SYS 2202: Data and Information Engineering
Fall 2019
Course Info
| Course Info | |
|---|---|
| Class Time: | M,W,F 9:00 - 9:50am |
| Class Location: | Mechanical Engr 341 |
| Instructor: | Dr. Michael D. Porter |
|---|---|
| Email: | mdp2u {at} virginia.edu |
| Office: | 102F Olsson Hall |
| Office Hours: | Tues 2:00-3:00pm (and by appt.) |
| TA: | Sodiq Adewole |
|---|---|
| Email: | soa2wg {at} virginia.edu |
| Office Hours: | 5:00 – 6:15 PM Tuesdays & Wednesdays Thornton Hall D102 (and by appt.) |
Course Prerequisites:
The official pre-reqs are SYS 2001, CS 1110, and CS 2110. Systems thinking and basic programming concepts will be most useful for this course.
Course Description:
This course provides an introduction to a fundamental aspect of data science - working with data. Learn skills to efficiently and effectively obtain, manipulate, store, and analyze data (i.e., convert data to information) to support decision making and future data modeling (e.g., regression, data mining, machine learning) efforts. Emphasis on obtaining, cleaning, combining, and wrangling the data into a more usable form. Learn how to break up a large dataset into manageable pieces and then use a variety of quantitative and visual tools to summarize and extract information from it. The challenges of big data (e.g., size, streaming data, mixed variable types) will be addressed throughout the course. As an introductory course, focus will be on understanding basic concepts and how to implement them in R, a leading data science language.
Student Learning Objectives:
Students completing this course should be able to: (i) manipulate and extract information from several types of data (ii) make informative, and pretty, plots to aid in decision making (iii) prepare data for modeling (e.g., regression, data mining, and machine learning) (iv) create and present reproducible results from data analysis (v) effectively communicate the results of data analysis and the methods used to obtain the results.
Textbooks:
Required
- R for Data Science by Hadley Wickham and Garrett Grolemund
- Free online version: http://r4ds.had.co.nz/
- Paperback is available
- R Cheatsheets
- Tidyverse Website
Optional
- ggplot2 (2nd edition) by Hadley Wickham
- Free online version: https://ggplot2-book.org/
- Paperback is available
- R Graphics Cookbook by Winston Chang
- some online portions: http://www.cookbook-r.com/Graphs/
- R in Action by Kabacoff
- R for Everyone: Advanced Analytics and Graphics by Jarad Lander
- R Cookbook by Paul Teetor
Other Course Materials:
Software
- This course requires the use of the following software:
- R (http://cran.us.r-project.org) is a free command-line based statistical language
- RStudio is a free IDE for R (http://www.rstudio.com/ide)
- All of these programs are free and cross-platform (Windows, Mac, Linux). Install R first, then RStudio. Use the latest versions.
- While these programs are available in the computer lab, it is required to have these programs on your laptop so you can participate in quizzes and in-class labs.
DataCamp
- This course will require access to DataCamp.
- DataCamp will provide free academic access for the semester. Thanks DataCamp!
- You will receive an invitation over email with instructions for gaining access to your account
Course Assessment:
The course grade will be based on homework, in-class labs, midterm exams, and a final project (presentation and report).
A+: \(\scriptstyle \geq\) 98%, A: 92-97%, A-: 90-91%, B+: 88-89%, B: 82-87%, B-: 80-81%, etc.
- Homework: 40%
- DataCamp Assignments
- In-Class Participation: 10%
- Labs
- Discussion
- Midterms: 2 x 15% (30% total)
- In-class
- Project: 20%
- Groups of 3
- Preliminary Results (5%)
- Presentation (10%)
- Report (5%)
- There is no grade “curving”.
- There will be no make-up homework, exams, or quizzes.
- There will be no “extra credit” assignments; spend your time on the assigned work.
- If you have special circumstances (e.g., a documented physical condition) that prevent you from adhering to the posted deadlines, please inform me as soon as possible.
- The DataCamp homework assignments will be scored based on XP points. There will be a mix of required courses and electives used to gain XP.
- Score for homework = XP/55000
- Note: there is no extra credit for exceeding 55000XP :(
- The in-class participation grade will be based on a combination of classroom participation and in-class labs
- Pre-class assignments (reading and coding) will prepare you for the lecture. Absences or neglect of the pre-class assignments will result in a lowering of your participation grade.
- In-class labs will be assigned on a (roughly) weekly basis. Instructions and grading information will be provided with the lab.
