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Statistical Consulting Practicum

Closed
York University
York University
Toronto, Ontario, Canada
Kelly Ramsay
Kelly Ramsay
Assistant Professor
4
Timeline
  • January 29, 2024
    Experience start
  • March 1, 2024
    Midterm
  • April 2, 2024
    Experience end
Experience
3/3 project matches
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries

Experience scope

Categories
Machine learning Data visualization Data analysis Data modelling Data science
Skills
programming data analytics consulting data analysis
Student goals and capabilities

Looking to elevate your organization, and bring it to the next level? Bring on students from York University to be your student-consultants, in a project-based experience. Students will work on one main project over the course of the semester, connecting with you as needed with virtual communication tools.

Students in this program/course will work in groups to complete a full data analysis from start to finish. They will be assigned a dataset from a given company and research questions. Students will also complete exploratory data analysis, propose and execute a modelling strategy to draw insights from the given data.

Students

Students
Graduate
Any level
12 students
Project
40 hours per student
Level UP Teams assign students to projects
Teams of 3
Expected outcomes and deliverables

Deliverables are negotiable, and will seek to align the needs of the students and the organization.

Some final project deliverables might include:

  1. A 20 minute presentation on key findings and recommendations
  2. A detailed report including their research, analysis, insights and recommendations
  3. Analysis code
Project timeline
  • January 29, 2024
    Experience start
  • March 1, 2024
    Midterm
  • April 2, 2024
    Experience end

Project Examples

Requirements

Students in groups of 5 will work with your company to identify your needs and provide actionable recommendations, based on their in-depth research and analysis.

Project activities that students can complete may include, but are not limited to:

  • Visualizations and summary statistics: A sample of visualizations and descriptive statistics they have examined, with brief explanation, demonstrating their main findings from the exploratory analysis.
  • Conduct proper data quality control, such as handling missing values, outliers, errors, and non-normalized fields
  • Conduct a multi-step EDA process with proper visualizations at each step, explaining why they used the visualizations they did and how the results informed and/or motivated their subsequent decisions
  • Generate, test, and interpret the results of informed hypotheses
  • Build predictive models, possibly using machine learning methods
  • Synthesize the partial results from individual analyses
  • Analyze performance and explore any shortcomings
  • Conduct a sensitivity analysis as applicable

Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

Be available for a quick phone/virtual call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.

Provide a dedicated contact person who is available for bi-weekly drop-ins to address studentsโ€™ questions as well as periodic messages over the duration of the project.

Provide an opportunity for students to present their work and receive feedback.

Provide relevant information/data as needed for the project.

How is your project relevant to the course?