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afarbin merged 1 commit into from
Aug 13, 2026
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Matrix update: PhD courses re-derived from Proposal-v3 #1

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52 changes: 26 additions & 26 deletions data/granular.csv
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Expand Up @@ -13,17 +13,17 @@ C1. Data science foundations and scientific reasoning,C1.11,literature review an
C1. Data science foundations and scientific reasoning,C1.12,research questions and hypotheses,D,,,I,,,D,,,I,E,,D,I,,,I,,,D,,,D,,,,,,,D
C1. Data science foundations and scientific reasoning,C1.13,stakeholder identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.1,intro to computing,,,,D,D,,,,,,,,,,,,,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.2,"hardware, software, and operating systems",I,,,D,D,,,,,,,,,I,,,I,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.2,"hardware, software, and operating systems",I,,,D,D,,,,,,,,,I,,,I,,,,,,,,,I,,,,
C2. Computing environment and developer tools,C2.3,storage and filesystems,I,,,D,D,,,,,,,,,I,,,,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.4,Linux/Unix commands,D,,,D,D,,,,,,,,,,,,,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.5,shell commands,D,,,D,D,,,,,,,,,,,,,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.6,version control,D,,,E,D,,,,,,I,,,,,,D,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.6,version control,D,,,E,D,,,,,,I,,,,,,D,,,,,,,,,D,,,,
C2. Computing environment and developer tools,C2.7,Git,D,,,E,D,,,,,,I,I,,,,,D,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.8,GitHub,D,,,E,D,,,,,,I,I,,,,,D,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.9,Jupyter,,,I,E,E,D,,,E,D,,I,,D,,,D,,,,,D,,,,,,,,
C2. Computing environment and developer tools,C2.10,Google Colab,,,I,I,I,,,,I,D,,,,,,,E,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.11,WSL,,,,D,I,,,,,,,,,,,,,,,,,,,,,,,,,
C2. Computing environment and developer tools,C2.12,repositories and reproducible handoff of code,,,,I,,,,,,,,I,I,I,,,E,,D,,D,,,,,,,,,
C2. Computing environment and developer tools,C2.12,repositories and reproducible handoff of code,,,,I,,,,,,,,I,I,I,,,E,,D,,D,,,,,D,,,,
C3. Python programming and software construction,C3.1,variables and types,,,,E,D,,,,,,,,,E,,,,,,,,,,,,,,,,
C3. Python programming and software construction,C3.2,syntax,,,,E,D,,,,,,,,,E,,,,,,,,,,,,,,,,
C3. Python programming and software construction,C3.3,conditional statements,,,,E,E,,,,,,,,,E,,,,,,,,,,,,,,,,
Expand All @@ -39,7 +39,7 @@ C3. Python programming and software construction,C3.12,object-oriented programmi
C3. Python programming and software construction,C3.13,design patterns,,,,I,D,,,,,,,,,,,,I,,,,,,,,,,,,,
C3. Python programming and software construction,C3.14,UML,,,,,D,,,,,,,,,,,,,,,,,,,,,,,,,
C3. Python programming and software construction,C3.15,scripting vs building software,,,,I,D,,,,,,,,,,,,I,,,,,,,,,,,,,
C3. Python programming and software construction,C3.16,multithreading and multiprocessing,,,,,,,,,,,,,,,,,I,,,,,,,,,,,,,
C3. Python programming and software construction,C3.16,multithreading and multiprocessing,,,,,,,,,,,,,,,,,I,,,,,,,,,D,,,,
C3. Python programming and software construction,C3.17,iterators and generators,,,,I,D,,,,,,,,,,,,D,,,,,,,,,,,,,
C3. Python programming and software construction,C3.18,serialization (JSON/YAML/pickle/npy),,,,,D,,,,,,,,,,,,I,,,,,,,,,,,,,
