Academic Calendar - 2025
Western University Academic Calendar. - 2025
Statistical Sciences
An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests.
Extra Information: Offered in two formats: 3 lecture hours, or weekly online lectures and 2 in-class lab hours.
Course Weight: 0.50
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Statistical inference, experimental design, sampling design, confidence intervals and hypothesis tests for means and proportions, regression and correlation.
Antirequisite(s): All other courses or half courses in Introductory Statistics, except Statistical Sciences 1023A/B and Statistical Sciences 2037A/B, Data Science 1000A/B.
Prerequisite(s): Grade 12U Mathematics or Mathematics 0109A/B, Mathematics 0110A/B or Mathematics 1229A/B.
Extra Information: 3 lecture hours (Huron, King's). Note also that Statistical Sciences 1024A/B cannot be taken concurrently with any Introductory Statistics course. For a full list of Introductory Statistics courses please see:
https://www.westerncalendar.uwo.ca/Departments.cfm?DepartmentID=55&SelectedCalendar=Live&ArchiveID=
Course Weight: 0.50
This course is also offered at:
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Statistical inference, experimental design, sampling design, confidence intervals and hypothesis tests for means and proportions, regression and correlation.
Antirequisite(s): All other courses or half courses in Introductory Statistics, except Statistical Sciences 1023A/B and Statistical Sciences 2037A/B, Data Science 1000A/B.
Prerequisite(s): Grade 12U Mathematics or Mathematics 0109A/B, Mathematics 0110A/B or Mathematics 1229A/B.
Extra Information: 3 lecture hours (Huron, King's). Note also that Statistical Sciences 1024A/B cannot be taken concurrently with any Introductory Statistics course. For a full list of Introductory Statistics courses please see:
https://www.westerncalendar.uwo.ca/Departments.cfm?DepartmentID=55&SelectedCalendar=Live&ArchiveID=
Course Weight: 0.50
This course is also offered at:
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Descriptive statistics and graphs, probability and distributions. Sampling, hypothesis testing, and confidence intervals. Experimental design and analysis of variance. Regression and correlation, including multiple regression. Applications emphasized.
Antirequisite(s) at Main Campus: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Antirequisite(s) at King's: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B, the former Psychology 2810, the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B, Statistical Sciences 2037A/B if taken prior to Fall 2010.
Prerequisite(s): One full course or equivalent from: Applied Mathematics 1201A/B, Data Science 1000A/B, (Calculus 1000A/B or Calculus 1500A/B or Numerical and Mathematical Methods 1412A/B or the former Applied Mathematics 1412A/B), (Calculus 1301A/B or Calculus 1501A/B or Numerical and Mathematical Methods 1414A/B or the former Applied Mathematics 1414A/B), Mathematics 1600A/B, Mathematics 1225A/B, Mathematics 1228A/B, Mathematics 1229A/B, Mathematics 1230A/B, or the former Applied Mathematics 1413, the former Statistical Sciences 1024A/B.
Extra Information: 3 lecture hours. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 1.00
This course is also offered at:
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Descriptive statistics and graphs, probability and distributions. Sampling, hypothesis testing, and confidence intervals. Experimental design and analysis of variance. Regression and correlation, including multiple regression. Applications emphasized.
Antirequisite(s) at Main Campus: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Antirequisite(s) at King's: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B, the former Psychology 2810, the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B, Statistical Sciences 2037A/B if taken prior to Fall 2010.
Prerequisite(s): One full course or equivalent from: Applied Mathematics 1201A/B, Data Science 1000A/B, Statistical Sciences 1024A/B, Calculus 1000A/B or Calculus 1500A/B or Numerical and Mathematical Methods 1412A/B or the former Applied Mathematics 1412A/B, Calculus 1301A/B or Calculus 1501A/B or Numerical and Mathematical Methods 1414A/B or the former Applied Mathematics 1414A/B, Mathematics 1600A/B, Mathematics 1225A/B, Mathematics 1228A/B, Mathematics 1229A/B, Mathematics 1230A/B, or the former Applied Mathematics 1413.