- There will be two in-class midterm exams
- The exam dates are posted in the Class Schedule (on the course website). Note these now so there are no conflicts.
- There will be no make-up exams except under extreme circumstances.
- The final project allows you to put into practice what you have been learning and demonstrate your mastery of the course material. You must come up with your own project topic. Ideally your project will be related to your current research, job aspirations, or interests.
- Teams of 3
- The preliminary results are to demonstrate that you are on the right pace
- The presentations will take place at the end of the semester and will be evaluated by the teaching staff and your classmates
- The final report will incorporate revisions suggested during the presentation. There will be several options for format (e.g., report, blog, whitepaper, webpage). More details will be provided later in the semester.
Course Outline:
- Introduction to R
- R and Rstudio
- Basics of R
- Reading Data into R
- Advanced Data Structures
- Loops, Control Statements, Functions
- Data and Data Wrangling
- Transforming Data
- Reshaping Data
- Cleaning Data
- Relational Data
- Working with spatial, temporal, text, and html data
- Visualization
- single variable plots (e.g., histograms, barplots, boxplots)
- two variable plots (e.g., scatterplots, line plots, dotplots)
- multiple variable plots (e.g., faceting, colors, shapes, shading)
- ggplot2 graphics
- spatial graphics
- interactive graphics
Course Management
- Most course material will be available from the class webpage
- Announcements may be made in Collab
- Labs and final project deliverables will be submitted in Collab
- Homework assignments will be completed in DataCamp
Academic Calendar:
Important dates for the semester can be found on the academic calendar: http://www.virginia.edu/registrar/calendar.html
Policy on Academic Misconduct (Honor Code):
I trust every student in this course to fully comply with all provisions of the University’s Honor Code and work together to maintain UVA’s Community of Trust. By enrolling in this course, you have agreed to abide by and uphold the Honor System of the University of Virginia, as well as the following policies specific to this course.
- All submitted work must be pledged.
- All work must be completed individually unless specific permissions are given on the assignment.
- Homework and in-class exercises can be discussed with classmates, but the final write-up, code, and solutions must be your own.
- It is not always easy to tell what qualifies as a violation, so do not be afraid to talk to me about it. Such discussions do not imply guilt of any kind.
- All suspected violations will be forwarded to the Honor Committee, and you may, at my discretion, receive an immediate zero on that assignment regardless of any action taken by the Honor Committee.
Please let me know if you have any questions regarding the course Honor policy. If you believe you may have committed an Honor Offense, you may wish to file a Conscientious Retraction by calling the Honor Offices at (434) 924-7602. For your retraction to be considered valid, it must, among other things, be filed with the Honor Committee before you are aware that the act in question has come under suspicion by anyone. More information can be found at http://honor.virginia.edu. Your Honor representatives can be found at: http://honor.virginia.edu/representatives.
Disability Statement:
The University of Virginia strives to provide accessibility to all students. If you require an accommodation to fully access this course, please contact the Student Disability Access Center (SDAC) at (434) 243-5180 or sdac@virginia.edu. If you are unsure if you require an accommodation, or to learn more about their services, you may contact the SDAC at the number above or by visiting their website at http://studenthealth.virginia.edu/student-disability-access-center/faculty-staff.
Your Well Being
The University of Virginia and School of Engineering and Applied Sciences serve as a safe space for students and aims to promote your well-being. If you are feeling overwhelmed, stressed, or isolated, there are many individuals here who are ready and wanting to help. If you wish, you can make an appointment with me to discuss in private. Alternatively, the Student Health Center offers Counseling and Psychological Services (CAPS) https://www.studenthealth.virginia.edu/caps. If you prefer to speak anonymously and confidentially over the phone, call Madison House’s HELP Line 24/7 at434-295-8255 https://www.madisonhouse.org/overview-helpline/.
If you or someone you know is struggling with gender, sexual, or domestic violence, there are many community and University of Virginia resources available. The Office of the Dean of Students, Sexual Assault Resource Agency (SARA), and UVA Women’s Center are ready and eager to help. Contact the Director of Sexual and Domestic Violence Services at 434-982-2774.
Religious Accommodations
Students who wish to request academic accommodation for a religious observance should submit their request to me by email as far in advance as possible. If you have questions or concerns about your request, you can contact the University’s Office for Equal Opportunity and Civil Rights (EOCR) https://eocr.virginia.edu/accommodations-religious-observance. Accommodations do not relieve you of the responsibility for completion of any part of the coursework you miss as the result of a religious observance.
