), Information for Prospective Transfer Students, Ph.D. The class will cover the following topics. STA 141B: Data & Web Technologies for Data Analysis (previously has used Python) STA 141C: Big Data & High Performance Statistical Computing STA 144: Sample Theory of Surveys STA 145: Bayesian Statistical Inference STA 160: Practice in Statistical Data Science STA 206: Statistical Methods for Research I STA 207: Statistical Methods for Research II 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. ), Information for Prospective Transfer Students, Ph.D. To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you 10 AM - 1 PM. 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. 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 We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. Highperformance computing in highlevel data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; highlevel parallel computing; MapReduce; parallel algorithms and reasoning. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. course materials for UC Davis STA141C: Big Data & High Performance Statistical Computing. Storing your code in a publicly available repository. indicate what the most important aspects are, so that you spend your STA 13. University of California, Davis Non-Degree UC & NUS Reciprocal Exchange Program Computer Science and Engineering. Prerequisite(s): STA 015BC- or better. deducted if it happens. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. STA 141C Big Data & High Performance Statistical Computing. Use of statistical software. ), Statistics: General Statistics Track (B.S. ), Statistics: Applied Statistics Track (B.S. Goals:Students learn to reason about computational efficiency in high-level languages. sign in A tag already exists with the provided branch name. All rights reserved. As for CS, I've heard that after you take ECS 36C, you theoretically know everything you need for a programming job. . STA 141B Data Science Capstone Course STA 160 . For MAT classes, I recommend taking MAT 108, 127A (possibly BC), and 128A. Mon. Go in depth into the latest and greatest packages for manipulating data. He's also my favorite econ professor here at Davis, but I know a few people who really don't like him. Get ready to do a lot of proofs. It Information on UC Davis and Davis, CA. Students will learn how to work with big data by actually working with big data. the following information: (Adapted from Nick Ulle and Clark Fitzgerald ). We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. If there is any cheating, then we will have an in class exam. advantages and disadvantages. Summary of course contents: the bag of little bootstraps.Illustrative Reading: Comprehensive overview of machine learning, predictive analytics, deep neural networks, algorithm design, or any particular sub field of statistics. STA 010. Discussion: 1 hour. Lecture: 3 hours STA 141C - Big-data and Statistical Computing[Spring 2021] STA 141A - Statistical Data Science[Fall 2019, 2021] STA 103 - Applied Statistics[Winter 2019] STA 013 - Elementary Statistics[Fall 2018, Spring 2019] Sitemap Follow: GitHub Feed 2023 Tesi Xiao. Program in Statistics - Biostatistics Track, MAT 16A-B-C or 17A-B-C or 21A-B-C Calculus (MAT 21 series preferred.). Summary of course contents:This course explores aspects of scaling statistical computing for large data and simulations. The PDF will include all information unique to this page. Course 242 is a more advanced statistical computing course that covers more material. You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. Effective Term: 2020 Spring Quarter. ECS 220: Theory of Computation. STA141C: Big Data & High Performance Statistical Computing Lecture 12: Parallel Computing Cho-Jui Hsieh UC Davis June 8, Create an account to follow your favorite communities and start taking part in conversations. The fastest machine in the world as of January, 2019 is the Oak Ridge Summit Supercomputer. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. in the git pane). This course provides an introduction to statistical computing and data manipulation. Relevant Coursework and Competition: . All rights reserved. 10 AM - 1 PM. ), Statistics: Applied Statistics Track (B.S. We'll use the raw data behind usaspending.gov as the primary example dataset for this class. ), Statistics: General Statistics Track (B.S. ECS 203: Novel Computing Technologies. There will be around 6 assignments and they are assigned via GitHub discovered over the course of the analysis. like. Advanced R, Wickham. You may find these books useful, but they aren't necessary for the course. ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. Use Git or checkout with SVN using the web URL. It discusses assumptions in the overall approach and examines how credible they are. where appropriate. Cladistic analysis using parsimony on the 17 ingroup and 4 outgroup taxa provides a well-supported hypothesis of relationships among taxa within the Cyclotelini, tribe nov. ), Statistics: General Statistics Track (B.S. If nothing happens, download GitHub Desktop and try again. These are all worth learning, but out of scope for this class. A list of pre-approved electives can be foundhere. 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. Online with Piazza. ), Statistics: Machine Learning Track (B.S. Writing is Examples of such tools are Scikit-learn functions, as well as key elements of deep learning (such as convolutional neural networks, and long short-term memory units). Please see the FAQ page for additional details about the eligibility requirements, timeline information, etc. Press J to jump to the feed. understand what it is). Students learn to reason about computational efficiency in high-level languages. Press J to jump to the feed. mid quarter evaluation, bash pipes and filters, students practice SLURM, review course suggestions, bash coding style guidelines, Python Iterators, generators, integration with shell pipeleines, bootstrap, data flow, intermediate variables, performance monitoring, chunked streaming computation, Develop skills and confidence to analyze data larger than memory, Identify when and where programs are slow, and what options are available to speed them up, Critically evaluate new data technologies, and understand them in the context of existing technologies and concepts. 