C4. Core libraries and computational tools,C4.1,NumPy,,,D,E,E,D,,,E,I,,,,E,,,I,,,,D,,,,,,,,,
Expand Down Expand Up @@ -70,7 +70,7 @@ C5. Mathematical and statistical foundations,C5.12,hypothesis testing,D,,,,,D,E,
C5. Mathematical and statistical foundations,C5.13,null vs alternative hypothesis,D,,,,,D,E,E,,,,,E,,I,D,,,D,I,,I,,,,,,,,
C5. Mathematical and statistical foundations,C5.14,p-values,D,,,,,D,E,E,,,,,E,,I,D,,,D,I,,I,,,,,,,,
C5. Mathematical and statistical foundations,C5.15,t-test,D,,,,,,E,E,,,,,E,,I,D,,,D,I,,,,,,,,,,
C5. Mathematical and statistical foundations,C5.16,Monte Carlo methods,,,,D,D,I,D,I,,,,,I,,I,,,,,,,D,,,,,,,,
C5. Mathematical and statistical foundations,C5.16,Monte Carlo methods,,,,D,D,I,D,I,,,,,I,,I,,,,,,,D,,,,D,,,,
C5. Mathematical and statistical foundations,C5.17,linear algebra,,,E,D,D,,,I,D,I,,,,D,E,E,D,,,,E,,,E,,,,,,
C5. Mathematical and statistical foundations,C5.18,correlation,D,D,,I,D,D,D,D,,I,,I,D,D,I,D,,,D,E,,,,,,,,,,
C5. Mathematical and statistical foundations,C5.19,ANOVA,,,,,,,,E,,,,,E,,,D,,,D,,,,,,,,,,,
Expand All @@ -80,7 +80,7 @@ C5. Mathematical and statistical foundations,C5.22,Bayes' theorem / Bayesian inf
C5. Mathematical and statistical foundations,C5.23,"MCMC (Gibbs, Metropolis-Hastings)",,,,,,,,,,,,,,,I,,,,,,,D,,,,,,,,
C5. Mathematical and statistical foundations,C5.24,multivariate analysis / MANOVA / Hotelling's T-squared,,,,,,,,,,,,,,,,E,,,,,,,,,I,,,,,
"C6. Data understanding, wrangling, and preprocessing",C6.1,data understanding,E,D,,D,D,E,D,,,D,,,D,E,D,I,,,,E,D,,,,,,,,,
"C6. Data understanding, wrangling, and preprocessing",C6.2,data exploration,E,D,,D,D,E,D,I,I,E,,I,D,E,D,D,I,D,D,E,D,,D,,,,I,D,,
"C6. Data understanding, wrangling, and preprocessing",C6.2,data exploration,E,D,,D,D,E,D,I,I,E,,I,D,E,D,D,I,D,D,E,D,,D,,,,,D,,
"C6. Data understanding, wrangling, and preprocessing",C6.3,data analysis,D,D,,D,D,D,D,,,D,,,E,E,,D,,,,D,,,,,,,,,,
"C6. Data understanding, wrangling, and preprocessing",C6.4,data entry,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"C6. Data understanding, wrangling, and preprocessing",C6.5,data formatting,E,D,,D,,E,,,,D,,,I,E,,I,D,,I,,,,,,,,,,,
Expand Down Expand Up @@ -129,41 +129,41 @@ C7. Databases and SQL,C7.13,Python-SQL integration,,,,,,,,,,,,,,,,,,,,,,,D,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.14,StoryPoints,,D,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.15,Power BI basics,,I,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.16,KPI tracking,,I,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.17,audience-centered communication,D,E,,I,,,,,,,,E,,I,I,,I,I,,D,,,D,,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.17,audience-centered communication,D,E,,I,,,,,,,,E,,I,I,,I,I,,D,,,D,,,,,D,,
"C8. Visualization, dashboards, and storytelling",C8.18,exploratory vs explanatory design,D,D,,,,,,,,,,,,,I,,,,,,,,,,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.19,avoiding misleading visuals,D,D,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.20,poster design,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"C8. Visualization, dashboards, and storytelling",C8.21,data storytelling,D,E,,I,,,,,,,,,,I,I,,,,I,D,,,D,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.1,what machine learning is,D,,,I,,E,,,E,D,,,,D,,,D,I,,D,D,,,I,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.2,machine learning concepts,D,,,,,E,,,E,D,,,,D,,,D,,,D,D,,,,,,D,,,
