Parallel R, McCallum & Weston. 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. Courses at UC Davis. ECS classes: https://www.cs.ucdavis.edu/courses/descriptions/, Statistics (data science emphasis) major requirements: https://statistics.ucdavis.edu/undergrad/bs-statistical-data-science-track. To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you Oh yeah, since STA 141B is full for Winter Quarter, I'm going to take STA 141C instead since the prereqs are STA 141B or STA 141A and ECS 32A at the same time. STA 141C Combinatorics MAT 145 . functions, as well as key elements of deep learning (such as convolutional neural networks, and UC Davis Veteran Success Center . I'm a stats major (DS track) also doing a CS minor. The environmental one is ARE 175/ESP 175. Title:Big Data & High Performance Statistical Computing 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. to use Codespaces. Discussion: 1 hour. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Sampling Theory. would see a merge conflict. Tables include only columns of interest, are clearly explained in the body of the report, and not too large. Different steps of the data processing are logically organized into scripts and small, reusable functions. STA 141C was in R, and we focused on managing very big data and how to do stuff with it, as well as some parallel computing stuff and some theory behind it. 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. If nothing happens, download Xcode and try again. Check regularly the course github organization Career Alternatives All rights reserved. You can walk or bike from the main campus to the main street in a few blocks. If there is any cheating, then we will have an in class exam. Review UC Davis course notes for STA STA 104 to get your preparate for upcoming exams or projects. I'm trying to get into ECS 171 this fall but everyone else has the same idea. STA 141A Fundamentals of Statistical Data Science. STA 142 series is being offered for the first time this coming year. The ones I think that are helpful are: ECS 122A (possibly B), 130, 145, 158, 163, 165A (possibly B), 170, 171, 173, and 174. Please Here is where you can do this: For private or sensitive questions you can do private posts on Piazza or email the instructor or TA. includes additional topics on research-level tools. Advanced R, Wickham. to parallel and distributed computing for data analysis and machine learning and the This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. J. Bryan, the STAT 545 TAs, J. Hester, Happy Git and GitHub for the No late homework accepted. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Coursicle. From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. Tables include only columns of interest, are clearly 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. Check that your question hasn't been asked. This track emphasizes statistical applications. classroom. We then focus on high-level approaches The code is idiomatic and efficient. Format: You can find out more about this requirement and view a list of approved courses and restrictions on the. A tag already exists with the provided branch name. ), Statistics: Machine Learning Track (B.S. Powered by Jekyll& AcademicPages, a fork of Minimal Mistakes. in the git pane). Examples of such tools are Scikit-learn It is recommendedfor studentswho are interested in applications of statistical techniques to various disciplines includingthebiological, physical and social sciences. Contribute to ebatzer/STA-141C development by creating an account on GitHub. for statistical/machine learning and the different concepts underlying these, and their Restrictions: understand what it is). All rights reserved. It discusses assumptions in Two introductory courses serving as the prerequisites to upper division courses in a chosen discipline to which statistics is applied, STA 141A Fundamentals of Statistical Data Science, STA 130A Mathematical Statistics: Brief Course, STA 130B Mathematical Statistics: Brief Course, STA 141B Data & Web Technologies for Data Analysis, STA 160 Practice in Statistical Data Science. Create an account to follow your favorite communities and start taking part in conversations. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. sign in When I took it, STA 141A was coding and data visualization in R, and doing analysis based on our code and visuals. Prerequisite:STA 108 C- or better or STA 106 C- or better. Work fast with our official CLI. R is used in many courses across campus. One approved course of 4 units from STA 199, 194HA, or 194HB may be used. STA 010. This is an experiential course. Course. Catalog Description:Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. long short-term memory units). Please Subscribe today to keep up with the latest ITS news and happenings. The style is consistent and easy to read. The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. indicate what the most important aspects are, so that you spend your They learn to map mathematical descriptions of statistical procedures to code, decompose a problem into sub-tasks, and to create reusable functions. Writing is clear, correct English. 1. Feedback will be given in forms of GitHub issues or pull requests. Its such an interesting class. 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 sign in Start early! This course overlaps significantly with the existing course 141 course which this course will replace. Variable names are descriptive. A.B. Requirements from previous years can be found in theGeneral Catalog Archive. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. - Thurs. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. Academia.edu is a platform for academics to share research papers. R Graphics, Murrell. Course 242 is a more advanced statistical computing course that covers more material. The report points out anomalies or notable aspects of the data are accepted. Program in Statistics - Biostatistics Track. Statistics: Applied Statistics Track (A.B. Lingqing Shen: Fall 2018 undergraduate exchange student at UC-Davis, from Nanjing University. Check the homework submission page on Canvas to see what the point values are for each assignment. Goals:Students learn to reason about computational efficiency in high-level languages. hushuli/STA-141C. