IE6600 Fall 2020 Northeastern University • Boston 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.

Tue, Fri 08:30am – 10:10am (ET)

09/11/2020 – 12/11/2020

Online via Zoom, Boston

4 Credits

Upcoming Deadlines

No upcoming deadlines at this time

Course Platform

Canvas

Course materials and grades

Piazza

Course discussions

Zoom

Visit Zoom

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-2:00pm ET

Anuja Nanal

Teaching Assistant

Zoom

Office Hours: Thur 10:00am to 11:00am ET

Jinhui Zhao

Guest Lecturer

Software Engineer, Tech Lead @Amazon

Topic: Job Search and Interviewing

Technical Lead and Full Stack Engineer with 10+ years experience, formerly at TikTok and Amazon.

Qibin Tan

Guest Lecturer

DevOps Engineer @iRobot

Topic: Job Search and Interviewing

Build and Release Engineer with 7+ years experience in DevOps.

Wenxin Liu

Guest Lecturer

Cloud Infrastructure Architect @Amazon

Topic: Job Search and Interviewing

Cloud Infrastructure Architect with 10+ years experience in cloud architecture.

Kate Kryder

Specialist

Data Visualization Specialist

See course slides for booking link

Office Hours: By appointment

Jodi Bolognese

Specialist

Engineering Librarian

See course slides for booking link

Office Hours: By appointment

Course Schedule

Date	Topic	Materials	Assignments
Module 1: Introduction
Sep 11	Course introduction and expectations	Instructor's Brain
Sep 15	Basic of R I	R4E Basics of R R4E Advanced Data Structure
Sep 18	Basic of R II	R4E Basics of R R4E Advanced Data Structure
Sep 22	R functions and the grammar of visualization I	R4DS Workflow Link Wickham A layered grammar of graphics Paper (optional) R4E Writing R functions
Sep 25	R functions and the grammar of visualization II	R4DS Workflow Link Wickham A layered grammar of graphics Paper (optional) R4E Writing R functions Link	HW1 released
Module 2: Basic Visualization and Data Engineering
Sep 29	R functions and the grammar of visualization II	R4DS Workflow Link Wickham A layered grammar of graphics Paper (optional) RGC Quickly Exploring Data Link	Group arrangement due Due Sep 28 @23:59 ET
Reminder: 9/29, Last day to drop a full-semester fall class without a W grade
Oct 2	Data Visualization Concepts I
Oct 6	Data Visualization Concepts II Basic data visualization in R I	R4DS Data Visualization with ggplot2 Link (additional) RGC Quickly Exploring Data	HW1 released Due Oct 5 @23:59 ET
Oct 9	Basic data visualization in R II	R4DS Data Visualization with ggplot2 Link (optional) RGC Quickly Exploring Data	HW2 released
Oct 13	Data transformation with dplyr I	R4DS Data Transformation with dplyr Link
Oct 16	Data transformation with dplyr II	R4DS Data Transformation with dplyr Link	HW3 released HW2 Due Oct 15 @23:59 ET
Oct 20	Data wrangling with tibbles, readr and tidyr I	R4DS Tibbles with tibble Link R4DS Data Import with readr Link R4DS Tidy Data with tidyr Link
Oct 23	Data wrangling with tibbles, readr and tidyr II Optional: Data wrangling with stringr, forcats and lubridate Instructor: Zhenyuan Lu, PhD	R4DS Tibbles with tibble Link R4DS Data Import with readr Link R4DS Tidy Data with tidyr Link (optional) R4DS Strings with stringr, Factors with forcats, Dates and Times with lubridate Link	HW4 released Project proposal Due Oct 22 @23:59 ET HW3 Due Oct 22 @23:59 ET
Oct 27	Visualizing relational data	R4DS Relational Data with dplyr Link
Module 3: Advanced Visualization
Oct 30	Introduction to Shiny interactive visualization web app I	R Shiny Shiny tutorial Link	HW5 released HW4 Due Oct 29 @23:59 ET
Nov 3	Introduction to Shiny interactive visualization web app II	R Shiny Shiny tutorial Link
Nov 6	Introduction to Shiny interactive visualization web app III	R Shiny Shiny tutorial Link
Nov 10	Data analytics web apps with Shiny	R Shiny Shiny tutorial Link
Nov 13	Exploratory data analysis and more data visualization I	R4DS Exploratory Data Analysis Link	HW6 released HW5 Due Nov 12 @23:59 ET
Nov 17	Exploratory data analysis and more data visualization II	R4DS Exploratory Data Analysis Link
Nov 20	No class - Moved to 11/24		HW6 due 11/23 @ 11:59pm Due Nov 23 @23:59 ET
Nov 24	Additional topics and Workshop Guest Topic: Job Search and Interviewing Guest: Jinhui Zhao @Amazon Guest Topic: Job Search and Interviewing Guest: Qibin Tan @iRobot Guest Topic: Job Search and Interviewing Guest: Wenxin Liu @Amazon
Dec 29	No classes - Thanksgiving		Project and Slides Due Nov 30 @11:59pm
Dec 1	Project presentation
Dec 4	Project presentation
Dec 8	Project presentation
Dec 11	Project presentation
Module: Happy Holidays!

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.

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.