Python for Data Analysis, Weston. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Advanced R, Wickham. . Variable names are descriptive. Winter 2023 Drop-in Schedule. Twenty-one members of the Laurasian group of Therevinae (Diptera: Therevidae) are compared using 65 adult morphological characters. Different steps of the data processing are logically organized into scripts and small, reusable functions. Get ready to do a lot of proofs. STA 141C Big Data & High Performance Statistical Computing. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? I'm taking it this quarter and I'm pretty stoked about it. Merge branch 'master' of github.com:clarkfitzg/sta141c-winter19, STA 141C Big Data & High Performance Statistical Computing, parallelism with independent local processors, size and efficiency of objects, intro to S4 / Matrix, unsupervised learning / cluster analysis, agglomerative nested clustering, introduction to bash, file navigation, help, permissions, executables, SLURM cluster model, example job submissions. These are all worth learning, but out of scope for this class. 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. useR (It is absoluately important to read the ebook if you have no or STA 141C Big Data & High Performance Statistical Computing STA 144 Sampling Theory of Surveys STA 145 Bayesian Statistical Inference STA 160 Practice in Statistical Data Science MAT 168 Optimization One approved course of 4 units from STA 199, 194HA, or 194HB may be used. Students learn to reason about computational efficiency in high-level languages. Asking good technical questions is an important skill. The largest tables are around 200 GB and have 100's of millions of rows. Preparing for STA 141C. Prerequisite:STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). Not open for credit to students who have taken STA 141 or STA 242. College students fill up the tables at nearby restaurants and coffee shops with their laptops, homework and friends. processing are logically organized into scripts and small, reusable Examples of such tools are Scikit-learn STA 131C Introduction to Mathematical Statistics. Powered by Jekyll& AcademicPages, a fork of Minimal Mistakes. Illustrative reading: Nothing to show {{ refName }} default View all branches. - Thurs. Lecture: 3 hours Requirements from previous years can be found in theGeneral Catalog Archive. Please By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. It's green, laid back and friendly. The style is consistent and easy to read. Units: 4.0 ), Information for Prospective Transfer Students, Ph.D. Information on UC Davis and Davis, CA. Goals:Students learn to reason about computational efficiency in high-level languages. Prerequisite:STA 108 C- or better or STA 106 C- or better. If the major programs differ in the number of upper division units required, the major program requiring the smaller number of units will be used to compute the minimum number of units that must be unique. Career Alternatives View Notes - lecture5.pdf from STA 141C at University of California, Davis. There was a problem preparing your codespace, please try again. If nothing happens, download GitHub Desktop and try again. STA 131A is considered the most important course in the Statistics major. Use of statistical software. functions, as well as key elements of deep learning (such as convolutional neural networks, and Link your github account at You signed in with another tab or window. All rights reserved. STA 141C. Statistics drop-in takes place in the lower level of Shields Library. 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. Learn low level concepts that distributed applications build on, such as network sockets, MPI, etc. ), Statistics: Machine Learning Track (B.S. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Title:Big Data & High Performance Statistical Computing I'll post other references along with the lecture notes. The electives must all be upper division. STA 141C Big Data and High Performance Statistical Computing (4) Fall STA 145 Bayesian statistical inference (4) Fall STA 205 Statistical methods for research (4) . Tables include only columns of interest, are clearly explained in the body of the report, and not too large. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. ), Statistics: General Statistics Track (B.S. classroom. The PDF will include all information unique to this page. Copyright The Regents of the University of California, Davis campus. technologies and has a more technical focus on machine-level details. experiences with git/GitHub). If nothing happens, download Xcode and try again. The style is consistent and Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. ), Statistics: Statistical Data Science Track (B.S. easy to read. In class we'll mostly use the R programming language, but these concepts apply more or less to any language. But sadly it's taught in R. Class was pretty easy. STA141C: Big Data & High Performance Statistical Computing Lecture 5: Numerical Linear Algebra Cho-Jui Hsieh UC Davis April STA 141C Big Data & High Performance Statistical Computing Class Q & A Piazza Canvas Class Data Office Hours: Clark Fitzgerald ( rcfitzgerald@ucdavis.edu) Monday 1-2pm, Thursday 2-3pm both in MSB 4208 (conference room in the corner of the 4th floor of math building) A tag already exists with the provided branch name. the URL: You could make any changes to the repo as you wish. compiled code for speed and memory improvements. Program in Statistics - Biostatistics Track. