Course materials for UCLA's STATS 418 - Tools in Data Science covering R packages, machine learning libraries, databases, and reproducibility tools.
teach-data-science-UCLA-master-appl-stats is a collection of course materials for UCLA's STATS 418 - Tools in Data Science graduate course. It provides structured learning resources covering advanced data science tools including R packages, machine learning libraries, databases, and reproducibility workflows. The materials help bridge statistical theory with practical implementation using technologies data scientists use in industry.
Graduate students in statistics or data science programs, particularly those in UCLA's Master of Applied Statistics program. Also useful for self-learners seeking structured curriculum on practical data science tools beyond basic statistical concepts.
Provides a comprehensive, university-level curriculum focused on practical data science tools rather than just theory. The materials are designed by industry-experienced instructors and cover the complete data science workflow from data acquisition to model deployment.
Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA
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Covers the full data science workflow from data acquisition to model deployment, as detailed in the 10-week syllabus including tools like ggplot2, shiny, and Hadoop.
Includes advanced R packages and big data technologies like Spark and H2O, which are commonly used in professional data science environments, as outlined in the course objectives.
Integrates Rmarkdown, Jupyter notebooks, and git/Github for collaborative and reproducible data analysis, as highlighted in Week 3 of the syllabus.
Led by Szilárd Pafka with guest speakers from industry, ensuring practical insights and real-world application, as noted in the instructors section.
The course primarily uses R, with limited coverage of Python, which might not align with teams or projects preferring Python ecosystems, as indicated by the reliance on R packages.
Requires completion of STATS 404 and 405, making it inaccessible for those without foundational knowledge in statistical computing and data management, as stated in the prerequisites.
As archived course materials, it lacks interactive elements, live support, or updated content to reflect current tool trends, unlike live courses with ongoing revisions.