diff --git a/data/granular.csv b/data/granular.csv index 293f892..aa657ba 100644 --- a/data/granular.csv +++ b/data/granular.csv @@ -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,,,,,,,,,,,,,,,, @@ -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,,,,,,,,, @@ -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,,,,,,,,,,, @@ -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,,,,,,,,,,, @@ -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,,,,,,,,, @@ -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,,,,,,,,,,,,,,,,,,,,, @@ -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,,,,,,,,,,,,, @@ -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,,,,,,,,,,,,,,,,,,,, @@ -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,,,,,,,,, diff --git a/data/matrix.csv b/data/matrix.csv index f29cacc..e5f1480 100644 --- a/data/matrix.csv +++ b/data/matrix.csv @@ -6,11 +6,11 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA 1. Foundations & Scientific Reasoning,Research design / questions / hypotheses,D,,,I,,,D,,,I,E,,D,I,,,I,,,D,,,D,,,,,,,D 1. Foundations & Scientific Reasoning,Literature review & related work,,,,,,,,,,,D,,,,,,I,,,D,,,I,,,,,,,I 1. Foundations & Scientific Reasoning,Stakeholder identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -2. Computing Environment & Developer Tools,Hardware / OS / storage / filesystems,I,,,D,D,,,,,,,,,I,,,I,,,,,,,,,,,,, +2. Computing Environment & Developer Tools,Hardware / OS / storage / filesystems,I,,,D,D,,,,,,,,,I,,,I,,,,,,,,,I,,,, 2. Computing Environment & Developer Tools,Linux / Unix shell commands,D,,,D,D,,,,,,,,,,,,,,,,,,,,,,,,, -2. Computing Environment & Developer Tools,Version control: Git / GitHub,D,,,E,D,,,,,,I,I,,,,,D,,,,,,,,,,,,, +2. Computing Environment & Developer Tools,Version control: Git / GitHub,D,,,E,D,,,,,,I,I,,,,,D,,,,,,,,,D,,,, 2. Computing Environment & Developer Tools,Jupyter / Google Colab,,,I,E,E,D,,,E,D,,I,,D,,,E,,,,,D,,,,,,,, -2. Computing Environment & Developer Tools,Reproducibility & code handoff,,,,I,,,,,,,,I,I,I,,,D,,D,,D,,,,,,,,, +2. Computing Environment & Developer Tools,Reproducibility & code handoff,,,,I,,,,,,,,I,I,I,,,D,,D,,D,,,,,D,,,, 3. Python Programming & Software Construction,"Variables, types, syntax, operators",,,,E,D,,,,,,,,,E,,,,,,,,,,,,,,,, 3. Python Programming & Software Construction,Conditionals & loops / control flow,,,,E,E,,,,,,,,,E,,,,,,,,,,,,,,,, 3. Python Programming & Software Construction,"Functions (args, recursion, scope)",,,,E,E,,,,,,,,,E,,,,,,,,,,,,,,,, @@ -20,7 +20,7 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA 3. Python Programming & Software Construction,"Object-oriented programming (classes, inheritance, operator overloading)",,,,E,E,,,,,,,,,,,,D,,,,,,,,,,,,, 3. Python Programming & Software Construction,Design patterns / UML / software vs scripting,,,,I,D,,,,,,,,,,,,I,,,,,,,,,,,,, 3. Python Programming & Software Construction,Serialization (JSON/YAML/pickle/npy),,,,,D,,,,,,,,,,,,I,,,,,,,,,,,,, -3. Python Programming & Software Construction,Multithreading / multiprocessing,,,,,,,,,,,,,,,,,I,,,,,,,,,,,,, +3. Python Programming & Software Construction,Multithreading / multiprocessing,,,,,,,,,,,,,,,,,I,,,,,,,,,D,,,, 4. Core Libraries & Computational Tools,"NumPy (arrays, vectorization, matrices)",,,D,E,E,D,,,E,I,,,,E,,,I,,,,D,,,,,,,,, 4. Core Libraries & Computational Tools,"Pandas (DataFrames, CSV, groupby)",,,,E,D,D,,,,D,,,,E,,,I,,,,,,,,,,,,, 4. Core Libraries & Computational Tools,Matplotlib,,,I,E,E,D,,,D,D,,,,D,,,D,,,,D,,,,,,,,, @@ -36,7 +36,7 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA 5. Mathematical & Statistical Foundations,Histograms & data summaries,D,D,,D,D,D,E,,,D,,,D,D,,,,,,E,,,,,,,,,, 5. Mathematical & Statistical Foundations,"Hypothesis testing (null/alt, p-values, t-test)",D,,,,,,E,E,,,,,E,,I,D,,I,D,D,,I,,,D,,,,, 5. Mathematical & Statistical Foundations,Correlation,D,D,I,I,D,D,D,D,,I,,I,D,D,I,D,,,D,E,,,,,,,,,, -5. Mathematical & Statistical Foundations,Monte Carlo methods,,,,D,D,,D,I,,,,,,,I,,,,,,,D,,,,,,,, +5. Mathematical & Statistical Foundations,Monte Carlo methods,,,,D,D,,D,I,,,,,,,I,,,,,,,D,,,,D,,,, 5. Mathematical & Statistical Foundations,"Linear algebra (vectors, matrices, eigen, SVD)",,,E,D,D,,,I,,I,,,,D,E,E,D,,,,E,,,E,,,,,, 5. Mathematical & Statistical Foundations,Bayes' theorem / Bayesian inference,,,D,,D,,D,,,,,,,,I,,I,,,D,,E,,,D,,,,, 5. Mathematical & Statistical Foundations,Sampling distributions & inference theory,D,,D,I,,D,E,,,,,,E,,,D,,,,,,,,,D,,,,, @@ -44,7 +44,7 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA 5. Mathematical & Statistical Foundations,ANOVA,,,,,,,,E,,,,,E,,,D,,,D,,,,,,,,,,, 5. Mathematical & Statistical Foundations,Multivariate analysis / MANOVA / Hotelling's T-squared,,,,,,,,,,,,,,,,E,,,,,,,,,I,,,,, 5. Mathematical & Statistical Foundations,"MCMC (Gibbs, Metropolis-Hastings)",,,,,,,,,,,,,,,I,,,,,,,D,,,,,,,, -"6. Data Understanding, Wrangling & Preprocessing",Data exploration / EDA,E,D,,D,D,E,D,I,I,E,,I,D,E,D,D,I,D,D,E,D,,D,,,,I,D,, +"6. Data Understanding, Wrangling & Preprocessing",Data exploration / EDA,E,D,,D,D,E,D,I,I,E,,I,D,E,D,D,I,D,D,E,D,,D,,,,,D,, "6. Data Understanding, Wrangling & Preprocessing",Cleaning & formatting,E,D,,D,,E,,,,D,I,,,E,,I,D,D,I,E,I,,D,,,,,,, "6. Data Understanding, Wrangling & Preprocessing",Missing data & imputation,D,D,,D,,E,,,,E,I,,,E,,I,,,,E,,I,I,,,,,,, "6. Data Understanding, Wrangling & Preprocessing",Outlier detection,E,D,,,,D,,,,D,,,,D,,D,,,,E,,,,,,,,,, @@ -64,50 +64,50 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA "8. Visualization, Dashboards & Storytelling",Tableau,,E,,,,,,,,,,,,,,,,,,,,,,,,,,,, "8. Visualization, Dashboards & Storytelling",Power BI,,I,,,,,,,,,,,,,,,,,,,,,,,,,,,, "8. Visualization, Dashboards & Storytelling",Dashboards & interactivity,,E,,,,,,,,,,,,,,,,,,,,,D,,,,,,, -"8. Visualization, Dashboards & Storytelling",Data storytelling / audience communication,D,E,,I,,,,,,,,,,I,I,,I,I,I,D,,,D,,,,,,, +"8. Visualization, Dashboards & Storytelling",Data storytelling / audience communication,D,E,,I,,,,,,,,,,I,I,,I,I,I,D,,,D,,,,,D,, "8. Visualization, Dashboards & Storytelling",Exploratory vs explanatory design,,D,,,,,,,,,,,,,,,,,,,,,,,,,,,, 9. ML Foundations & Classical Supervised Learning,What ML is / ML pipelines,D,,,I,,E,,,E,D,,,,D,,,D,I,,D,D,,D,I,,,D,D,, -9. ML Foundations & Classical Supervised Learning,Supervised vs unsupervised,D,,,I,,D,,,E,,,,,D,I,,D,,,D,D,,,,,,I,D,, +9. ML Foundations & Classical Supervised Learning,Supervised vs unsupervised,D,,,I,,D,,,E,,,,,D,I,,D,,,D,D,,,,,,D,D,, 9. ML Foundations & Classical Supervised Learning,Linear regression,D,,,,,E,D,E,E,,,I,D,D,,,D,I,E,D,D,,,I,,,D,D,, 9. ML Foundations & Classical Supervised Learning,Multiple & polynomial