A project-based course in statistical computing. Students will choose individual projects on computing topics related to statistical modeling and practice, including databases, parallel and cluster programming, big data frameworks (e.g. Spark or Hadoop), algorithms and data structures, numerical methods, and other topics based on student interest. The course will include introductions to each topic as well as student presentations on the results of their projects. Multiple programming languages will be supported.
Recommended prerequisite: 36-650 or 36-750
Note: Masters and undergraduate students should enroll in 36-651, PhD students in 36-751. There’s no difference between the sections, just bureaucratic accounting.
By the end of this course, students will be able to:
The main component of this course is a project, which you will work on for the entire mini. See the Course project page for details on the projects.
Along with the code itself, you will complete:
There are four components to the course grade: the project, the tutorial, project status updates, and the presentation. Students are also expected to attend class, participate in discussions, and review code from classmates (which I consider to be part of the project).
Statistics PhD students: Grades in this class are letter grades by default, but can be pass/fail by request. Email me your request so I don’t lose track.
Discussing projects with your classmates is allowed and encouraged, but it is important that every student get practice working on their own. This means that all the work you turn in must be your own. You must devise and write your own code and carry out your own tests.
You may use external sources (books, websites, papers) to
But external sources must be used to support your project, not to obtain your project. You may not use them to copy code snippets or algorithms to use in your project without attribution.
If you do use code from online or other sources, you must include code comments identifying the source.
Please talk to me if you have any questions about this policy. Any form of cheating is grounds for sanctions to be determined by the instructor, including grade penalties or course failure. Students taking the course pass/fail may have this status revoked. I am also obliged in these situations to report the incident to your academic program and the appropriate University authorities. Please refer to the University Policy on Academic Integrity.
If you have a disability and have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at firstname.lastname@example.org.
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