Data Science Curriculum Pathway

Course Skills (by Course)

CSC 201: Intro to Data Science	CSC 303: Data Science Foundations	CSC 461: Data Mining & ML	CSC 463: Artificial Intelligence	CSC 477: Visualizations
Identify data types, collection methods, licensing, and trusted data sources.	Master advanced R programming for efficient, maintainable code.	Extract, merge, clean, and prepare datasets for analysis.	Explain ML concepts and implement classical & deep learning algorithms.	Explain the role and principles of data visualization in data science.
Acquire, clean, and preprocess data across multiple platforms.	Perform advanced data manipulation with dplyr and data.table.	Create and interpret feature exploration visualizations.	Build deep learning neural networks.	Conduct EDA to guide visualization choices.
Conduct exploratory data analysis to identify patterns and trends.	Develop static & interactive visualizations using ggplot2, Plotly, and Shiny.	Select and interpret linear models with feature selection.	Build CNN models.	Create static visualizations with ggplot2, Matplotlib, Seaborn, and Plotly.
Create clear and informative visualizations.	Apply regression, ANOVA, logistic regression, and diagnostics.	Design, train, and deploy ML models.	Build Keras neural networks.	Develop interactive visualizations using Plotly and Bokeh.
Perform association rule mining.	Implement supervised (classification/regression) and unsupervised (clustering/dimensionality reduction) learning.	Evaluate and validate predictive models.	Use TensorFlow & PyTorch for NN design, testing, and evaluation.	Apply design principles for clarity, storytelling, and accessibility.
Communicate findings effectively to varied audiences.	Analyze time series and perform text mining/sentiment analysis.	Make predictions from trained models.	Apply AI techniques for data-driven decision-making.	Implement advanced visualizations (heatmaps, treemaps, geospatial, 3D).
—	Apply advanced ML algorithms (decision trees, SVMs) to real-world case studies.	—	Select algorithms based on performance and efficiency.	Design dashboards and craft compelling narratives.
—	Present and critique final course projects.	—	Evaluate AI progress and address ethical concerns.	Apply ethical principles to ensure truthful and unbiased visualizations.
Survey, Introductory	Foundations	Advanced ML, Mining, Metrics, Deployment	Advanced DL, LLM, RL, RAG	Master Visualization

Course Goals

Course	Goal
CSC 201	Introductory survey of data science concepts; build literacy in data acquisition, cleaning, and exploratory analysis.
CSC 303	Strengthen core data science skills with advanced R methods; prepare for higher-level machine learning and AI courses.
CSC 461	Apply data mining and specific ML algorithms, with focus on metrics, diagnostics, and deployment.
CSC 463	Apply advanced AI methods including deep learning, RL, LLMs, and retrieval-augmented generation; evaluate ethical implications.
CSC 477	Master data visualization theory and tools; communicate insights effectively through static, interactive, and dashboard formats.

Prerequisites, Tools, and Math Requirements

Course	Prerequisite(s)	Languages & Tools	Books	Math Level
CSC 201	MAT 232	R, Excel	Wickham	Intro Prob & Stat
CSC 303	CSC 201 and (MAT 232 or MAT 325)	R	DS4A + Hastie + Wickham	Prob & Stat + Calc + Linear Alg.
CSC 461	CSC 201 and (MAT 232 or MAT 325)	R	—	—
CSC 463	CSC 201 and (MAT 232 or MAT 325)	R, Python	DS4A + Russell	Prob & Stat + Calc + Linear Alg.
CSC 477	None	R, Python, Tableau, Shiny, Quarto, Jupyter	Own Notes + Wickham + Ryan Lindy	Intro Prob & Stat