Grading is a nasty side effect of mass learning and academia. We are in a class at a university and will have to manage this side effect. However, we do not have to let it control our learning, thinking, or this class. Learning and thinking should motivate each activity.

We will complete work in the following areas

  • Weekly reading (30%)
  • Case Study Preparation (5%)
  • Case Study (40%)
  • Teach one another (15%)
  • Visualization challenge(10%)

Weekly reading

To keep up with the conversation in class, you will need to keep up to date with the reading. We have a couple of quiz questions about the readings with some self-reporting on your reading completion.

Case Study

Our class is focused on 6 data case studies. This section will count for over 50% of your grade.

  • Preparation for the case study (5%): You will have two checkpoints for each project.
  • Case Study completion (40%): Each student will submit their final presentations based on the requirements of the case study.
  • Teach one another (10%): Each student will be responsible for presenting to another student and then provide questions and feedback on another project as a part of teaching one another.

Teach one another

This section includes three elements - 1) Tooltips presentations, 2) Case study presentations, and 3) Case study comments. Each is worth 5% of your overall grade. There will be one quiz that you can repeatedly take over the semester to document your completion.

Visualization challenge

We expect to provide an in-class and take-home challenge that you can start on the last day of class. It will cover three elements.

  1. A messy data set that requires you to describe its issues and what you would do to clean it up for use in visualization.
  2. A data journalism article review where you identify the strengths and weaknesses of the visualizations used to tell their story.
  3. A visualization request to be done in Tableau with the data set that we provide.

Semester deliverables

  1. A cover letter stating the key concepts and techniques that you learned during our projects and your goals to continue learning in this area - include a grade request that represents your knowledge and task completion
  2. A dream data science resume that covers the skills you desire upon graduation.