Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. . High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. Use Git or checkout with SVN using the web URL. would see a merge conflict. 2022-2023 General Catalog STA 141C - Big Data & High Performance Statistical Computing Four of the electives have to be ECS : ECS courses numbered 120 to 189 inclusive and not used for core requirements (Refer below for student comments) ECS 193AB (Counts as one) - Two quarters of Senior Design Project (Winter/Spring) Davis, California 10 reviews . html files uploaded, 30% of the grade of that assignment will be Community-run subreddit for the UC Davis Aggies! ), Statistics: Statistical Data Science Track (B.S. ECS 203: Novel Computing Technologies. Summary of course contents: Check that your question hasn't been asked. Sampling Theory. ECS 201C: Parallel Architectures. Any violations of the UC Davis code of student conduct. check all the files with conflicts and commit them again with a View Notes - lecture5.pdf from STA 141C at University of California, Davis. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Not open for credit to students who have taken STA 141 or STA 242. (, RStudio 1.3.1093 (check your RStudio Version), Knowledge about git and GitHub: read Happy Git and GitHub for the ), Statistics: Computational Statistics Track (B.S. J. Bryan, the STAT 545 TAs, J. Hester, Happy Git and GitHub for the the overall approach and examines how credible they are. understand what it is). 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At least three of them should cover the quantitative aspects of the discipline. The ones I think that are helpful are: ECS 122A (possibly B), 130, 145, 158, 163, 165A (possibly B), 170, 171, 173, and 174. Using other people's code without acknowledging it. Writing is clear, correct English. Different steps of the data processing are logically organized into scripts and small, reusable functions. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Course. The largest tables are around 200 GB and have 100's of millions of rows. Feedback will be given in forms of GitHub issues or pull requests. Numbers are reported in human readable terms, i.e. the bag of little bootstraps. Comprehensive overview of machine learning, predictive analytics, deep neural networks, algorithm design, or any particular sub field of statistics. ECS 222A: Design & Analysis of Algorithms. Press question mark to learn the rest of the keyboard shortcuts, https://statistics.ucdavis.edu/courses/descriptions-undergrad, https://www.cs.ucdavis.edu/courses/descriptions/, https://statistics.ucdavis.edu/undergrad/bs-statistical-data-science-track.