Extra Information: 3 lecture hours. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 1.00
This course is also offered at:
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An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests. Emphasis will be placed on health-related applications.
Antirequisite(s) at Main campus: Data Science 1000A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Statistical Sciences 1023A/B.
Antirequisite(s) at Huron: All other courses or half courses in Introductory Statistics.
Extra Information: Offered in two formats: 3 lecture hours, or weekly online lectures and 2 in-class lab hours (Main); 3 lecture hours (Huron).
Note at Main campus: Cannot be taken for credit by students registered in the Faculty of Science and Schulich School of Medicine and Dentistry with the exception of students in Food and Nutrition.
Course Weight: 0.50
This course is also offered at:
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An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests. Emphasis will be placed on health-related applications.
Antirequisite(s) at Main campus: Data Science 1000A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Statistical Sciences 1023A/B.
Antirequisite(s) at Huron: All other courses or half courses in Introductory Statistics.
Extra Information: Offered in two formats: 3 lecture hours, or weekly online lectures and 2 in-class lab hours (Main); 3 lecture hours (Huron).
Note at Main campus: Cannot be taken for credit by students registered in the Faculty of Science and Schulich School of Medicine and Dentistry with the exception of students in Food and Nutrition.
Course Weight: 0.50
This course is also offered at:
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An introduction to statistics with emphasis on the applied probability models used in Electrical and Civil Engineering and elsewhere. Topics covered include samples, probability, probability distributions, estimation (including comparison of means), correlation and regression.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B, Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Prerequisite(s): 0.5 course from Numerical and Mathematical Methods 1412A/B, Calculus 1000A/B, Calculus 1500A/B, the former Applied Mathematics 1412A/B, plus 0.5 course from Numerical and Mathematical Methods 1414A/B, Calculus 1301A/B, Calculus 1501A/B, the former Applied Mathematics 1414A/B. The former Applied Mathematics 1413 may also be used to meet this 1.0 course prerequisite.
Extra Information: 3 lecture hours, 1 tutorial hour. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Applied Financial Modeling, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 0.50
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A data-driven introduction to statistics intended primarily for students in Chemical and Mechanical Engineering. Exploratory data analysis, probability, the Binomial, Poisson, Normal, Chi-Square and F distributions. Estimation, correlation and regression (model building and parameter estimation), analysis of variance, design of experiments.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B, Data Science 1000A/B or Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B,MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Prerequisite(s): 0.5 course from Numerical and Mathematical Methods 1412A/B, Calculus 1000A/B, Calculus 1500A/B, the former Applied Mathematics 1412A/B, plus 0.5 course from Numerical and Mathematical Methods 1414A/B, Calculus 1301A/B, Calculus 1501A/B, the former Applied Mathematics 1414A/B. The former Applied Mathematics 1413 may also be used to meet this 1.0 course prerequisite.
Extra Information: 3 lecture hours, 1 tutorial hour. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Applied Financial Modeling, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 0.50
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An introductory course in the application of statistical methods, intended for students in departments other than Statistical and Actuarial Sciences, Applied Mathematics, Mathematics, or students in the Faculty of Engineering. Topics include sampling, confidence intervals, analysis of variance, regression and correlation.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B, Statistical Sciences 2037A/B, Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2858A/B.
Prerequisite(s): 1.0 Mathematics course or equivalent numbered 1000 or above. Data Science 1000A/B or the former Statistical Sciences 1024A/B or Integrated Science 1001X can be used to meet 0.5 of the 1.0 mathematics course requirement.
Extra Information: 2 lecture hours, 3 lab hours. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling other than the Minor in Applied Statistics, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 0.50
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Modeling deterministic systems with differential equations: first and second order ODEs, systems of linear differential equations. Laplace transforms and moment generating functions. Modeling stochastic systems with Markov chains: discrete and continuous time chains, Chapman-Kolmogorov equations, ergodic theorems.
