IE6600 Spring 2020 Northeastern University • Seattle Past

Computation and Visualization for Analytics

r shiny tableau

This course 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.

Mon, Thur 08:30am – 10:10am PT

09/09/2020 – 12/10/2020

Zoom, Seattle

4 Credits

Upcoming Deadlines

No upcoming deadlines at this time

Course Platform

Canvas

Course materials and grades

Piazza

Course discussions

Course Materials

R For Data Science (R4DS)

Required

Author: Wickham, Hadley, and Garrett Grolemund Url

Shiny Tutorial

Additional

RStudio Shiny Tutorial Url

Tableau Public Knowledge Base

Additional

Tableau Public Knowledge Base Url

R For Everyone (RFE)

Additional

Author: Lander, Jared P. Url

R Markdown (RMD)

Recommended

Author: Xie, Yihui, et al. Url

R Graphics Cookbook (RGC)

Additional

Author: Chang, Winston Url

Advanced R (ADR)

Recommended

Author: Wickham, Hadley Url

R Packages (RPK)

Additional

Author: Wickham, Hadley Url

Text Mining with R (TM)

Recommended

Authors: Silge, Julia, and David Robinson Url

Teaching Staff

Zhenyuan Lu, PhD

Instructor

Deep Learning Scientist and AI/ML Adjunct Faculty

Zoom

Office Hours: Tue 1:00pm to 2:00pm PT

Course Schedule

Date	Topic	Materials	Assignments

Polices

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.

Course Goals

R For Data Science, Wickham, Hadley, and Garrett Grolemund

Accommodations for Students with Disabilities

If you have a disability, I encourage you to contact the Disability Resource Center to register and request accommodations. Also, please discuss your needs with me as early in the semester as possible.

Taking Care of Yourself

Eating healthy food, having regular exercise, avoiding alcohol and drugs, getting adequate sleep, and taking time to relax will help you achieve your goals and manage stress.

If you have difficulty keeping up with any materials or homework for personal reasons, please let me know early. If you or your friends/classmates appear 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. Please let me know 72 hours in advance before the due day if you need extensions with a reasonable justification. 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.