Statistical Consulting Practicum
General
- Graduate
- 5 learners; teams of 5
- 30 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 2/1 project matches
- Anywhere
- Academic experience
- Any
- Any
Categories
Skills
Project timeline
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January 30, 2023Experience start
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March 4, 2023Midterm report
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April 11, 2023Experience end
Timeline
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January 30, 2023Experience start
-
March 4, 2023Midterm report
Students will submit a report, discussing their exploratory analysis and any preliminary findings. They will include their proposed modelling strategy.
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April 11, 2023Experience end
Overview
- Details
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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.
- Learner skills
- Programming, Data analytics, Consulting, Data analysis
- Deliverables
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Deliverables are negotiable, and will seek to align the needs of the students and the organization.
Some final project deliverables might include:
- A 10-15 minute presentation on key findings and recommendations
- A detailed report including their research, analysis, insights and recommendations
- Analysis code
Project Examples
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 students’ 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?