Antirequisite(s): The former Applied Mathematics 2503A/B.
Prerequisite(s): Calculus 2402A/B or (Calculus 2502A/B and Calculus 2503A/B) or (Calculus 2502A/B and Mathematics 2123A/B), Mathematics 1600A/B (or the former Linear Algebra 1600A/B) or Applied Mathematics 1411A/B, Statistical Sciences 2857A/B or Economics 2122A/B. In each course a minimum mark of 60% is required.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Probability axioms, conditional probability, Bayes' theorem. Random variables motivated by real data and examples. Parametric univariate models as data reduction and description strategies. Multivariate distributions, expectation and variance. Likelihood function will be defined and exploited as a means of estimating parameters in certain simple situations.
Prerequisite(s): 0.5 course from Calculus 1000A/B, Calculus 1500A/B, or Applied Mathematics 1412A/B, each with a minimum mark of 60%, plus 0.5 course from Calculus 1301A/B (minimum mark 85%), Calculus 1501A/B (minimum mark 60%), or Applied Mathematics 1414A/B (minimum mark 60%). The former Applied Mathematics 1413 with a minimum mark of 60% may also be used to meet this 1.0 course prerequisite.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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An introduction to the theory of statistics with strong links to data as well as its probabilistic underpinnings. Topics covered include estimation and hypothesis testing, sampling distributions, linear regression, experimental design, law of large numbers and central limit theorem.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2857A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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An introduction to programming using a high level language (currently R).
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2857A/B or a minimum mark of 70% in one of Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Economics 2222A/B, MOS 2242A/B, Psychology 2812A/B or the former Psychology 2810; and enrollment in a module offered by the departments of Applied Mathematics; Mathematics; and Statistical and Actuarial Sciences.
Pre-or Corequisite(s): Statistical Sciences 2858A/B for those using Statistical Sciences 2857A to meet the prerequisites.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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A continuation of the study of multivariate probability and stochastic processes. This course builds on the background developed in the second year courses, and focuses on the more advanced aspects of multivariate probability, namely transformations where the domain of random variables must be carefully considered.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B and in Statistical Sciences 2503A/B or Calculus 2503A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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A case study approach to how data are collected in science, social science and medicine, including the methods of designed experiments, sample surveys, observational studies and administrative records.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B or a minimum mark of 70% in one of Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Economics 2222A/B, MOS 2242A/B, Psychology 2812A/B or the former Psychology 2810; and enrollment in a module offered by the departments of Applied Mathematics; Mathematics; and Statistical and Actuarial Sciences.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Point estimation: sufficiency, completeness, consistency, unbiasedness, Cramer-Rao inequality, Rao-Blackwell theorem, Hypotheses tests: uniformly most powerful tests, likelihood ratio tests.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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Simple and multiple linear regression models and their use to model data using computing including model specification and assumptions, inference and estimation, use of indicator variables, regression diagnostics, model building and selection. Introduction to forecasting and time series.
Antirequisite(s): Economics 2123A/B and Economics 2223A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B.
Pre- or Corequisite(s): Statistical Sciences 2864A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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Estimation and tests for generalized linear models, including residual analysis and the use of statistical packages. Logistic regression, log-linear models. Additional topics may include generalized estimating equations, quasi-likelihood and generalized additive models.
Prerequisite(s): Statistical Sciences 3859A/B with at least 60%.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Applied linear modelling emphasizing data analysis using software including statistical inference review, visualization, multiple regression, logistic regression, and extensions. Core topics include assumptions, estimation, confidence/prediction intervals, hypothesis testing, diagnostics, indicator variables, cross validation, prediction, model building and model assessment. Other topics may include random effects or smoothing methods.
Antirequisite(s): Statistical Sciences 3859A/B, Statistical Sciences 3860A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B or a minimum mark of 70% in one of Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Economics 2222A/B, MOS 2242A/B, Psychology 2812A/B or the former Psychology 2810. Enrollment in a module offered by either the Department of Statistical and Actuarial Sciences, Mathematics or Applied Mathematics.