C9. Machine learning foundations and classical supervised learning,C9.3,machine learning pipelines,D,,,I,,E,,,E,D,,,,D,,,D,,,D,,,D,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.4,supervised vs unsupervised learning,D,,,I,,D,,,E,,,,,D,I,,D,,,D,D,,,,,,I,D,,
C9. Machine learning foundations and classical supervised learning,C9.2,machine learning concepts,D,,,,,E,,,E,D,,,,D,,,D,,,D,D,,,,,,E,,,
C9. Machine learning foundations and classical supervised learning,C9.3,machine learning pipelines,D,,,I,,E,,,E,D,,,,D,,,D,,,D,,,D,,,,,D,,
C9. Machine learning foundations and classical supervised learning,C9.4,supervised vs unsupervised learning,D,,,I,,D,,,E,,,,,D,I,,D,,,D,D,,,,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.5,prediction,D,,,,,D,,,D,,,,D,D,,,D,,,D,,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.6,regression,D,,,,,E,D,E,E,,,,D,D,,,D,,E,D,D,,,,,,,D,,
C9. Machine learning foundations and classical supervised learning,C9.7,linear regression,D,,,,,E,D,E,E,,,I,D,D,,,D,I,E,D,D,,,I,,,D,,,
C9. Machine learning foundations and classical supervised learning,C9.6,regression,D,,,,,E,D,E,E,,,,D,D,,,D,,E,D,D,,,,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.7,linear regression,D,,,,,E,D,E,E,,,I,D,D,,,D,I,E,D,D,,,I,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.8,multiple linear regression,I,,,,,D,,D,D,I,,,D,D,,,I,,E,D,I,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.9,polynomial regression,I,,,,,D,,,D,I,,,,I,,,,,E,,I,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.10,classification,D,,,I,,E,,,E,,,,,D,E,,E,,D,E,D,,,,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.11,logistic regression,I,,,,,D,,D,E,,,I,,I,D,,I,,D,,D,I,,,,,D,,,
C9. Machine learning foundations and classical supervised learning,C9.11,logistic regression,I,,,,,D,,D,E,,,I,,I,D,,I,,D,,D,I,,,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.12,sigmoid function,,,,,,D,,,E,,,,,,I,,D,,I,,,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.13,decision boundary,,,,,,I,,,E,,,,,,D,,,,,,I,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.14,cost functions,,,,,,D,,,E,D,,,,,,,E,,,I,E,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.15,learning rate,,,,,,D,,,E,D,,,,,,,E,,,D,E,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.16,gradient descent,,,,,,D,,,E,D,,,,,,,E,,,D,E,,,,,,,,D,
C9. Machine learning foundations and classical supervised learning,C9.16,gradient descent,,,,,,D,,,E,D,,,,,,,E,,,D,E,,,,,,D,,D,
C9. Machine learning foundations and classical supervised learning,C9.17,K-nearest neighbors,D,,,I,,D,,,,,,,,D,,,,,,D,,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.18,SVM,I,,,I,,,,,,,,,,I,,,,,,,D,,,,,,D,,,
C9. Machine learning foundations and classical supervised learning,C9.18,SVM,I,,,I,,,,,,,,,,I,,,,,,,D,,,,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.19,baseline models,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.20,demand prediction,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.21,decision trees,I,,,,,E,,,D,,,I,,I,,,,,,E,,,,,,,D,,,
C9. Machine learning foundations and classical supervised learning,C9.22,"ensembles (bagging, random forest, boosting)",,,,,,D,,,D,D,,I,,I,,,I,,,,,,,,,,D,,,
C9. Machine learning foundations and classical supervised learning,C9.21,decision trees,I,,,,,E,,,D,,,I,,I,,,,,,E,,,,,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.22,"ensembles (bagging, random forest, boosting)",,,,,,D,,,D,D,,I,,I,,,I,,,,,,,,,,D,D,,
C9. Machine learning foundations and classical supervised learning,C9.23,discriminant analysis (LDA/QDA),,,,,D,,,,,,,,,,E,,,,,,,,,,,,,,,
