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The course grade consists of the following components and percentages:
Homework: 30%
Class participation: 15%
Course project: 55%
There will be about 4 homeworks; The homeworks will be assigned in the first part of the course and students will submit their homework using GitHub. You must have created a GitHub account before class starts. To receive full credit, homework must be posted by 11:59 PM Pacific Time on the due date. For Winter 2020, homework assignments will typically be due on Tuesdays unless otherwise noted.
The instructors and TAs will then provide feedback via GitHub issues. You will have a week to respond to the feedback by a homework revision to make up any points not received on the first submission. If you choose to go this route (and you should), you will need to @mention the TA or instructor who first reviewed your homework.Twenty percent will be deducted from homework received up to a week after its due date. No credit will be given for homework more than one week late (unless there is special permission from the instructors). All homework, including those with extensions, must be submitted no later than midnight March 17, 2020.
Our expectation is that everything you type in a Python notebook is your own work. Any instances of "copy-paste" from the web or another person's notebook should be clearly cited. Of course you may look at examples but it is our strong preference that you refrain from copy-paste and type everything in. There is a learning reason for this, which we are happy to discuss in office hours or on slack.
We will follow UW academic misconduct policy for any suspected instances of cheating on HW or projects. Any confirmed instance of cheating results in a zero on a HW assignment. Any 2nd confirmed instance of cheating results in a zero for the entire course grade.
Class participation will be graded based on project related presentations, e.g. stand ups, technology reviews and project summaries.
A significant portion of the course grade is based on a single joint project that includes concepts from both DIRECT classes. This will be an open ended project related to clean energy and/or molecular data science and require, at minimum, creation of a new software project and application of one or more Data Science methods. Projects will be graded based on:
- the poster presentation
- documentation (especially use cases & function and design specifications)
- the features implemented
- appropriate use of data science methodology
- code quality
Grades for the project will be assigned to the entire team, although there will be adjustments for significant discrepancies in the relative contributions of team members. The latter will be assessed by commit logs and by a peer assesment survey students will take at the end of the quarter. Separate, defined graded components will be given for SEDS and DSMfCER.
Project deadline and poster presentation dates are still to be determined, but will be posted soon.