Pre-or Corequisite(s): Statistical Sciences 2864A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Continuous-time Markov chains, applications to phase-type distributions, Markov chain Monte Carlo simulation and queuing theory.
Antirequisite(s): The former Statistical Sciences 3652A/B, former Statistical Sciences 4652A/B, former Statistical Sciences 4657A/B and former Statistical Sciences 4737A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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An introduction to data analytics consulting in the context of Problem, Plan, Data, Analysis and Conclusion, including interpersonal techniques; communication; teamwork; project management; copyright, intellectual property, compensation and negotiation; robust data analysis; and ethics. A large portion of the course will be conducted in a seminar format with student participation.
Prerequisite(s): Statistical Sciences 3859A/B with at least 60%. Registration in fourth year of the Honours Specialization in Data Science or Honours Specialization in Statistics modules.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Completely randomized designs, randomized complete and incomplete block designs, factorial and fractional factorial designs, latin square designs, hierarchical designs, random and fixed effect models.
Antirequisite(s): The former Statistical Sciences 3846A/B.
Prerequisite(s): Statistical Sciences 3843A/B or Statistical Sciences 2858A/B and Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Modern methods of data analysis including linear and generalized linear models, modern nonparametric regression, principal component analysis, multilevel modelling and bootstrapping.
Prerequisite(s): A minimum mark of 60% in both Statistical Sciences 3843A/B and Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Simple random sampling with and without replacement, stratification, systematic sampling, cluster and multistage clustering, ratio and regression estimation, models in surveys, survey design, estimation and analysis.
Antirequisite(s): The former Statistical Sciences 3853F/G.
Prerequisite(s): Statistical Sciences 3859A/B or Statistical Sciences 3843A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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A review of multiple regression including assumptions, estimation and inference, diagnostics, and modelling with factors. Variable selection techniques including cross-validation. Smoothing techniques, generalized additive models, and the incorporation of random effects and/or serial auto-correlated error structures.
Prerequisite(s): Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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ARIMA models, seasonality, dynamic regression, model building using an interactive computer package, forecasting, intervention analysis, control, applications in econometrics, business, and other areas.
Antirequisite(s): The former Statistical Sciences 3861A/B.
Prerequisite(s): A minimum mark of 60% in both Statistical Sciences 3858A/B and Statistical Sciences 2864A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Review of fundamental concepts in statistical computing, including programming, optimization methods and Monte Carlo simulations. A selection of advanced topics such as bootstrapping, robust methods, statistical graphics, Markov chain Monte Carlo, nonlinear regression, relational databases, time series analysis, and spatial statistics.
Prerequisite(s): Statistical Sciences 2864A/B and Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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A course description will be available from the department at the time of registration.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B or permission of the department.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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A course description will be available from the department at the time of registration.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B and permission of the department.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Statistical Sciences 4960F/G BUSINESS SKILLS FOR FINANCIAL PROFESSIONALS, DATA SCIENTISTS AND OTHER QUANTITATIVE PROFESSIONALS
This course aims to develop important business skills that are often not emphasized in the formal education of quantitative financial professionals. The course focuses on four main topic areas: how businesses work, financial statement analysis, oral and written communications skills, and leadership and people management.
Prerequisite(s): Registration in fourth year of an Actuarial Science, Data Science, Statistics or
Financial Modeling module.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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The student will work on a project under faculty supervision. The project may involve an extension, or more detailed coverage, of material presented in other courses. Credit for the course will involve a written report as well as an oral presentation.
Prerequisite(s): Registration in the fourth year of the Honours Specialization in Actuarial Science, Statistics, or Financial Modelling. Students must have a modular course average of at least 80% and must find a faculty member to supervise the project.
Course Weight: 0.50
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Statistical Sciences
An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests.
Extra Information: Offered in two formats: 3 lecture hours, or weekly online lectures and 2 in-class lab hours.