C9. Machine learning foundations and classical supervised learning,C9.24,regression diagnostics / transformations / WLS,,,,,,,I,D,,,,,D,I,,,,,E,,,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.1,metrics,D,,,I,I,E,,,D,E,,I,,D,D,,D,,I,D,I,,D,,,,D,D,,
"C10. Model evaluation, validation, and optimization",C10.2,model evaluation,D,,,I,,E,,,E,D,,,,D,D,,E,,,D,I,,D,,,,D,E,,
"C10. Model evaluation, validation, and optimization",C10.1,metrics,D,,,I,I,E,,,D,E,,I,,D,D,,D,,I,D,I,,D,,,,,D,D,
"C10. Model evaluation, validation, and optimization",C10.2,model evaluation,D,,,I,,E,,,E,D,,,,D,D,,E,,,D,I,,D,,,,,E,D,
"C10. Model evaluation, validation, and optimization",C10.3,training/validation/test sets,D,,,I,,D,,,D,E,,I,,D,D,,D,,D,D,,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.4,cross-validation,,,,,,D,,,D,D,,,,,I,,I,,D,D,,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.5,overfitting and underfitting,,,,I,,E,,,E,E,,,,D,,,E,I,I,D,I,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.6,regularization,,,,,,,,,D,I,,,,,,,E,,D,,E,I,,,,,,,D,
"C10. Model evaluation, validation, and optimization",C10.6,regularization,,,,,,,,,D,I,,,,,,,E,,D,,E,I,,,,,D,D,D,
"C10. Model evaluation, validation, and optimization",C10.7,feature scaling,,,I,,,E,,,D,D,,,,D,D,I,D,,,,,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.8,convergence,,,,,,I,,,D,,,,,,,,D,,,,E,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.9,choosing learning rates,,,,,,I,,,D,,,,,,,,D,,,,D,,,,,,,,,
Expand All @@ -176,13 +176,13 @@ C9. Machine learning foundations and classical supervised learning,C9.24,regress
"C10. Model evaluation, validation, and optimization",C10.16,precision/recall tradeoffs,D,,,,,I,,,D,D,,I,,,D,,,,,,,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.17,skewed-dataset metrics,D,,,,,,,,D,D,,,,,,,,,,,,,,,,,,,,
"C10. Model evaluation, validation, and optimization",C10.18,ROC / AUC / confusion matrix,D,,,,D,I,,,,D,,,,,D,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.1,clustering,D,,,,,E,,,D,,,,,D,,I,,,,D,I,,,,,,,D,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.1,clustering,D,,,,,E,,,D,,,,,D,,I,,,,D,I,,,,,,D,D,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.2,K-means,D,,,,,E,,,D,,,,,D,,I,,,,D,I,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.3,anomaly detection,I,,,,,,,,D,,,,,,,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.4,Gaussian model for anomaly detection,,,,,,,,,D,,,,,,,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.5,anomaly detection evaluation,,,,,,,,,D,,,,,,,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.6,choosing anomaly features,,,,,,,,,D,,,,,,,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.7,recommender systems,,,,,,I,,,E,,,,,,,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.7,recommender systems,,,,,,I,,,E,,,,,,,,,,,,,,,,,,D,D,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.8,per-item features,,,,,,,,,D,,,,,,,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.9,collaborative filtering,,,,,,,,,E,,,,,,,,,,,,,,,,,,,,,
"C11. Unsupervised learning, anomaly detection, recommenders, and reinforcement learning",C11.10,content-based filtering,,,,,,,,,E,,,,,,,,,,,,,,,,,,,,,
Expand All @@ -205,14 +205,14 @@ C12. Deep learning and representation learning,C12.7,multiclass classification,,
C12. Deep learning and representation learning,C12.8,softmax,,,,I,,,,,D,E,,,,,,,E,,,,,,,,,,,,,
C12. Deep learning and representation learning,C12.9,deep learning frameworks,,,,,,,,,E,D,,I,,,,,E,,,,,,,,,,,,E,
