects
- Course Evaluation
- Hall of Fame
Course goals
This section of IE6600 follows the flipped classroom model, and delivers all course materials online. The schedule below shows the due dates for all modules.
Textbooks
The required textbook:
- R For Data Science (R4DS), Wickham, Hadley, and Garrett Grolemund
The required tutorials:
- Shiny tutorial, R Shiny
Additional textbooks:
- R For Everyone (R4E), Lander, Jared P.
- R Markdown (RMD), Xie, Yihui, et al.
R-related Materials
- R Graphics Cookbook (RGC), Chang, Winston.
- Advanced R (ADR), Wickham, Hadley.
- R Packages (RPK), Wickham, Hadley.
- Text Mining with R (TM), Silge, Julia, and David Robinson.
Schedule
(subject to change)
Date | Lecture | Readings | Logistics | |
---|---|---|---|---|
Module 1: Introduction | ||||
9/12 |
Course introduction and expectations; Basic of R [slides] |
|
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9/19 |
Basic of R
[slides] [slides2] |
|
||
9/26 |
R functions and the grammar of visualization
[slides] |
|
||
9/27, Last day to drop a full-semester fall class without a W grade | ||||
Module 2: Basic Visualization and Data Engineering | ||||
10/3 |
Data Visualization Concepts
|
Group arrangement due 10/2 @ 11:59pm PT HW1 out |
||
10/10 | No classes - Thanksgiving Day (CAN) | |||
10/17 |
Basic data visualization in R
[slides] |
|
HW1 due 10/16 @ 11:59pm PT |
|
10/24 |
Data transformation with dplyr
[slides] |
HW2 due 10/23 @ 11:59pm PT |
||
10/31 |
Data wrangling with tibbles, readr and tidyr Optional: Data wrangling with stringr, forcats [slides] [slides2] |
|
HW3 due 10/30 @ 11:59pm PT |
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11/7 |
Visualizing relational data; Visualization by PCA [slides] [github] |
|
HW4 due 11/6 @ 11:59pm PT |
|
Module 3: Advanced Visualization | ||||
11/14 |
Introduction to Shiny interactive visualization web app I
[slides] |
|
Project proposal due 11/13 @ 11:59pm PT |
|
11/21 |
Introduction to Shiny interactive visualization web app II
[slides] |
|
||
11/28 |
Additional Topics and Workshop Optional: EDA and more data visualization [slides] [github] |
HW5 due 11/27 @ 11:59pm PT |
||
12/5 |
Preparation for final project and presentation (No Class)
|
|
HW6 due 12/4 @ 11:59pm PT |
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Project and Slides due 12/11 @ 11:59pm PT | ||||
12/12 |
Project presentation
|
|||
Peer Review due 12/13 @ 11:59pm PT | ||||
Team members evaluation due 12/13 @ 11:59pm PT |
Policies
Please post questions, or discussion only via Canvas. The visibility of questions and discussion are expected to set for public view (to the Entire class on Canvas). Please feel free to send instructor/TAs emails regarding any personal or other private issues/concerns.
Plagiarism, cheating, and any form of unauthorized collaboration will not be tolerated and will be handled in accordance with University policies described in the Student Handbook. For additional information on Northeastern University’s Academic Integrity Policy
Northeastern University strictly prohibits discrimination or harassment on the basis of race, color, religion, religious creed, genetic information, sex, gender identity, sexual orientation, age, national origin, ancestry, veteran, or disability status. Please review Northeastern’s Title IX policy, which protects individuals from sex or gender-based discrimination, including discrimination based on gender-identity. Faculty members are required to report all allegations of sex/gender-based discrimination to the Title IX coordinator.
Accommodations for Students with Disabilities
If you have a disability, I encourage you to contact Disability Resource Center to register and request the accommodations. Also please discuss your needs with me as early in the semester as possible.
Take care of yourself
Eating healthy food, having regular exercises, avoiding alcohol and drugs, getting adequate sleep and taking time to relax. This will help you achieve your goals and tame stress.
If you have difficulty to keep up with any materials or homework for personal reasons please let me know early. If you or your friends/classmates who appears to be struggling, or having trouble coping with stress. We strongly encourage you to seek support at the We Care program at NEU. At Northeastern, a student is never alone when struggling with a demanding situation.
Homework
There are 6 individual homework assignments. Due day will be posted with the homework. Late submission would not be accepted. Each student has one time 3-day extension per semester only applied on homework. This extension will be applied automatically. Please let me know 24 hours in advance before the due day if any emergencies or difficulties occur.
Requests for regrades in writing will only be accepted no less than 10 days after receiving grade. Please send the instructor your NUID, and name with title “Request for regrade: HW+number” via email. The new grade may be lower than the original one.
Please feel free to refer to any materials from my slides. You may discuss homework with your classmates, but all the assignments are supposed to completed by your own. Sharing of completed solutions will not be tolerated. Plagiarism will be considered, if solutions and project documentations with a very high degree of similarity with other student’s or materials online. Such academic dishonesty will be handled in accordance with university policies.
Projects
More details will be posted later in the semester.
- Project Example Students’ demo
- Credits: Qiu Yucheng; Yu Wei
Course Evaluation
- Homework 42%
- Final Project 50%
- Proposal 10%
- Presentation 35%
- Peer Review 5%
- Class Participation 8%
This course does not have any quizzes or exams.
Hall of Fame
Top Projects
Fall2022
: Best Project : Honorable Mention
: COVID-19 Analysis
Bananas: I-Hsuan Huang, Yiming Wang, Yen-Fong Li, Wenzheng Liao
: Academic success visualization and analysis
TaylorExpansion: Farnaz Mohseni, Maryam Kian, David Suero Cobos, Sashankh Addanki, Yu Swe Zin Aung
: Washington Electric Vehicle Data Visualization and Analysis
KohenKappa: Gen Li, Zhichun Li, Fante Meng, Lingyun Ding
Top Contributors
Fall2022
: Yiming Wang
: Farnaz Mohseni
: Gen Li; Zhichun Li; Fante Meng