Course Weight: 0.50
Return to top
Statistical inference, experimental design, sampling design, confidence intervals and hypothesis tests for means and proportions, regression and correlation.
Antirequisite(s): All other courses or half courses in Introductory Statistics, except Statistical Sciences 1023A/B and Statistical Sciences 2037A/B, Data Science 1000A/B.
Prerequisite(s): Grade 12U Mathematics or Mathematics 0109A/B, Mathematics 0110A/B or Mathematics 1229A/B.
Extra Information: 3 lecture hours (Huron, King's). Note also that Statistical Sciences 1024A/B cannot be taken concurrently with any Introductory Statistics course. For a full list of Introductory Statistics courses please see:
https://www.westerncalendar.uwo.ca/Departments.cfm?DepartmentID=55&SelectedCalendar=Live&ArchiveID=
Course Weight: 0.50
This course is also offered at:
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Statistical inference, experimental design, sampling design, confidence intervals and hypothesis tests for means and proportions, regression and correlation.
Antirequisite(s): All other courses or half courses in Introductory Statistics, except Statistical Sciences 1023A/B and Statistical Sciences 2037A/B, Data Science 1000A/B.
Prerequisite(s): Grade 12U Mathematics or Mathematics 0109A/B, Mathematics 0110A/B or Mathematics 1229A/B.
Extra Information: 3 lecture hours (Huron, King's). Note also that Statistical Sciences 1024A/B cannot be taken concurrently with any Introductory Statistics course. For a full list of Introductory Statistics courses please see:
https://www.westerncalendar.uwo.ca/Departments.cfm?DepartmentID=55&SelectedCalendar=Live&ArchiveID=
Course Weight: 0.50
This course is also offered at:
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Descriptive statistics and graphs, probability and distributions. Sampling, hypothesis testing, and confidence intervals. Experimental design and analysis of variance. Regression and correlation, including multiple regression. Applications emphasized.
Antirequisite(s) at Main Campus: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Antirequisite(s) at King's: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B, the former Psychology 2810, the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B, Statistical Sciences 2037A/B if taken prior to Fall 2010.
Prerequisite(s): One full course or equivalent from: Applied Mathematics 1201A/B, Data Science 1000A/B, (Calculus 1000A/B or Calculus 1500A/B or Numerical and Mathematical Methods 1412A/B or the former Applied Mathematics 1412A/B), (Calculus 1301A/B or Calculus 1501A/B or Numerical and Mathematical Methods 1414A/B or the former Applied Mathematics 1414A/B), Mathematics 1600A/B, Mathematics 1225A/B, Mathematics 1228A/B, Mathematics 1229A/B, Mathematics 1230A/B, or the former Applied Mathematics 1413, the former Statistical Sciences 1024A/B.
Extra Information: 3 lecture hours. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 1.00
This course is also offered at:
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Descriptive statistics and graphs, probability and distributions. Sampling, hypothesis testing, and confidence intervals. Experimental design and analysis of variance. Regression and correlation, including multiple regression. Applications emphasized.
Antirequisite(s) at Main Campus: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Antirequisite(s) at King's: All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B, the former Psychology 2810, the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B, Statistical Sciences 2037A/B if taken prior to Fall 2010.
Prerequisite(s): One full course or equivalent from: Applied Mathematics 1201A/B, Data Science 1000A/B, Statistical Sciences 1024A/B, Calculus 1000A/B or Calculus 1500A/B or Numerical and Mathematical Methods 1412A/B or the former Applied Mathematics 1412A/B, Calculus 1301A/B or Calculus 1501A/B or Numerical and Mathematical Methods 1414A/B or the former Applied Mathematics 1414A/B, Mathematics 1600A/B, Mathematics 1225A/B, Mathematics 1228A/B, Mathematics 1229A/B, Mathematics 1230A/B, or the former Applied Mathematics 1413.
Extra Information: 3 lecture hours. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 1.00
This course is also offered at:
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An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests. Emphasis will be placed on health-related applications.
