Course Policies

Summary

This class is divided into three parts. Part 1 will build students’ skill-sets to be able to read data into R, manipulate, clean and understand this data, and visualize data using a wide variety of graphical concepts and tools. Part 2 will build on part 1 and focus on uncovering relevant patterns in the data using a variety of modeling approaches. Part 3 puts the parts together and has students explore different data from start to finish, with an emphasis on learning how to communicate their analyses to an audience.

Grading

Key course components listed below. For each component/theme, students will be assigned some combination of readings, problem-solving tasks and, occasionally, a competency quiz for each of the 14 course components shown below. These grades are pass/fail. Data Science is a team sport, your participation and interpersonal collaborations matter here.

Component #	Topic
1	EDA in Data Science
1	Summarizing Data
2	Foundations of Graphics
3	ggplot2 fundamentals
4	Visualize: distributions
5	Visualize: correlations
6	Visualize: rankings & other patterns
7	Visualize: time series
8	Participation and Review
9	Intro to ML
10	Unsupervised learning: k-means 1
11	Unsupervised learning: k-means 2
12	Spatial Data: intro to maps
13	Text-as-Data
14	Dimension reduction: principal component analysis

Final grades will be assigned as follows.

Assignments and Class Work:

Grade	Requirement
A (~95%)	Successful completion of all assignments + good participation
B (~85%)	Successful completion of 12 or more assignments + good participation
C (~75%)	Insufficient completion of course assignments + insufficient participation
F	Otherwise

Exam and Projects:

Your final exam and projects will be given a letter grade (A, B, C, F). In the end, these three grades (assignments and class work, mid-term, and final project) will be weighted as follows:

Weight	Requirement
30%	Assignments, participation, and in-class work
30%	Mid-term exam
40%	Final project

Grades will be taken sometimes in class and sometimes outside of class. You can view your grades in BrightSpace. Grading will work along these lines: you will either be graded as having completed an assignment (1) or not (0). If you have not completed an assignment, but made a best faith effort to accurately complete it, you will be asked to complete it in office hours. (You need have to resubmit in person during office hours and have 1 week to resubmit). If you have not at all tried to complete the assignment by the time of the due date, you will simply be given a 0 = incomplete. (If you have a personal situation keeping you from attending class or working on an assignment please come see me).

Communication

Students will be invited to a course slack channel. Questions related to course logistics, content, homework, quizzes, or the final project should be posted in the slack channel. Individual questions should be sent to the instructor and/or TA by direct slack message.

Office Hours

Currently office hours are set to follow immediately after class until 5pm in room 202. Subject to change.

Collaborative learning

Students are encouraged to work together on homework assignments. Unless specifically noted in the instructions, students should not collaborate on quizzes or work otherwise noted as ‘individual work.’ Students that violate the collaborative-work policy on a quiz will fail the quiz in question and forfeit the opportunity to retake or resubmit.

Inclusive policy

This class respects and welcomes students of all backgrounds, identities, and abilities. If there are circumstances that make our learning environment and activities difficult, if you have medical information that you need to share with me, or if you need specific arrangements in case the building needs to be evacuated, please let me know. I am committed to creating an effective learning environment for all students, but I can only do so if you discuss your needs with me as early as possible. I promise to maintain the confidentiality of these discussions. If appropriate, also contact Student Access Services to get more information about specific accommodations.

Safety

The safety of students, faculty, and staff at Vanderbilt University is of the utmost importance. As a Vanderbilt student, you are automatically enrolled in AlertVU, which is used in emergencies which pose an imminent threat to the community. If you need to contact the Vanderbilt Police in an emergency, call 911 from any campus phone or (615) 421-1911 from any other phone. Additional information about emergency preparedness is available online.