Introduction to Data Management and Processing

Schedule: Wednesdays 4:39pm - 7:55pm

Location: Shillman Hall 320

Dates: Sep 7, 2022 - Dec 17, 2022

Instructor: Kylie Bemis (she/her) | | Virtual Office Hours: See Piazza

Piazza: Questions and lecture material are handled via Piazza | Sign up at https://piazza.com/northeastern/fall2022/ds5110bemis

Canvas: Course schedule and assignments are available via Canvas | Log in at https://northeastern.instructure.com/courses/126660

Teams: Office hours are held virtually via Microsoft Teams | Log in at https://teams.northeastern.edu

Required Textbooks: R for Data Science (R4DS) by Wickham and Grolemund

Supplementary Textbooks: Text Mining with R (TMR) by Silge and Robinson, Advanced R (AdvR) by Wickham, R Packages (Rpkg) by Wickham

Academic integrity: Be familiar with the university’s academic integrity policy on cheating and plagiarism.

Overview

Data science is the discipline concerned with transformation, processing, management, and modeling of data for the purpose of extracting knowledge from raw observations. This course discusses the practical issues and techniques for data importing, tidying, transforming, and modeling. Programming is a cross-cutting aspect of the course. Students will gain experience with data science tools through short assignments. The course work includes a term project based on real-world data.

Topics

Policies

General

Please let me know if you use a different name or pronouns from what appears the class roster. You may use a preferred name on Piazza and when submitting assignments and exams, but please be consistent and inform the instructors. The Northeastern LGBTQA Center can provide resources for changing your name and gender marker in the Northeastern system.

Please reach out to me early if you have difficulty keeping up with class material or completing assignments for personal reasons. The We Care program at Northeastern University is a resource available to you in times of stress.

All students are expected to abide by the university’s academic integrity policy and respect Northeastern’s commitment to diversity and inclusion.

Northeastern University strictly prohibits discrimination or harassment on the basis of race, color, religion, religious creed, genetic information, sex, gender identity, sexual orientation, age, national origin, ancestry, veteran, or disability status. Please review Northeastern’s Title IX policy, which protects individuals from sex or gender-based discrimination, including discrimination based on gender-identity. Faculty members are required to report all allegations of sex/gender-based discrimination to the Title IX coordinator.

Please be kind and respectful to your fellow students regardless of identity or background. Students are expected to respect and use other students’ chosen names and pronouns.

Remote Instruction and COVID-19

This course is taught in-person. Students may attend class in-person or remotely. Remote students will be able to participate virtually via Zoom online meetings. All course content can be accessed and completed remotely. However, some content and assignments will require synchronous virtual attendance (i.e., during the regularly scheduled class time in the Boston time zone), such as project presentations.

Classes may be recorded at the discretion of the instructor. The instructor may move to teaching the course online if in-person attendance is very low or in the case of health emergencies.

You are strongly encouraged to wear a mask and practice safe social distancing in the classroom when possible.

Please do not come to class in-person if you are experiencing COVID-19 symptoms.

Technology

Piazza

Course administration, including all questions, course materials, and course announcements will be handled via Piazza.

Please do not email instructors or TAs directly – use Piazza for your questions and queries instead. This allows us to track all course-related correspondence in a single location.

General questions that may be useful to other students should be posted to the whole class. If your question is specific to you, or includes a partial solution, then post it to instructors only.

Please see this Stackoverflow guide for how to ask a good question.

Canvas

Assignments, quizzes, and grading will be administered via Canvas.

All assignments and quizzes will be posted on Canvas, and must be submitted on Canvas by the posted due date. Please do not email completed assignments or quizzes to instructors or TAs, or post them on Piazza.

Zoom

Classes will be broadcast synchronously via Zoom. Remote students can use Zoom to attend class virtually. The instructor may teach some class sessions fully remotely if the need arises.

Microsoft Teams

Virtual office hours will be held via Microsoft Teams. During scheduled office hours or by appointment, instructors and TAs will be available for live chat or video call on Microsoft Teams.

Homework

Five to six homework assignments are to be completed individually for this class. Each homework is due online via Canvas on the date scheduled on Canvas. (Please refer to the deadline on the actual assignment rather than the semester schedule, which is tentative and may not be updated if the assignment changes.)

Some aspects of the homework may be discussed with each other, but they should be completed individually, and your submitted work should be your own. Sharing of worked solutions will not be tolerated and will be considered cheating. Plagiarised solutions will receive a zero. Solutions with a very high degree of similarity with another past or current student’s will be considered plagiarism, and will be treated accordingly.

Quizzes

There will two cumulative quizzes during the semester. Both quizzes will be completed online via Canvas on the dates scheduled on Canvas.

Project

There will be a final project completed in small teams. Project teams will propose a data science project using real-world data, culminating in a presentation and written report at the end of the semester.

Project guidelines will be posted on Piazza and discussed in class approximately midway through the semester.

Peer review

Students will expected to provide oral and written feedback on their classmates’ projects, both on the scientific content and on the effectiveness of their communication.

Rubrics will be posted on Piazza and discussed in class.

Late work and grading

Late assignments will not be accepted. Extensions may be given on a case-by-case basis if requested at least 48 hours in advance of the due date with a reasonable justification.

Petitions for re-grades must be made in writing via Piazza private message no later than 1 week after receiving the original grade. The petition must clearly explain why a re-grading is justified. The new grade may be lower than the original grade.

Before petitioning the instructor for a re-grade, students should first contact the grader to make sure they understand why they lost points.

Grade scale

The grade in this class is distributed as follows:

Final grades will follow the following scale:

These scales are subject to change at the discretion of the instructor.