Antirequisite(s) at Main campus: Data Science 1000A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Statistical Sciences 1023A/B.
Antirequisite(s) at Huron: All other courses or half courses in Introductory Statistics.
Extra Information: Offered in two formats: 3 lecture hours, or weekly online lectures and 2 in-class lab hours (Main); 3 lecture hours (Huron).
Note at Main campus: Cannot be taken for credit by students registered in the Faculty of Science and Schulich School of Medicine and Dentistry with the exception of students in Food and Nutrition.
Course Weight: 0.50
This course is also offered at:
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An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests. Emphasis will be placed on health-related applications.
Antirequisite(s) at Main campus: Data Science 1000A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Statistical Sciences 1023A/B.
Antirequisite(s) at Huron: All other courses or half courses in Introductory Statistics.
Extra Information: Offered in two formats: 3 lecture hours, or weekly online lectures and 2 in-class lab hours (Main); 3 lecture hours (Huron).
Note at Main campus: Cannot be taken for credit by students registered in the Faculty of Science and Schulich School of Medicine and Dentistry with the exception of students in Food and Nutrition.
Course Weight: 0.50
This course is also offered at:
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An introduction to statistics with emphasis on the applied probability models used in Electrical and Civil Engineering and elsewhere. Topics covered include samples, probability, probability distributions, estimation (including comparison of means), correlation and regression.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B, Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Prerequisite(s): 0.5 course from Numerical and Mathematical Methods 1412A/B, Calculus 1000A/B, Calculus 1500A/B, the former Applied Mathematics 1412A/B, plus 0.5 course from Numerical and Mathematical Methods 1414A/B, Calculus 1301A/B, Calculus 1501A/B, the former Applied Mathematics 1414A/B. The former Applied Mathematics 1413 may also be used to meet this 1.0 course prerequisite.
Extra Information: 3 lecture hours, 1 tutorial hour. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Applied Financial Modeling, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 0.50
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A data-driven introduction to statistics intended primarily for students in Chemical and Mechanical Engineering. Exploratory data analysis, probability, the Binomial, Poisson, Normal, Chi-Square and F distributions. Estimation, correlation and regression (model building and parameter estimation), analysis of variance, design of experiments.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B, Data Science 1000A/B or Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B,MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Prerequisite(s): 0.5 course from Numerical and Mathematical Methods 1412A/B, Calculus 1000A/B, Calculus 1500A/B, the former Applied Mathematics 1412A/B, plus 0.5 course from Numerical and Mathematical Methods 1414A/B, Calculus 1301A/B, Calculus 1501A/B, the former Applied Mathematics 1414A/B. The former Applied Mathematics 1413 may also be used to meet this 1.0 course prerequisite.
Extra Information: 3 lecture hours, 1 tutorial hour. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics, the Minor in Applied Financial Modeling, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 0.50
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An introductory course in the application of statistical methods, intended for students in departments other than Statistical and Actuarial Sciences, Applied Mathematics, Mathematics, or students in the Faculty of Engineering. Topics include sampling, confidence intervals, analysis of variance, regression and correlation.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B, Statistical Sciences 2037A/B, Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2858A/B.
Prerequisite(s): 1.0 Mathematics course or equivalent numbered 1000 or above. Data Science 1000A/B or the former Statistical Sciences 1024A/B or Integrated Science 1001X can be used to meet 0.5 of the 1.0 mathematics course requirement.
Extra Information: 2 lecture hours, 3 lab hours. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling other than the Minor in Applied Statistics, the Minor in Data Science, or the Certificate in Data Science.
Course Weight: 0.50
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Modeling deterministic systems with differential equations: first and second order ODEs, systems of linear differential equations. Laplace transforms and moment generating functions. Modeling stochastic systems with Markov chains: discrete and continuous time chains, Chapman-Kolmogorov equations, ergodic theorems.
Antirequisite(s): The former Applied Mathematics 2503A/B.
