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BME1478material

Course structure

Each week students will be sent course materials on Monday or Tuesday. These will include a video tutorial, sample files, and a set of exercises. The course can be completed entirely online. Students will have opportunity to get one-on-one guidance from TAs on Wednesday mornings from 10 AM-12 PM in room 412, Rosebrugh building, St. George campus. Students will also be able to ask for help online using Slack during this time. Weekly exercises and assignments will be due on the Sunday night of each week.

Week 1. Introduction & computer setup

Week 2. Introduction to the command line

Week 3. Python introduction: data types, conditional statements, loops, functions, packages

Week 4. Python continued: review

Week 5. Python continued: OOP and more on analyzing data (multiple files)

Week 6. command line programming and version control

Week 7. Using Python instead of Excel

Week 8. Data visualisation

Week 9. Visualization for exploratory data analysis

Week 10. Statistics: data preprocessing (tidy data, reshaping data, cleaning data); Hypothesis testing (ttetst: one sample, two sample, paired, ANOVA)

Week 11. Statistics: Tests of Association (linear regression, multivariate linear regression, generalized linear models)

Week 12. Machine learning (Normalization, Dimensionality reduction, PCA, UMAP, Supervised Learning:Decision trees, Evaluating model, Unsupervised Learning: K-means clustering, Quality metrics)

Assessment

Each week will include a set of associated exercises, each set worth 6% of the total course grade. A final assignment will also be required, worth 28% of the total grade.

Grading

90% -----at least an A-

80% -----at least an B-

70% -----at least an C-

60% -----at least an D-

Late assignments will be accepted but docked 20% for each day late.

Students should send completed exercises/assignments to the following email address: [email protected]

Exercises/assignments are due by 11:59 PM on the Sunday of each week

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