<2022-01-18 Tue> - <2022-04-29 Fri>
- Time and Location
- Time: Monday 09:20AM - 12:05PM, AUST 434
- First two weeks online through this WebEx Link
- Instructor: HaiYing (Ben) Wang
- Office Hours: WeMo 1:00 - 2:00PM via this WebEx Link, or by appointment
- Office Phone: (860) 486-6142
- Email: haiying.wang@uconn.edu
- GitHub: Ossifragus
- Grader: Boyi Zhang
- Email: boyi.zhang@uconn.edu
- GitHub: ReleaseU
- Course objectives: The course will provide a broad overview of principles and practice of statistical computing in data science.
- Course Description: Students who complete the course successfully will be proficient in current statistical computing languages, data management methods, and working in a collaborative development environment. The approach in this class is to learn by doing. Like any topic, and especially when learning a new language, it is critical to practice a lot and often. This course will focus both on promoting individual student learning and fostering collaborative team work to enable students to achieve goals in a domain of interest. Through the course project, students will learn how to convey results effectively to people with little or no background in statistics, and demonstrate an understanding of ethical issues in data science.
- The Julia programming language will be introduced and used.
- Julia is freely available at https://julialang.org/
- Visual Studio Code is freely available at https://code.visualstudio.com/
- Julia-vscode: https://github.com/julia-vscode/julia-vscode
- GitHub: https://github.com/STAT5125-UConn/2022Spring
Students should “watch” this repository to receive notifications for any updates. It is highly recommended that students “fork” this repository to make “pull requests”. Course materials such as notes and source code files will be posted at this repository. Students will be given access to this repository after they have registered at github.com and accepted the invitation to homework through the invitation link:https://classroom.github.com/a/P95_VWv-
GitHub Classroom will be used to assign and collect homework and project.
| Category | Weight |
|---|---|
| Homework assignment | 30% |
| Participation and quizzes | 20% |
| Project | 50% |
- Homework:
- Homework assignments will be given during the semester. Each homework consists two steps: in the first step, students submit they own work; in the second step students submit an assessment form on another student’s work.
- Students may consult amongst themselves or with the instructor, but each student must submit his/her own work.
- It is recommended that students submit homework in GitHub Flavored Markdown (GFM), although other GitHub compatible file types such as Jupyter Notebooks and pdf files are also acceptable. Students should also submit the Julia source codes.
- No credit will be given for submitted assignments exhibiting duplication or copying of solutions (from peers or existing solutions).
- Late submissions within a 24-hour grace period will only be worth 50% - 95% of the points. Submissions beyond 24 hours will not be graded and will receive no credit. No homework grade will be dropped.
- All homework assignments must be typed and submitted through the GitHub.
- Participation and Quizzes:
- We will have “active learning” in the classroom via discussion, Q&A, and problem solving.
- There will be pop quizzes and the dates will be randomly selected. If you miss a quiz, the only circumstance that you can make it up is you have notified me of your time conflict with the class in advance. And it must be made up within 24 hours.
- Final Project
- There will be a final project that requires both an oral presentation and a written report. More details about the project will be provided separately.
- An brief introduction to Git and GitHub.
- An express introduction to Julia
- Reproducible report generating
- Stochastic and statistical simulation
- Data processing with DataFrames
- Visualization
- Optimization
- Parallel and Distinguished computing
A fundamental tenet of all educational institutions is academic honesty; academic work depends upon respect for and acknowledgement of the research and ideas of others. Misrepresenting someone else’s work as one’s own is a serious offense in any academic setting and it will not be condoned. Academic misconduct includes, but is not limited to, providing or receiving assistance in a manner not authorized by the instructor in the creation of work to be submitted for academic evaluation (e.g. papers, projects, and examinations); any attempt to influence improperly (e.g. bribery, threats) any member of the faculty, staff, or administration of the University in any matter pertaining to academics or research; presenting, as one’s own,the ideas or words of another for academic evaluation; doing unauthorized academic work for which another person will receive credit or be evaluated; and presenting the same or substantially the same papers or projects in two or more courses without the explicit permission of the instructors involved. A student who knowingly assists another student in committing an act of academic misconduct shall be equally accountable for the violation, and shall be subject to the sanctions and other remedies described in The Student Code.
- Counseling and Mental Health Services 486-4705 (after hours, use 486-3427)
- Career Services 486-3013
- Alcohol and Other Drug Services 486-9431
- Dean of Students Office 486-3426
- Center for Students with Disabilities 486-2020 (voice), 486-2077 (TDD)
- Online Course Support: https://achieve.uconn.edu/online-course/
- Keep Learning: https://onlinestudent.uconn.edu/keeplearning/
The instructor reserves the right to make changes to the syllabus as necessitated by circumstances.