Xingye Qiao
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MATH 247 CS 207 Intro Data Science
MATH 448 Mathematical Statistics
MATH 457 Intro Statistical Learning
MATH 488P Principles of Data Science
MATH 502 Statistical Inference
MATH 531 Regression Model I
MATH 535 Stat Learning & Data Mining
MATH 540/1 Capstone Seminar
MATH 570 Applied Multivariate Analysis
MATH 590S Causal Inference
MATH 605 Theory of Machine Learning
DATA 500 Intro Data Analytics
DATA 501 Predictive & Inferential Analytics

Math 540-541 Capstone Seminar. Fall 2022 to Spring 2023.

  • Instructor: Xingye Qiao

  • Email: qiao@math.binghamton.edu

  • Office: WH-134

  • Meeting time & location: R 2:50 pm to 4:15 pm at WH 329.

  • Office hours: Friday 10 to 11 am excluding holidays. I should usually be there but you are recommended to email me to confirm just in case.

Course Description

The Capstone Seminar aims at providing students with an opportunity to integrate and apply the algorithms, methods and tools they have learned throughout the MA in Statistics program to identify and solve real-world statistics and data science problems, with a special emphasis on interdisciplinary work. Depending on the project's complexity, students will work individually or in small teams on a problem, typically specified by a faculty, industry, or governmental sponsor, or identified by the students under guidance of the faculty. Students will submit a consolidated report and give a presentation at the conclusion of the project. The course serves as a final preparation for students entering into the profession. Students get experience in working as teams, participating in project planning and scheduling, writing reports, giving presentations, and interpreting results in a professional manner. The course will also provide professional development training to the students, who be required to create a resume and put together a GitHub portfolio. Students will also be required to set up job search profiles, such as that served on LinkedIn.

Learning Outcomes

  • Identify relevant questions and objectives through client engagement and team discussion.

  • Demonstrate information literacy through a critical review of technical literature relevant for the management and analysis of data for their group project.

  • Synthesize and apply technical knowledge acquired in other courses to real-life problems.

  • Develop a project-appropriate plan and structure for data management.

  • Resolve group work allocation, leadership and cooperation issues.

  • Structure, manage and access one or more large, complex datasets.

  • Complete the analysis and interpretation of a complex, real-world data project.

  • Present the analysis and interpretation of a complex, real-world data project in both written reports and digital+oral presentations.

  • Develop skills to communicate with and balance the interests of multiple stakeholders (both technical and non-technical) on a project.

  • Develop a fundamental understanding of all aspects of the data science/machine learning pipeline, including in what ways each part of the pipeline can significantly affect the others.

  • Think broadly and critically about the implications of technical design choices: from data collection to assessment of the downstream socio-technical impact.

Milestones/deliverables

  • Prepare a professional resume (5%)

  • Set up a LinkedIn profile (5%)

  • Set up a GitHub portfolio showcasing previous projects and coursework (5%)

  • Prepare and practice a 30-second self-introduction (5%)

  • A presentation of a past project from a different course (10%)

  • A written project proposal (10%)

  • Project progress presentation (20%)

  • Project final presentation (20%)

  • Project final report (20%)

During the presentation of a past project, the student should start with the prepared self-introduction, advertise for their GitHub portforlio, and then present the materials.

Timeline

  • Week 1: Introduction; discussion statistics jobs and what statisticians do.

  • Week 2: Discuss project ideas. Critique on resume drafts.

  • Week 3: Discuss sources of data in general terms and ethical issues with their use. Students present project ideas. Finalize on resumes.

  • Week 4: Exploratory data analysis. Finalize on project ideas. Work on LinkedIn profiles.

  • Week 5: Students submit project proposal. Finalize on LinkedIn profiles. Set up GitHub portfolios.

  • Week 6: Work on statistical modelling. Rehearse self-introduction.

  • Week 7: Past-project presentations.

  • Week 8: Work on project

  • Week 9: Work on project

  • Week 10: Project progress presentations.

  • Week 11: Students will read and be prepared to discuss one or two articles on visualizing data provided by the Instructor.

  • Week 12: Work on project

  • Week 13: Students present their project

  • Week 14: Submit final reports to the Instructor. Add project to resume, LinkedIn profile and GitHub portfolio.