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ANL588 is an independent study course where students complete a project under supervision of a mentor (from the industry or SUSS). I run a seminar series for the course based on Provost and Fawcett's Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking and James et al.'s Introduction to Statistical Learning.
The seminars focus on the development of data analytic thinking, in conjunction with Python programming basics, regression, classification, module evaluation and tuning, unsupervised learning. Implementations using both R and Python are discussed in this course.
Remark: The notes are undergoing edits and changes from time to time and the numbering of the seminar slides may change.
Past students: Drop me an email if you like to have the updated Python/R scripts.
Seminar 0: Introduction to Data Analytics and Python Programming
Seminar 1: Python Programming
Seminar 2 - Linear Regression (Theory).pptxSeminar 2 - Linear Regression (Practice) .pptxSeminar 3: Classification
Seminar 3 - Classification (Theory).pptxSeminar 3 - Classification (Practice).pptxSeminar 4: Model Fitting
Seminar 4 - Model Fitting (Theory).pptxSeminar 4 - Model Fitting (Practice).pptxSeminar 5: Unsupervised Learning
Seminar 5 - Unsupervised Machine Learning.pptxGoogle Sites
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