C12. Deep learning and representation learning,C12.10,additional layer types,,,,,,,,,D,D,,I,,,,,E,,,,,,,,,,,,,
C12. Deep learning and representation learning,C12.11,transfer learning,,,,,,,,,D,D,,,,,,,D,,,,,,,,,,,,,
C12. Deep learning and representation learning,C12.11,transfer learning,,,,,,,,,D,D,,,,,,,D,,,,,,,,,,,,D,
C12. Deep learning and representation learning,C12.12,recurrent neural networks,,,,,,,,,I,,,,,,,,,,,,,,,,,,,,D,
C12. Deep learning and representation learning,C12.13,natural language processing,,,,,,,,,I,,,,,,,,D,,,,,,,,,,,,D,
C12. Deep learning and representation learning,C12.14,embeddings,,,,,,,,,I,,,,,,,,D,,,,,,,,,,,,,
C12. Deep learning and representation learning,C12.15,transformer models,,,,,,,,,I,I,,,,,,,D,,,,,,,,,,,,D,
C12. Deep learning and representation learning,C12.16,generative models,,,,,,,,,I,I,,,,,,,I,,,,,,,,,,,,D,
C13. Computer vision and image analysis,C13.1,image analysis,,,,,,,,,E,D,,,,,,,E,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.2,computer vision basics,,,,,,,,,E,D,,,,,,,E,,,,,,,,,,,,I,
C13. Computer vision and image analysis,C13.2,computer vision basics,,,,,,,,,E,D,,,,,,,E,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.3,image preprocessing,,,,,,,,,D,D,,I,,,,,D,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.4,resizing,,,,,,,,,,D,,,,,,,D,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.5,normalization,,,,,,,,,,D,,,,,,,D,,,,,,,,,,,,,
Expand All @@ -222,7 +222,7 @@ C13. Computer vision and image analysis,C13.8,edge detection,,,,,,,,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.9,filters,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.10,convolution,,,,,,,,,E,E,,I,,,,,E,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.11,pooling,,,,,,,,,E,E,,I,,,,,D,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.12,CNN architectures,,,,,,,,,E,E,,I,,,,,E,,,,,,,,,,,,D,
C13. Computer vision and image analysis,C13.12,CNN architectures,,,,,,,,,E,E,,I,,,,,E,,,,,,,,,,D,D,,
C13. Computer vision and image analysis,C13.13,dropout,,,,,,,,,,I,,I,,,,,D,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.14,batch normalization,,,,,,,,,,,,,,,,,I,,,,,,,,,,,,,
C13. Computer vision and image analysis,C13.15,CIFAR-10,,,,,,,,,,E,,,,,,,,,,,,,,,,,,,,
Expand Down Expand Up @@ -251,7 +251,7 @@ C13. Computer vision and image analysis,C13.19,confusion matrix–style evaluati
"C14. Responsible AI, ethics, privacy, explainability, and deployment",C14.19,Flask,,,,,,,,,,,,I,,,,,,,,,,,I,,,,,,,
"C14. Responsible AI, ethics, privacy, explainability, and deployment",C14.20,model operationalization and demoing deployed predictions,,,,,,,,,,,,E,,,,,I,,,,,,E,,,,,,,
"C14. Responsible AI, ethics, privacy, explainability, and deployment",C14.21,"causal inference (propensity, IPW, ATE/ATT)",,,,,,,,,,,,,,,I,,,,,,,D,,,,,,,,
"C15. Capstone, research, communication, and career-readiness rows",C15.1,project selection,,,,,,,,,,,D,D,D,D,,,E,I,I,D,D,,E,,,,,,,D
"C15. Capstone, research, communication, and career-readiness rows",C15.1,project selection,,,,,,,,,,,D,D,D,D,,,E,I,I,D,D,,E,,,,,,I,D
"C15. Capstone, research, communication, and career-readiness rows",C15.2,team formation,,,,,,,,,,,D,D,D,D,,,D,,I,I,D,,,,,,,,,
"C15. Capstone, research, communication, and career-readiness rows",C15.3,advisor meetings,,,,,,,,,,,D,D,D,,,,,,,,,,D,,,,,,,D
"C15. Capstone, research, communication, and career-readiness rows",C15.4,peer review,,,,,,,,,,,D,E,,I,,,D,,,D,I,,,,,,,,,
Expand Down
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