Program Related Skills
Academic courses in this program provide opportunities to develop both transferable and specific skillsets.
Check out MyCareerCentre to learn more on how to articulate skills you’ve developed in your program to employers and/or academic admissions committees in our Skills from your Academics module (under our ‘Assess Yourself’ section).
Need additional support? Book a career counselling or an employment strategy appointment to discuss how you can demonstrate these skills to employers.
Statistics graduates develop a variety of skills well-equipped for various industries and further education programs, including, but not limited to:
- Analytical & Critical Thinking: Ability to analyze complex statistical information, evaluate evidence and assumptions, and draw well‑reasoned conclusions to inform data-informed decision-making.
- Collaboration & Teamwork: Contribute effectively within interdisciplinary teams, working with individuals with diverse areas of expertise and team roles to address data-driven questions and shared goals.
- Communication: Effectively explain complex statistical findings and associated uncertainty to both technical and general audiences through written reports, data visualizations (e.g., graphs, dashboards), presentations, and professional communication platforms (e.g., email, collaborative digital tools).
- Data Management & Cleaning: Organize, clean, and prepare datasets for analysis, ensuring data quality, consistency, and appropriate documentation across analytical workflows.
- Ethical & Responsible Data Use: Demonstrate awareness of ethical considerations in statistical practice, including data privacy, bias, transparency, and the responsibility for accurate interpretation and communication of results.
- Experimental & Observational Design: Design and evaluate experiments, surveys, and observational studies by formulating testable hypotheses, identifying sources of variation and bias, and assessing limitations to support valid statistical inference using ethical data collection practices.
- Machine Learning & Predictive Modelling: Apply statistical and computational methods to build and evaluate predictive models, assess model performance, and understand the assumptions and limitations of data‑driven predictions.
- Problem Solving & Quantitative Reasoning: Apply quantitative reasoning to model complex problems, evaluate constraints and assumptions, and identify appropriate statistical approaches to addressing theoretical and practical questions.
- Programming & Statistical Tools: Use statistical programming languages and analytical tools (e.g., R, Python, SAS, SQL) for data cleaning, analysis, modelling, and visualization.
- Statistical Inference & Uncertainty: Draw conclusions from data using probability theory, confidence intervals, hypothesis testing, and model‑based reasoning, while quantifying uncertainty and variability in results.
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Updated June 2026