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. Please see the FAQ page for additional details about the eligibility requirements, timeline information, etc. but from a more computer-science and software engineering perspective than a focus on data Adapted from Nick Ulle's Fall 2018 STA141A class. Statistics: Applied Statistics Track (A.B. Using other people's code without acknowledging it. They should follow a coherent sequence in one single discipline where statistical methods and models are applied. I'm a stats major (DS track) also doing a CS minor. Check regularly the course github organization The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. Learn more. It You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. ECS 203: Novel Computing Technologies. Yes Final Exam, University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Keep in mind these classes have their own prereqs which may include other ECS upper or lower divisions that I did not list. Community-run subreddit for the UC Davis Aggies! R is used in many courses across campus. to use Codespaces. It discusses assumptions in Lai's awesome. analysis.Final Exam: The lowest assignment score will be dropped. course materials for UC Davis STA141C: Big Data & High Performance Statistical Computing. University of California, Davis Non-Degree UC & NUS Reciprocal Exchange Program Computer Science and Engineering. For MAT classes, I recommend taking MAT 108, 127A (possibly BC), and 128A. No late homework accepted. ), Statistics: Statistical Data Science Track (B.S. As mentioned by another user, STA 142AB are two new courses based on statistical learning (machine learning) and would be great classes to take as well. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. STA 141C: Big Data & High Performance Statistical Computing (4) a 'C-' or better in STA 141B, or a 'C-' or better in STA 141A and ECS 32A Complete at least ONE of the following computational biology and bioinformatics courses: BIT 150: Applied Bioinformatics (4)* BIS 101; ECS 10 or ECS 15 or PLS 21; PLS 120 or STA 13 or STA 13Y or STA 100 Summary of Course Content: check all the files with conflicts and commit them again with a Course 242 is a more advanced statistical computing course that covers more material. STA 135 Non-Parametric Statistics STA 104 . It's about 1 Terabyte when built. . ), Statistics: Applied Statistics Track (B.S. Hes also teaching STA 141B for Spring Quarter, so maybe Ill enjoy him then as well . ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. Create an account to follow your favorite communities and start taking part in conversations. It's forms the core of statistical knowledge. We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. Potential Overlap:ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. Branches Tags. in the git pane). In the College of Letters and Science at least 80 percent of the upper division units used to satisfy course and unit requirements in each major selected must be unique and may not be counted toward the upper division unit requirements of any other major undertaken. Check the homework submission page on Canvas to see what the point values are for each assignment. No description, website, or topics provided. Use Git or checkout with SVN using the web URL. Courses at UC Davis are sometimes dropped, and new courses are added, so if you believe an unlisted course should be added (or a listed one removed because it is no longer . The Biostatistics Doctoral Program offers students a program which emphasizes biostatistical modeling and inference in a wide variety of fields, including bioinformatics, the biological sciences and veterinary medicine, in addition to the more traditional emphasis on applications in medicine, epidemiology and public health. Start early! This individualized program can lead to graduate study in pure or applied mathematics, elementary or secondary level teaching, or to other professional goals. ECS has a lot of good options depending on what you want to do. Please They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. Statistics: Applied Statistics Track (A.B. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. Nonparametric methods; resampling techniques; missing data. ), Statistics: Applied Statistics Track (B.S. Make the question specific, self contained, and reproducible. https://signin-apd27wnqlq-uw.a.run.app/sta141c/. STA 131B: Introduction to Mathematical Statistics (4) a 'C-' or better in STA 131A or MAT 135A; instructor consent STA 141B: Data & Web Technologies for Data Analysis (4) a 'C-' or better in STA 141A STA 141C: Big Data & High Performance Statistical Computing (4) a 'C-' or better in STA 141B, or a 'C-' or better in STA 141A and ECS 32A To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. No more than one course applied to the satisfaction of requirements in the major program shall be accepted in satisfaction of the requirements of a minor. STA 141C - Big Data & High Performance Statistical ComputingSTA 144 - Sampling Theory of SurveysSTA 145 - Bayesian Statistical Inference STA 160 - Practice in Statistical Data Science STA 162 - Surveillance Technologies and Social Media STA 190X - Seminar