Fall 2019
Course Information for Fall 2019
|
Instructor Information
Lectures
Labs |
Lab and Project Links
| # | Labs | Projects | Due Dates |
|---|---|---|---|
| 1 | Tools of the Trade | Creating Data | Tuesday, 10 September 2019 |
| 2 | Searching and Splitting | Extracting Information | Tuesday, 17 September 2019 |
| 3 | Modular Design and Lists | Calculating Thermoclines | Tuesday, 24 September 2019 |
| 4 | The Random Package and matplotlib | Population Variability Analysis | Tuesday 1 October 2019 |
| 5 | Lists | Simulating Elephant Population Management | Tuesday 8 October 2019 |
| 6 | Searching and Design of Experiments | Optimizing Simulations | Friday 18 October 2019 |
| 7 | Classes | Object-Oriented Simulation Design | Tuesday 29 October 2019 |
| 8 | Designing Classes | Object-Oriented Physics Simulation | Tuesday 5 November 2019 |
| 9 | Dictionaries and Inheritance | Pinball | Tuesday 12 November 2019 |
| 10 | Geometric Thinking | Pinball with Rotation | Tuesday 19 November 2019 |
| 11 | Designing Interaction | Something Interactive | Monday 9 December 2019 |
Project Guidelines
Much of what you learn in this course will come from implementing the ideas you learn in lecture. Each week in lab we will practice a new concept, which will then be used as a core part of the project. Each project will incorporate some kind of scientific data analysis or simulation. In all core CS courses, the defined part of the project will constitute about 85% of it. You can choose to stop there, or you can extend the project in ways that are interesting to you for additional credit. Each project will have some suggested extensions, but you are always free to choose your own.
A project will always have a coding part and an associated report. Your code should be well-written, well-commented, and efficient. The report gives you a chance to do two things: explain your code/design to others, and explain the results of your project. These are two different skills, both of which are important for any computer scientist or software developer. The first requires you to explain an algorithm or computational concept using natural language. This is necessary in order to communicate or explain ideas in a structured manner. The second requires you to examine the results of your code, analyze whether it is working, and explain the results. Note that the second part also helps you to ensure your code is working properly.
All projects will be graded out of 30 points. The code and quality comments constitute 21 points, the report constitutes 5 points, and you can earn up to an additional 4 points with extensions. A 30/30 project requires perfect code, a clear and well-written report, and meaningful or significant extensions. Note that doing lots of extensions will not make up for a poor report, code with errors, or code without comments.
The faculty and TAs are available to help you, especially with projects. Please follow the 30-minute rule: if you have not made any progress after 30 minutes of honest effort, stop what you are doing and ask a question of a TA or faculty. The point of the projects is not to be frustrated, but to make progress.
Success in the projects results from starting early, attending lecture and lab, making use of the course resources, and asking questions when you get stuck. All projects will be due Tuesday evening by midnight except as noted above. Projects submitted after the specified deadline will be graded without considering extensions. Any project work must be submitted within a week of the deadline to receive any credit. Submit what you have and move on to the next project.