IE6600 • Spring 2022 • Northeastern University • Seattle

Zhenyuan Lu

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.

Course goals

This section of IE6600 follows the flipped classroom model, and delivers all course materials online via Canvas. The schedule below shows the due dates for all modules.

Textbooks

R-related Materials

Schedule

Date	Lecture	Readings	Logistics
Module 1: Introduction
1/18	Course introduction and expectations	Instructor’s brain
1/25	Basic of R [slides] [slides2]	R4E "Basics of R" R4E "Advanced Data Structure"
1/31, Last day to drop a full-semester fall class without a W grade
2/1	R functions and the grammar of visualization [slides]	R4DS Workflow Wickham A layered grammar of graphics (optional) R4E "Writing R functions"
Module 2: Basic Visualization and Data Engineering
2/8	Data Visualization Concepts		Group arrangement due 2/7 @ 11:59pm PT HW1 out
2/15	Basic data visualization in R [slides]	R4DS Data Visualization with ggplot2 (optional) RGC "Quickly Exploring Data"	HW1 due 2/14 @ 11:59pm PT HW2 out
2/22	Data transformation with dplyr [slides]	R4DS Data Transformation with dplyr	HW2 due 2/21 @ 11:59pm PT HW3 out
3/1	Data wrangling with tibbles, readr and tidyr Optional: Data wrangling with stringr, forcats [slides] [slides2]	R4DS Tibbles with tibble R4DS Data Import with readr R4DS Tidy Data with tidyr (optional) R4DS "Strings with stringr", "Factors with forcats"	HW3 due 2/28 @ 11:59pm PT HW4 out
3/8	Visualizing relational data [slides]	R4DS Relational Data with dplyr	HW4 due 3/7 @ 11:59pm PT
Module 3: Advanced Visualization
3/15	No classes - Spring Break
3/22	Introduction to Shiny interactive visualization web app I	R Shiny Shiny tutorial	Project proposal due 3/21 @ 11:59pm PT HW5 out
3/29	Introduction to Shiny interactive visualization web app II	R Shiny Shiny tutorial
4/5	Exploratory data analysis and more data visualization	R4DS Exploratory Data Analysis	HW5 due 4/4 @ 11:59pm PT HW6 out
4/12	Additional R Workshop (Additional) Visualization by PCA [github]	- James, Gareth, et al. An Introduction to Statistical Learning, with Applications in R - Kilian Weinberger PCA for Visualization	HW6 due 4/11 @ 11:59pm PT
Project and Slides due 4/18 @ 11:59pm PT
4/19	Project presentation
Peer Review due 4/20 @ 11:59pm PT
Team members evaluation due 4/20 @ 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.

COVID-19

All students, faculty, staff, and vendors across Northeastern’s network of global campuses will be required to wear masks indoors. Northeastern’s indoor mask mandate will take effect on Friday, August 27 on the Boston campus. Each campus will continue to stay aligned with local guidance, and all campuses will resume indoor mask wearing no later than September 6.

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.

Computation and Visualization for Analytics