Prerequisite(s): Calculus 2402A/B or (Calculus 2502A/B and Calculus 2503A/B) or (Calculus 2502A/B and Mathematics 2123A/B), Mathematics 1600A/B (or the former Linear Algebra 1600A/B) or Applied Mathematics 1411A/B, Statistical Sciences 2857A/B or Economics 2122A/B. In each course a minimum mark of 60% is required.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Probability axioms, conditional probability, Bayes' theorem. Random variables motivated by real data and examples. Parametric univariate models as data reduction and description strategies. Multivariate distributions, expectation and variance. Likelihood function will be defined and exploited as a means of estimating parameters in certain simple situations.
Prerequisite(s): 0.5 course from Calculus 1000A/B, Calculus 1500A/B, or Applied Mathematics 1412A/B, each with a minimum mark of 60%, plus 0.5 course from Calculus 1301A/B (minimum mark 85%), Calculus 1501A/B (minimum mark 60%), or Applied Mathematics 1414A/B (minimum mark 60%). The former Applied Mathematics 1413 with a minimum mark of 60% may also be used to meet this 1.0 course prerequisite.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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An introduction to the theory of statistics with strong links to data as well as its probabilistic underpinnings. Topics covered include estimation and hypothesis testing, sampling distributions, linear regression, experimental design, law of large numbers and central limit theorem.
Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B and Data Science 1000A/B or the former Statistical Sciences 1024A/B): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2857A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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An introduction to programming using a high level language (currently R).
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2857A/B or a minimum mark of 70% in one of Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Economics 2222A/B, MOS 2242A/B, Psychology 2812A/B or the former Psychology 2810; and enrollment in a module offered by the departments of Applied Mathematics; Mathematics; and Statistical and Actuarial Sciences.
Pre-or Corequisite(s): Statistical Sciences 2858A/B for those using Statistical Sciences 2857A to meet the prerequisites.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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A continuation of the study of multivariate probability and stochastic processes. This course builds on the background developed in the second year courses, and focuses on the more advanced aspects of multivariate probability, namely transformations where the domain of random variables must be carefully considered.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B and in Statistical Sciences 2503A/B or Calculus 2503A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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A case study approach to how data are collected in science, social science and medicine, including the methods of designed experiments, sample surveys, observational studies and administrative records.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B or a minimum mark of 70% in one of Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Economics 2222A/B, MOS 2242A/B, Psychology 2812A/B or the former Psychology 2810; and enrollment in a module offered by the departments of Applied Mathematics; Mathematics; and Statistical and Actuarial Sciences.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Point estimation: sufficiency, completeness, consistency, unbiasedness, Cramer-Rao inequality, Rao-Blackwell theorem, Hypotheses tests: uniformly most powerful tests, likelihood ratio tests.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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Simple and multiple linear regression models and their use to model data using computing including model specification and assumptions, inference and estimation, use of indicator variables, regression diagnostics, model building and selection. Introduction to forecasting and time series.
Antirequisite(s): Economics 2123A/B and Economics 2223A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B.
Pre- or Corequisite(s): Statistical Sciences 2864A/B.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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Estimation and tests for generalized linear models, including residual analysis and the use of statistical packages. Logistic regression, log-linear models. Additional topics may include generalized estimating equations, quasi-likelihood and generalized additive models.
Prerequisite(s): Statistical Sciences 3859A/B with at least 60%.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Applied linear modelling emphasizing data analysis using software including statistical inference review, visualization, multiple regression, logistic regression, and extensions. Core topics include assumptions, estimation, confidence/prediction intervals, hypothesis testing, diagnostics, indicator variables, cross validation, prediction, model building and model assessment. Other topics may include random effects or smoothing methods.
Antirequisite(s): Statistical Sciences 3859A/B, Statistical Sciences 3860A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B or a minimum mark of 70% in one of Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Economics 2222A/B, MOS 2242A/B, Psychology 2812A/B or the former Psychology 2810. Enrollment in a module offered by either the Department of Statistical and Actuarial Sciences, Mathematics or Applied Mathematics.