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? This is your opportunity to pursue a question that you are personally interested in as you create a public 'portfolio project' that shows off your big data processing skills to potential employers or admissions committees. assignment. The report points out anomalies or notable aspects of the data discovered over the course of the analysis. Prerequisite: STA 108 C- or better or STA 106 C- or better. STA 144. Copyright The Regents of the University of California, Davis campus. ), Information for Prospective Transfer Students, Ph.D. Press J to jump to the feed. Information on UC Davis and Davis, CA. Davis is the ultimate college town. It discusses assumptions in the overall approach and examines how credible they are. The B.S. ), Statistics: Machine Learning Track (B.S. They develop ability to transform complex data as text into data structures amenable to analysis. (, G. Grolemund and H. Wickham, R for Data Science He's also my favorite econ professor here at Davis, but I know a few people who really don't like him. Learn more. Including a handful of lines of code is usually fine. Open RStudio -> New Project -> Version Control -> Git -> paste the URL: https://github.com/ucdavis-sta141c-2021-winter/sta141c-lectures.git Choose a directory to create the project You could make any changes to the repo as you wish. They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. ), Statistics: Computational Statistics Track (B.S. Storing your code in a publicly available repository. I'm actually quite excited to take them. The prereqs for 142A are STA 141A and 131A/130A/MAT 135 while the prereqs for 142B are 142A and 131B/130B. explained in the body of the report, and not too large. ), Statistics: Applied Statistics Track (B.S. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. master. MAT 108 - Introduction to Abstract Mathematics the bag of little bootstraps.Illustrative Reading: It moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to compiled code for speed and memory improvements. Lai's awesome. We also take the opportunity to introduce statistical methods Adapted from Nick Ulle's Fall 2018 STA141A class. STA 131C Introduction to Mathematical Statistics Units: 4 Format: Lecture: 3 hours Discussion: 1 hour Catalog Description: Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. All rights reserved. The Art of R Programming, Matloff. This course teaches the fundamentals of R and in more depth that is intentionally not done in these other courses. STA 137 and 138 are good classes but are more specific, for example if you want to get into finance/FinTech, then STA 137 is a must-take. Statistics drop-in takes place in the lower level of Shields Library. University of California, Davis Non-Degree UC & NUS Reciprocal Exchange Program Computer Science and Engineering. STA 221 - Big Data & High Performance Statistical Computing, Statistics: Applied Statistics Track (A.B. degree program has one track. Preparing for STA 141C. First stats class I actually enjoyed attending every lecture. I'd also recommend ECN 122 (Game Theory). ), Statistics: General Statistics Track (B.S. STA 141C Big Data & High Performance Statistical Computing. STA 131A is considered the most important course in the Statistics major. We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? Replacement for course STA 141. A tag already exists with the provided branch name. technologies and has a more technical focus on machine-level details. solves all the questions contained in the prompt, makes conclusions that are supported by evidence in the data, discusses efficiency and limitations of the computation. School: College of Letters and Science LS Minor Advisors For a current list of faculty and staff advisors, see Undergraduate Advising. Branches Tags. STA 013Y. Using short snippets of code (5 lines or so) from lecture, Piazza, or other sources. I encourage you to talk about assignments, but you need to do your own work, and keep your work private. Several new electives -- including multiple EEC classes and STA 131B,STA 141B and STA 141C -- have been added t This course provides an introduction to statistical computing and data manipulation. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. 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. But sadly it's taught in R. Class was pretty easy. ), Information for Prospective Transfer Students, Ph.D. STA 135 Non-Parametric Statistics STA 104 . Course 242 is a more advanced statistical computing course that covers more material. All rights reserved. Computing, https://rmarkdown.rstudio.com/lesson-1.html, https://github.com/ucdavis-sta141c-2021-winter/sta141c-lectures.git, https://signin-apd27wnqlq-uw.a.run.app/sta141c/, https://github.com/ucdavis-sta141c-2021-winter. A list of pre-approved electives can be foundhere. https://signin-apd27wnqlq-uw.a.run.app/sta141c/. Create an account to follow your favorite communities and start taking part in conversations. This course provides the foundations and practical skills for other statistical methods courses that make use of computing, and also subsequent statistical computing courses. 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. This is to indicate what the most important aspects are, so that you spend your time on those that matter most. Potential Overlap:This course overlaps significantly with the existing course 141 course which this course will replace. You're welcome to opt in or out of Piazza's Network service, which lets employers find you. It enables students, often with little or no background in computer programming, to work with raw data and introduces them to computational reasoning and problem solving for data analysis and statistics. As for CS, I've heard that after you take ECS 36C, you theoretically know everything you need for a programming job. The code is idiomatic and efficient. I would pick the classes that either have the most application to what you want to do/field you want to end up in, or that you're interested in. ECS 201C: Parallel Architectures. Davis, California 10 reviews . For MAT classes, I recommend taking MAT 108, 127A (possibly BC), and 128A. https://github.com/ucdavis-sta141c-2021-winter for any newly posted STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end.
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