| Description | Due Date1 | Due Week | |
|---|---|---|---|
| Individual assignment 1 | Setting up your computer | Jan 18 | 2 |
| GitHub username quiz | Create a GitHub account (if you haven’t already) and let us know what it is | Jan 18 | 2 |
| Individual assignment 2 | Version control practice | Jan 25 | 3 |
| Individual assignment 3 | Dockerfile practice | Feb 8 | 5 |
| Mid-term exam2,3 | The midterm is a summative assessment | Feb 14 | 6 |
| Milestone 1 | Question, data & rough draft of analysis in one monolithic literate code document, reproducible environment | Mar 1 | 8 |
| Individual assignment 4 | Reproducible reports practice | Mar 8 | 9 |
| Milestone 2 | literate code document broken into scripts and a report & data analysis pipeline to stitch everything together | Mar 15 | 10 |
| Milestone 3 | functions abstracted to a file/module & tests, function documentation | Mar 29 | 12 |
| Peer review | review of another group's project | Apr 5 | 13 |
| Individual assignment 5 | Packaging practice | Apr 5 | 13 |
| Final project | package & CI (the full monty package - including docs) | Apr 11 | 14 |
| Team work | Reflection of how the group worked together, as well as individual performance | Apr 11 | 14 |
| Final exam2,3,4 | The Final Exam will include all the material covered in all the components of the course (cumulative). This is a summative assessment. | NA | NA |
| 1 Assignments are due at 23:59 Vancouver time unless otherwise stated. | |||
| 2 These are summative assessments, you can learn more about them here: https://www.cmu.edu/teaching/assessment/basics/formative-summative.html | |||
| 3 A higher final exam score can be used to replace a lower midterm exam score. | |||
| 4 You must pass the final to pass the course. | |||
Assessment schedule
This course includes a substantial group project component. You will work in randomly assigned groups of four for the project milestones. There are also individual assignments that act as stepping stones to the project milestones. Given that collaboration is so important in data science, a portion of your final grade will be an assessment of the evidence you provide that you were an effective and productive team member. A combination of peer evaluation and GitHub history will be used to evaluate this. Your individual knowledge on the course materials (concepts and practical skills) will be evaluated on two summative assessments (midterm and final exam).
Finally, this course is delivered in a blended format, with some pre-work (video watching or reading) expected to be done before each lecture. These will be provided in the course Canvas shell. Each in class lecture session will start with iClicker cloud questions to probe your understanding of the pre-lecture material and then we will work through demonstrations and exercises in class to build off of this.
In general, assignments will be due 11:59 PM on Saturdays. However, in the final week of classes, all assignments need to be submitted by the final day of classes, thus we have two alternative due dates that week.