Computation and Visualization for Analytics
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.
Upcoming Deadlines
No upcoming deadlines at this time
Course Materials
R For Data Science (R4DS)
RequiredShiny Tutorial
AdditionalTableau Public Knowledge Base
AdditionalR For Everyone (RFE)
AdditionalR Markdown (RMD)
RecommendedR Graphics Cookbook (RGC)
AdditionalAdvanced R (ADR)
RecommendedR Packages (RPK)
AdditionalText Mining with R (TM)
RecommendedTeaching Staff
Course Schedule
| Date | Topic | Materials | Assignments |
|---|---|---|---|
| Module 1: Introduction | |||
| Sep 15 |
Course introduction and expectations |
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| Sep 22 |
Basics of R |
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| Module 2: Basic Visualization and Data Engineering | |||
| Sep 28 |
Last day to drop a full-semester fall class without a W grade |
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| Sep 29 |
R functions and the grammar of visualization |
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| Oct 6 |
Data Visualization Concepts |
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| Oct 13 |
Basic data visualization in R |
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| Oct 20 |
Data transformation with dplyr |
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| Oct 27 |
Data wrangling with tibbles, readr and tidyr |
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| Nov 3 |
Visualizing relational data |
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| Module 3: Advanced Visualization | |||
| Nov 10 |
Introduction to Shiny interactive visualization web app |
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| Nov 24 |
No Classes - Thanksgiving |
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| Dec 1 |
Exploratory data analysis and more data visualization |
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| Dec 1 |
Project presentations |
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| Dec 8 |
No Classes - Preparation for Presentation |
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| Dec 15 |
Project presentations |
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| Happy Holiday! | |||
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.
Projects
More details will be posted later in the semester.
Grading
| Component | Percentage |
|---|---|
| Homework | 42% |
| Final Project | 50% |
| Proposal | 10% |
| Presentation | 40% |
| Class Participation | 8% |
This course does not have any quizzes or exams.