Pre-or Corequisite(s): Statistical Sciences 2864A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Continuous-time Markov chains, applications to phase-type distributions, Markov chain Monte Carlo simulation and queuing theory.
Antirequisite(s): The former Statistical Sciences 3652A/B, former Statistical Sciences 4652A/B, former Statistical Sciences 4657A/B and former Statistical Sciences 4737A/B.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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An introduction to data analytics consulting in the context of Problem, Plan, Data, Analysis and Conclusion, including interpersonal techniques; communication; teamwork; project management; copyright, intellectual property, compensation and negotiation; robust data analysis; and ethics. A large portion of the course will be conducted in a seminar format with student participation.
Prerequisite(s): Statistical Sciences 3859A/B with at least 60%. Registration in fourth year of the Honours Specialization in Data Science or Honours Specialization in Statistics modules.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Completely randomized designs, randomized complete and incomplete block designs, factorial and fractional factorial designs, latin square designs, hierarchical designs, random and fixed effect models.
Antirequisite(s): The former Statistical Sciences 3846A/B.
Prerequisite(s): Statistical Sciences 3843A/B or Statistical Sciences 2858A/B and Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Modern methods of data analysis including linear and generalized linear models, modern nonparametric regression, principal component analysis, multilevel modelling and bootstrapping.
Prerequisite(s): A minimum mark of 60% in both Statistical Sciences 3843A/B and Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Simple random sampling with and without replacement, stratification, systematic sampling, cluster and multistage clustering, ratio and regression estimation, models in surveys, survey design, estimation and analysis.
Antirequisite(s): The former Statistical Sciences 3853F/G.
Prerequisite(s): Statistical Sciences 3859A/B or Statistical Sciences 3843A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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A review of multiple regression including assumptions, estimation and inference, diagnostics, and modelling with factors. Variable selection techniques including cross-validation. Smoothing techniques, generalized additive models, and the incorporation of random effects and/or serial auto-correlated error structures.
Prerequisite(s): Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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ARIMA models, seasonality, dynamic regression, model building using an interactive computer package, forecasting, intervention analysis, control, applications in econometrics, business, and other areas.
Antirequisite(s): The former Statistical Sciences 3861A/B.
Prerequisite(s): A minimum mark of 60% in both Statistical Sciences 3858A/B and Statistical Sciences 2864A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Review of fundamental concepts in statistical computing, including programming, optimization methods and Monte Carlo simulations. A selection of advanced topics such as bootstrapping, robust methods, statistical graphics, Markov chain Monte Carlo, nonlinear regression, relational databases, time series analysis, and spatial statistics.
Prerequisite(s): Statistical Sciences 2864A/B and Statistical Sciences 3859A/B.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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A course description will be available from the department at the time of registration.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B or permission of the department.
Extra Information: 3 lecture hours, 1 tutorial hour.
Course Weight: 0.50
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A course description will be available from the department at the time of registration.
Prerequisite(s): A minimum mark of 60% in Statistical Sciences 3657A/B and permission of the department.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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Statistical Sciences 4960F/G BUSINESS SKILLS FOR FINANCIAL PROFESSIONALS, DATA SCIENTISTS AND OTHER QUANTITATIVE PROFESSIONALS
This course aims to develop important business skills that are often not emphasized in the formal education of quantitative financial professionals. The course focuses on four main topic areas: how businesses work, financial statement analysis, oral and written communications skills, and leadership and people management.
Prerequisite(s): Registration in fourth year of an Actuarial Science, Data Science, Statistics or
Financial Modeling module.
Extra Information: 3 lecture hours.
Course Weight: 0.50
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The student will work on a project under faculty supervision. The project may involve an extension, or more detailed coverage, of material presented in other courses. Credit for the course will involve a written report as well as an oral presentation.
Prerequisite(s): Registration in the fourth year of the Honours Specialization in Actuarial Science, Statistics, or Financial Modelling. Students must have a modular course average of at least 80% and must find a faculty member to supervise the project.