Academic Calendar - 2024
Western University Academic Calendar. - 2024
Data Science
Students will learn how to visualize and analyze continuous and categorical data from various domains, using modern data science tools. Concepts of distributions, sampling, estimation, confidence intervals, experimental design, inference, correlation will be introduced in a practical, data-driven way.
Antirequisite(s): Statistical Sciences 1023A/B, Statistical Sciences 2037A/B, Statistical Sciences 2857A/B, the former Statistical Sciences 1024A/B.
Prerequisite(s): One or more of Ontario Secondary School MCV4U, MHF4U, MDM4U, Mathematics 0109A/B, Mathematics 0110A/B, Mathematics 1229A/B, or equivalent.
Extra Information: 3 lecture hours/week, 1 laboratory hour/week.
Course Weight: 0.50
Return to top
Programming for Data Science is intended for students with little or no background in programming. Design and analysis of algorithms and their implementation as modular, reliable, well-documented programs written in a modern programming language.
Antirequisite(s): Computer Science 1025A/B, Computer Science 1026A/B, Computer Science 2120A/B, Engineering Science 1036A/B, Digital Humanities 2200A/B.
Extra Information: 3 lecture hours/week, 2 laboratory hours/week.
Course Weight: 0.50
Return to top
Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.
Antirequisite(s): Integrated Science 2002B.
Prerequisite(s): 1.0 courses from Mathematics, Calculus, or Applied Mathematics (numbered 1000 and higher) with a minimum mark of 60%. Data Science 1000A/B (with a minimum grade of 60%) can be used to meet 0.5 of the 1.0 mathematics course requirements.
Extra Information: 2 lecture hours/week, 2 laboratory hours/week.
Course Weight: 0.50
Return to top
Mathematical background for students wanting to take Data Science 3000A/B, but missing background in linear algebra and calculus. Vector and matrix algebra, norms, linear dependence, inverses, vector spaces, eigenvectors and eigenvalues, Gradients, Hessians, basics of optimization. All concepts are explained in the context of data science examples.
Antirequisite(s): Mathematics 1600A/B, Mathematics 1700A/B, Numerical and Mathematical Methods 1411A/B, the former Applied Mathematics 1411A/B.
Prerequisite(s): 1.0 courses from Mathematics, Calculus, or Applied Mathematics (1000 and higher) with a minimal grade of 60%. Data Science 2000A/B or Integrated Science 2002B can be used to fulfil 0.5 of the requirements.
Extra Information: 3 lecture hours/week, 1 tutorial hour/week.
Course Weight: 0.50
Return to top
Basic principles of machine learning (estimation, optimization, prediction, generalization, bias-variance trade-off, regularization) in the context of supervised (linear models, decision trees, deep neuronal networks) and unsupervised (clustering and dimensionality reduction) statistical learning techniques. The course emphasizes the ability to apply techniques to real data sets and critically evaluate their performance.
Antirequisite(s): the former Computer Science 4414A/B, the former Statistical Sciences 3850F/G, the former Software Engineering 4460A/B.
Prerequisite(s): (Data Science 1200A/B or Computer Science 1026A/B or Computer Science 1027A/B or Computer Science 2120A/B or Digital Humanities 2220A/B or Engineering Science 1036A/B or Data Science 2000A/B or Integrated Science 2002B or Statistical Sciences 2864A/B); (Data Science 2000A/B or Integrated Science 2002B or Statistical Sciences 2857A/B or 0.5 course from the Introductory Statistics Course List); (Mathematics 1600A/B or Numerical and Mathematical Methods 1411A/B or the former Applied Mathematics 1411A/B or Data Science 2100A); (Calculus 1000A/B or Calculus 1500A/B or Numerical and Mathematical Methods 1412A/B or the former Applied Mathematics 1412A/B or Data Science 2100A). Note that Data Science 2000A/B, Integrated Science 2002B and Data Science 2100A can be used to fulfill multiple prerequisites.
Extra Information: 2 lecture hours/week, 2 lab hour/week. 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
Return to top
An individual research project on a specific data science problem supervised by a Western faculty member. A written thesis and an oral presentation will be required.
Antirequisite(s): Computer Science 3380F/G/Z, Computer Science 4460Z, Computer Science 4480Y, Computer Science 4470Y, Computer Science 4490Z, Statistical Science 4999F/G/Z, Actuarial Sciences 4997F/G/Z, Financial Modelling 4998F/G/Z.
Prerequisite(s): Registration in the Honours Specialization in Data Science. Data Science 3000A/B (or former Computer Science 4414A/B, the former Statistical Sciences 3850F/G, the former Software Engineering 4460A/B). 1.5 courses from Computer Science 3319A/B, Computer Science 3340A/B, Statistical Sciences 3843A/B, Statistical Sciences 3859A/B, Statistical Sciences 3860A/B, Statistical Sciences 4850F/G.
Extra Information: Starts in September of an academic year and ends in April of the same academic year.
