This course is designed to introduce statistical research methodology--the procedures by which statisticians go about approaching and analyzing data--to early undergraduates. Students will learn basic concepts of statistical learning--inference vs. prediction, supervised vs. unsupervised learning, regression vs. classification, etc.--and will reinforce this knowledge by applying, e.g., linear regression, random forest, principal components analysis, and/or hierarchical clustering and more to datasets provided by the instructor. Students will also practice disseminating the results of their analyses via oral presentations and posters. Analyses will primarily be carried out using the R programming language. Previous knowledge of R is not required for this course. Space is very limited; there will be an application process. The course is currently open to sophomore statistics students only.
Fall 2021, Tu-Th 1:25 - 2:45 PM, Wean 8427
Note: lab materials are available upon request; as I wish to reuse (at least some elements of) them, they will not be posted here. See the README file in the LECTURES directory for more information.
| Week | Day | Topic |
|---|---|---|
| 1 | Tu | pre-course assessment + R + statistical learning |
| Th | R: vectors + lab | |
| 2 | Tu | R: dplyr + lab |
| Th | R: ggplot + lab | |
| 3 | Tu | exploratory data analysis + lab |
| Th | K-means + hierarchical clustering + lab | |
| Fr | select semester project dataset | |
| 4 | Tu | principal components analysis + lab |
| Th | model assessment + bias-variance tradeoff + lab | |
| 5 | Tu | generalized linear models + linear regression + lab |
| Th | logistic regression + lab | |
| Fr | first data analysis report due: EDA + PCA | |
| 6 | Tu | best subset selection + lab |
| Th | penalized regression + lab | |
| 7 | Tu | machine learning + trees + lab |
| Th | no class: mid-semester break | |
| 8 | Tu | Random Forest + lab |
| Th | boosting + lab | |
| Fr | second data analysis report due: linear/logistic | |
| 9 | Tu | k nearest neighbors + lab |
| Th | support vector machine + lab | |
| 10 | Tu | naive Bayes + lab |
| Th | kernels: density estimation and regression + lab | |
| 11 | Tu | deep learning + lab |
| Th | deep learning + lab | |
| Fr | third data analysis report due: ML | |
| 12 | Tu | team poster preparation |
| Th | team poster preparation | |
| 13 | Tu | cancelled: Thanksgiving |
| Th | cancelled: Thanksgiving | |
| 14 | Tu | final data analysis report & team poster work |
| Tu | oral exam: high-level statistical learning concepts | |
| Th | final data analysis report & team poster work | |
| Th | oral exam: high-level statistical learning concepts | |
| Fr | final data analysis report due | |
| Fr | team poster due | |
| Fr | USCLAP intermediate data analysis report due | |
| F | We | departmental poster presentation |
| May 4 | We | CMU Meeting of the Minds poster presentation |
| 2022 |