Staff

Objectives

This course integrates the data acumen and visualization skills you developed in DATA 101 or 175 with the computational and mathematical/statistical skills you have been developing in other classes. We will emphasize data wrangling, predictive modeling, and visualization.

Upon completing this class, you should be able to work through each part of the data science lifecycle, including:

Communication

We will use the following communication tools:

Materials

Technology

  • GitHub
    • As part of our objective of reproducibility, we will be using git for distributing assignments, collaboration, and tracking progress.
  • RStudio Server

Textbooks

We will use the following materials. All are available freely online, but some may also be purchased in hardcover if desired.

Weekly expectations

Each week is the same(ish). Each week you will be expected to:

Lecture and Lab

  • Lectures are held Mondays and Wednesdays at 9-9:50am. If you have a laptop, please bring it.
    • On the first day of class (9/2), we will meet in SB 010. Please bring your laptops.
    • Based on how the Calvin community is doing managing Covid, we will decide whether to hold class in person or not. We may make more conservative decisions than the university requires overall.
    • So please check Teams for class location before coming in person. You may always join online.
  • Labs will be held Fridays at 9-9:50am, always on Teams.

Lab reports will be due on the following Monday.

Projects

You will complete two multi-week projects in this class.

In the Midterm Project, you will practice some parts of the data science lifecycle by reproducing a published visualization of your choice from source data. In the Final Project, you will additionally apply predictive analytics. You may choose to use the same or different dataset. Details about the projects

Final projects may be completed in teams of up to 3. Teams will have the following additional expectations:

  • Teams must submit a team contract about how they will work together
  • Teams must convince the instructional staff that each team member learned something substantial from completing the project.
  • Each team member must submit an assessment of how they and other team members fulfilled their contract.

Details of these expectations are forthcoming.

Grading

Unless otherwise arranged, grades will be weighted as follows:

Your lowest quiz score will get thrown out.

As the Calvin Academic Integrity Policy says, “At Calvin, the student-faculty relationship is based on trust and mutual respect.”

Data science is a fundamentally collaborative endeavor. Collaboration brings the benefits of multiple perspectives, needed to tackle complex problems faithfully and responsibly. But teamwork also brings the risk of one person doing all of the “learning” for the other. Thus:

Diversity and Inclusion

I came to Calvin because I wanted to explore what our Christian calling to “act justly” means in the context of data and the technologies that we use with it. Engaging that question wholeheartedly requires that each of us, me included, engage respectfully with perspectives very different from our own. For example, we must question those who abuse data for selfish gain, but we also must question the perspectives of those who challenge those abuses on purely secular grounds.

I intend for this class to be an environment where we equally respect people of every ethnicity, gender, socioeconomic background, political learning, religious background, etc. I will try to create that community by having us read diverse voices, engage with issues of importance to people unlike ourselves, and structure discussions that require students to engage respectfully with perspectives different from their own. I invite your help.

We will not always do this well. If you or someone else in this class is hurt by something I say or do in class, I would like to work to remedy it. I’ll welcome this feedback in whatever way is comfortable for you: in public, in private, via another person (such as our TA or my department chair, Keith Vander Linden), or via a report to Safer Spaces or the provost’s office.

Special Circumstances

Occasionally there are special circumstances that require that course policies be adjusted for a particular student. In such cases, it is the responsibility of the student to inform me of the situation as soon as possible, so that the appropriate arrangements can be made. This includes, but is not limited to, students with documented disabilities.

Calvin University has a continuing commitment to providing reasonable accommodations for students with documented disabilities. Like so many things this fall, the need for accommodations and the process for arranging them may be altered by the COVID-19 changes we are experiencing and the safety protocols currently in place. Students with disabilities who may need some accommodation in order to fully participate in this class are urged to contact Disability Services in the Center for Student Success () as soon as possible to explore what arrangements need to be made to assure access. The three of us (student, instructor, and Disability Services) will work together to come up with an appropriate solution.

We will give an incomplete grade (I) only in unusual circumstances, and only if those circumstances have been confirmed by the Student Life office.

Topics

About two weeks will be dedicated to each of the following topics:

See Moodle for details. We will also be discussing a variety of human contexts and ethics topics. We will curate this list of topics together in the first week of class.

License

Creative Commons License
This online work is licensed under a Creative Commons Attribution-ShareAlike 4.0 Internationale. Visit here for more information about the license.

Acknowledgments

A substantial amount of content for the first few weeks of this course is based on material from the “Data Science in a Box” (abbreviated “dsbox” in the materials here) project led by Dr. Mine Çetinkaya-Rundel.