Course Weight: 0.50
Return to top
Data Science
Students will learn how to visualize and analyze continuous and categorical data from various domains, using modern data science tools. Concepts of distributions, sampling, estimation, confidence intervals, experimental design, inference, correlation will be introduced in a practical, data-driven way.
Antirequisite(s): Statistical Sciences 1023A/B, Statistical Sciences 2037A/B, Statistical Sciences 2857A/B, the former Statistical Sciences 1024A/B.
Prerequisite(s): One or more of Ontario Secondary School MCV4U, MHF4U, MDM4U, Mathematics 0109A/B, Mathematics 0110A/B, Mathematics 1229A/B, or equivalent.
Extra Information: 3 lecture hours/week, 1 laboratory hour/week.
Course Weight: 0.50
Return to top
Programming for Data Science is intended for students with little or no background in programming. Design and analysis of algorithms and their implementation as modular, reliable, well-documented programs written in a modern programming language.
Antirequisite(s): Computer Science 1025A/B, Computer Science 1026A/B, Computer Science 2120A/B, Engineering Science 1036A/B, Digital Humanities 2200A/B.
Extra Information: 3 lecture hours/week, 2 laboratory hours/week.
Course Weight: 0.50
Return to top
Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.
Antirequisite(s): Integrated Science 2002B.
Prerequisite(s): 1.0 courses from Mathematics, Calculus, or Applied Mathematics (numbered 1000 and higher) with a minimum mark of 60%. Data Science 1000A/B (with a minimum grade of 60%) can be used to meet 0.5 of the 1.0 mathematics course requirements.
Extra Information: 2 lecture hours/week, 2 laboratory hours/week.
Course Weight: 0.50
Return to top
Mathematical background for students wanting to take Data Science 3000A/B, but missing background in linear algebra and calculus. Vector and matrix algebra, norms, linear dependence, inverses, vector spaces, eigenvectors and eigenvalues, Gradients, Hessians, basics of optimization. All concepts are explained in the context of data science examples.
Antirequisite(s): Mathematics 1600A/B, Mathematics 1700A/B, Numerical and Mathematical Methods 1411A/B, the former Applied Mathematics 1411A/B.
Prerequisite(s): 1.0 courses from Mathematics, Calculus, or Applied Mathematics (1000 and higher) with a minimal grade of 60%. Data Science 2000A/B or Integrated Science 2002B can be used to fulfil 0.5 of the requirements.
Extra Information: 3 lecture hours/week, 1 tutorial hour/week.
Course Weight: 0.50
Return to top
Basic principles of machine learning (estimation, optimization, prediction, generalization, bias-variance trade-off, regularization) in the context of supervised (linear models, decision trees, deep neuronal networks) and unsupervised (clustering and dimensionality reduction) statistical learning techniques. The course emphasizes the ability to apply techniques to real data sets and critically evaluate their performance.
Antirequisite(s): the former Computer Science 4414A/B, the former Statistical Sciences 3850F/G, the former Software Engineering 4460A/B.
Prerequisite(s): (Data Science 1200A/B or Computer Science 1026A/B or Computer Science 1027A/B or Computer Science 2120A/B or Digital Humanities 2220A/B or Engineering Science 1036A/B or Data Science 2000A/B or Integrated Science 2002B or Statistical Sciences 2864A/B); (Data Science 2000A/B or Integrated Science 2002B or Statistical Sciences 2857A/B or 0.5 course from the Introductory Statistics Course List); (Mathematics 1600A/B or Numerical and Mathematical Methods 1411A/B or the former Applied Mathematics 1411A/B or Data Science 2100A); (Calculus 1000A/B or Calculus 1500A/B or Numerical and Mathematical Methods 1412A/B or the former Applied Mathematics 1412A/B or Data Science 2100A). Note that Data Science 2000A/B, Integrated Science 2002B and Data Science 2100A can be used to fulfill multiple prerequisites.
Extra Information: 2 lecture hours/week, 2 lab hour/week. 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
Return to top
An individual research project on a specific data science problem supervised by a Western faculty member. A written thesis and an oral presentation will be required.
Antirequisite(s): Computer Science 3380F/G/Z, Computer Science 4460Z, Computer Science 4480Y, Computer Science 4470Y, Computer Science 4490Z, Statistical Science 4999F/G/Z, Actuarial Sciences 4997F/G/Z, Financial Modelling 4998F/G/Z.
Prerequisite(s): Registration in the Honours Specialization in Data Science. Data Science 3000A/B (or former Computer Science 4414A/B, the former Statistical Sciences 3850F/G, the former Software Engineering 4460A/B). 1.5 courses from Computer Science 3319A/B, Computer Science 3340A/B, Statistical Sciences 3843A/B, Statistical Sciences 3859A/B, Statistical Sciences 3860A/B, Statistical Sciences 4850F/G.
Extra Information: Starts in September of an academic year and ends in April of the same academic year.