regression,I,,,,,D,,D,D,I,,,D,D,,,I,,E,D,I,,,,,,,,, 9. ML Foundations & Classical Supervised Learning,Regression diagnostics / transformations / WLS,,,,,,,I,D,,,,,D,I,,,,,E,,,,,,,,,,, -9. ML Foundations & Classical Supervised Learning,Logistic regression,I,,,,,D,,D,E,,,I,,I,D,,I,,D,,D,I,,,,,D,,, -9. ML Foundations & Classical Supervised Learning,Gradient descent / cost functions / learning rate,,,,,,D,,,E,D,,,,,,,E,,,D,E,,,,,,,,D, +9. ML Foundations & Classical Supervised Learning,Logistic regression,I,,,,,D,,D,E,,,I,,I,D,,I,,D,,D,I,,,,,D,D,, +9. ML Foundations & Classical Supervised Learning,Gradient descent / cost functions / learning rate,,,,,,D,,,E,D,,,,,,,E,,,D,E,,,,,,D,,D, 9. ML Foundations & Classical Supervised Learning,K-Nearest Neighbors,D,,,I,,D,,,,,,,,D,,,,,,D,,,,,,,,,, -9. ML Foundations & Classical Supervised Learning,Decision trees,I,,,,,E,,,D,,,I,,I,,,,,,E,,,,,,,D,,, -9. ML Foundations & Classical Supervised Learning,Naive Bayes / SVM,I,,,I,,,,,,,,,,I,,,,,,D,D,,,,,,D,,, +9. ML Foundations & Classical Supervised Learning,Decision trees,I,,,,,E,,,D,,,I,,I,,,,,,E,,,,,,,D,D,, +9. ML Foundations & Classical Supervised Learning,Naive Bayes / SVM,I,,,I,,,,,,,,,,I,,,,,,D,D,,,,,,D,D,, 9. ML Foundations & Classical Supervised Learning,Discriminant analysis (LDA/QDA),,,,,D,,,,,,,,,,E,,,,,,,,,,,,,,, -9. ML Foundations & Classical Supervised Learning,"Ensembles (bagging, random forest, boosting)",,,,,,D,,,D,D,,I,,I,,,I,,,,,,,,,,D,,, -9. ML Foundations & Classical Supervised Learning,Regularization (Ridge/Lasso),,,,,,,,,D,I,,,,,,,D,,D,,E,I,,,,,,,D, -"10. Model Evaluation, Validation & Optimization",Metrics (accuracy/precision/recall/F1),D,,,I,I,E,,,D,E,,I,,D,,,D,,I,D,I,,D,,,,D,D,, +9. ML Foundations & Classical Supervised Learning,"Ensembles (bagging, random forest, boosting)",,,,,,D,,,D,D,,I,,I,,,I,,,,,,,,,,D,D,, +9. ML Foundations & Classical Supervised Learning,Regularization (Ridge/Lasso),,,,,,,,,D,I,,,,,,,D,,D,,E,I,,,,,D,D,D, +"10. Model Evaluation, Validation & Optimization",Metrics (accuracy/precision/recall/F1),D,,,I,I,E,,,D,E,,I,,D,,,D,,I,D,I,,D,,,,,D,D, "10. Model Evaluation, Validation & Optimization",ROC / AUC / confusion matrix,D,,,,D,I,,,,D,,,,,D,,,,,,,,,,,,,,, "10. Model Evaluation, Validation & Optimization",Train/validation/test split,D,,,I,,D,,,D,E,,I,,D,D,,D,,D,D,,,,,,,,,, "10. Model Evaluation, Validation & Optimization",Cross-validation,,,,,,D,,,D,D,,,,,I,,I,,D,D,,,,,,,,,, "10. Model Evaluation, Validation & Optimization",Overfitting / underfitting / bias-variance,,,,I,,E,,,E,E,,,,D,,,E,I,I,D,I,,,,,,,,, "10. Model Evaluation, Validation & Optimization",Learning curves / error analysis / model selection,,,,,,,,,D,D,,,,,,,D,,E,I,I,,,,,,E,E,, "10. Model Evaluation, Validation & Optimization",Hyperparameter tuning / grid search,,,,,,E,,,D,D,,,,I,,,E,,,,I,,,,,,,E,, -"11. Unsupervised, Anomaly, Recommenders & RL",Clustering / K-Means,D,,,,,E,,,D,,,,,D,,I,,,,D,I,,,,,,,D,, +"11. Unsupervised, Anomaly, Recommenders & RL",Clustering / K-Means,D,,,,,E,,,D,,,,,D,,I,,,,D,I,,,,,,D,D,, "11. Unsupervised, Anomaly, Recommenders & RL",Hierarchical / DBSCAN clustering,,,,,,D,,,,,,,,I,,,,,,I,,,,,,,,,, "11. Unsupervised, Anomaly, Recommenders & RL",Anomaly detection (Gaussian),I,,,,,,,,D,,,,,,,,,,,,,,,,,,,,, -"11. Unsupervised, Anomaly, Recommenders & RL",Recommender systems (collaborative/content),,,,,,I,,,E,,,,,,,,,,,,,,,,,,,,, -"11. Unsupervised, Anomaly, Recommenders & RL","Reinforcement learning (policies, Bellman, DQN)",I,,,,,,,,D,,,,,,,,,,,,,,,,,,,,, +"11. Unsupervised, Anomaly, Recommenders & RL",Recommender systems (collaborative/content),,,,,,I,,,E,,,,,,,,,,,,,,,,,,D,D,, +"11. Unsupervised, Anomaly, Recommenders & RL","Reinforcement learning (policies, Bellman, DQN)",I,,,,,,,,D,,,,,,,,,,,,,,,,,,D,D,, 12. Deep Learning & Representation Learning,Perceptron / neural networks / forward & backprop,,,,I,,D,,,E,E,,,,I,,,E,,,D,I,,,,,,D,D,E, 12. Deep Learning & Representation Learning,Activation functions / softmax / multiclass,,,,I,,D,,,D,E,,,,,,,E,,,I,I,,,,,,,,I, 12. Deep Learning & Representation Learning,Deep learning frameworks & layer types,,,,,,,,,E,D,,I,,,,,E,,,,,,,,,,,,E, -12. Deep Learning & Representation Learning,Transfer learning,,,,,,,,,D,D,,,,,,,D,,,,,,,,,,,,, +12. Deep Learning & Representation Learning,Transfer learning,,,,,,,,,D,D,,,,,,,D,,,,,,,,,,,,D, 12. Deep Learning & Representation Learning,RNNs / NLP / embeddings,,,,,,,,,I,,,,,,,,D,,,,,,,,,,,,D, -12. Deep Learning & Representation Learning,Transformers / generative models,,,,,,,,,I,I,,,,,,,D,,,,,,,,,,,,D, +12. Deep Learning & Representation Learning,Transformers / generative models,,,,,,,,,I,I,,,,,,,D,,,,,,,,,,,,E, 13. Computer Vision & Image Analysis,Image preprocessing / augmentation,,,,,,,,,D,D,,I,,,,,D,,,,,,,,,,,,, -13. Computer Vision & Image Analysis,Convolution / pooling / CNN architectures,,,,,,,,,E,E,,I,,,,,E,,,,,,,,,,,,D, +13. Computer Vision & Image Analysis,Convolution / pooling / CNN architectures,,,,,,,,,E,E,,I,,,,,E,,,,,,,,,,D,D,, 13. Computer Vision & Image Analysis,Pretrained vision models (ResNet/VGG/AlexNet),,,,,,,,,D,D,,,,,,,D,,,,,,,,,,,,, -13. Computer Vision & Image Analysis,Applied vision projects (MNIST/CIFAR/medical),,,,,,,,,E,E,,,,,,,E,,,,,,,,,,,,D, +13. Computer Vision & Image Analysis,Applied vision projects (MNIST/CIFAR/medical),,,,,,,,,E,E,,,,,,,E,,,,,,,,,,,,, "14. Responsible AI, Ethics, Privacy, XAI & Deployment",Ethics / privacy / data security,I,,,,,,,,,,,,,,I,,,I,,,,,,,,,,,, "14. Responsible AI, Ethics, Privacy, XAI & Deployment",Bias (human/cognitive) & responsible AI,I,,,,,,,,,,,D,,,,,,,,,,,,,,,,,, "14. Responsible AI, Ethics, Privacy, XAI & Deployment",Explainable AI (SHAP/LIME/Grad-CAM),,,,,,,,,,,,E,,,,,,,,,,,,,,,,,, "14. Responsible AI, Ethics, Privacy, XAI & Deployment",Black-box vs white-box / transparency,,,,,,,,,,,,E,,,,,,,,,,,,,,,,,, "14. Responsible AI, Ethics, Privacy, XAI & Deployment",Model deployment (Streamlit/Gradio/Flask),,,,,,,,,,,,E,,,,,,,,,,,E,,,,,,D, "14. Responsible AI, Ethics, Privacy, XAI & Deployment","Causal inference (propensity, IPW, ATE/ATT)",,,,,,,,,,,,,,,I,,,,,,,D,,,,,,,, -"15. Capstone, Communication & Career Readiness",Project selection / team formation / advising,,,,,,,,,,,D,D,D,D,,,E,I,I,D,D,,E,,,,,,,D +"15. Capstone, Communication & Career Readiness",Project selection / team formation / advising,,,,,,,,,,,D,D,D,D,,,E,I,I,D,D,,E,,,,,,I,D "15. Capstone, Communication & Career Readiness",Peer review / project defense / progress reports,,,,,,,,,,,D,E,,I,,,D,,,D,I,,D,,,,,,, "15. Capstone, Communication & Career Readiness",Final reports / posters / oral presentations,D,,,,,,,,,,E,E,D,E,,,D,D,D,E,E,,E,,,,,,, "15. Capstone, Communication & Career Readiness",Reproducibility & repo quality,,,,,,,,,,,I,D,,I,,,E,,,D,D,,I,,,,,,,