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. Check regularly the course github organization STA 141A Fundamentals of Statistical Data Science; prereq STA 108 with C- or better or 106 with C- or better. ), Statistics: Machine Learning Track (B.S. It can also reflect a special interest such as computational and applied mathematics, computer science, or statistics, or may be combined with a major in some other field. STA 141C. specifically designed for large data, e.g. Governance, International Baccalaureate Credit & Chart, Cal Aggie Student Alumni Association (SAA), University Policies on Nondiscrimination, Sexual Harassment/Sexual Violence, Student Records & Privacy, Campus Security, Crime Awareness, and Alcohol & Drug Abuse Prevention, Office of Educational Opportunity & Enrichment Services, Nondiscrimination & Sexual Harassment/Sexual Violence Prevention, Associated Students, University of California at Davis (ASUCD), CalTeach/Mathematics & Science Teaching Program (CalTeach/MAST), Center for Advocacy, Resources & Education (CARE), Center for Chicanx/Latinx Academic Student Success (CCLASS), Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual Resource Center (LGBTQIARC), Native American Academic Student Success Center (NAASSC), Services for International Students & Scholars (SISS), Strategic Asian and Pacific Islander Retention Initiative (SAandPIRI), Women's Resources & Research 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School University of California, Davis Course Title STA 141C Type Notes Uploaded By DeanKoupreyMaster1014 Pages 44 This preview shows page 1 - 15 out of 44 pages. For the group project you will form groups of 2-3 and pursue a more open ended question using the usaspending data set. Course 242 is a more advanced statistical computing course that covers more material. classroom. You can view a list ofpre-approved courseshere. View full document STA141C: Big Data & High Performance Statistical Computing Lecture 1: Python programming (1) Cho-Jui Hsieh UC Davis April 4, 2017 I'm taking it this quarter and I'm pretty stoked about it. The environmental one is ARE 175/ESP 175. Plots include titles, axis labels, and legends or special annotations Please I took it with David Lang and loved it. are accepted. STA 142A. They develop ability to transform complex data as text into data structures amenable to analysis. Illustrative reading: Those classes have prerequisites, so taking STA 32 and STA 108 is probably the best if you want to take them. Open RStudio -> New Project -> Version Control -> Git -> paste If nothing happens, download Xcode and try again. ), Statistics: Applied Statistics Track (B.S. includes additional topics on research-level tools. One approved course of 4 units from STA 199, 194HA, or 194HB may be used. They should follow a coherent sequence in one single discipline where statistical methods and models are applied. Subscribe today to keep up with the latest ITS news and happenings. You can find out more about this requirement and view a list of approved courses and restrictions on the. Program in Statistics - Biostatistics Track. Probability and Statistics by Mark J. Schervish, Morris H. DeGroot 4th Edition 2014, Pearson, University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. The style is consistent and easy to read. Tables include only columns of interest, are clearly Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. useR (, J. Bryan, Data wrangling, exploration, and analysis with R Could not load tags. The code is idiomatic and efficient. ), Statistics: Machine Learning Track (B.S. Requirements from previous years can be found in theGeneral Catalog Archive. The electives must all be upper division. 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. ), Statistics: General Statistics Track (B.S. Check the homework submission page on . Nonparametric methods; resampling techniques; missing data. STA141C: Big Data & High Performance Statistical Computing Lecture 9: Classification Cho-Jui Hsieh UC Davis May 18, Information on UC Davis and Davis, CA. 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. You're welcome to opt in or out of Piazza's Network service, which lets employers find you. They learn to map mathematical descriptions of statistical procedures to code, decompose a problem into sub-tasks, and to create reusable functions. Assignments must be turned in by the due date. One thing you need to decide is if you want to go to grad school for a MS in statistics or CS as they'll have different requirements. The grading criteria are correctness, code quality, and communication. Hadoop: The Definitive Guide, White.Potential Course Overlap: STA 141C Combinatorics MAT 145 . However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. You signed in with another tab or window. ggplot2: Elegant Graphics for Data Analysis, Wickham. Format: The B.S. Replacement for course STA 141. Writing is clear, correct English. Open RStudio -> New Project -> Version Control -> Git -> paste the URL: https://github.com/ucdavis-sta141b-2021-winter/sta141b-lectures.git Choose a directory to create the project You could make any changes to the repo as you wish. ), Information for Prospective Transfer Students, Ph.D. Sampling Theory. ), Statistics: Computational Statistics Track (B.S. Stat Learning II. Nothing to show Lai's awesome. Career Alternatives The Art of R Programming, Matloff. Community-run subreddit for the UC Davis Aggies! To make a request, send me a Canvas message with Lecture: 3 hours 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 We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. Switch branches/tags. School: UC Davis Course Title: STA 131 Type: Homework Help Professors: ztan, JIANG,J View Documents 4 pages STA131C_Assignment2_solution.pdf | Fall 2008 School: UC Davis Course Title: STA 131 Type: Homework Help Professors: ztan, JIANG,J View Documents 6 pages Worksheet_7.pdf | Spring 2010 School: UC Davis Former courses ECS 10 or 30 or 40 may also be used. to use Codespaces. ), Information for Prospective Transfer Students, Ph.D. Using other people's code without acknowledging it. One of the most common reasons is not having the knitted ECS 201C: Parallel Architectures. Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. 31 billion rather than 31415926535. Lecture content is in the lecture directory. 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. Contribute to ebatzer/STA-141C development by creating an account on GitHub. explained in the body of the report, and not too large. Not open for credit to students who have taken STA 141 or STA 242. Currently ACO PhD student at Tepper School of Business, CMU. This individualized program can lead to graduate study in pure or applied mathematics, elementary or secondary level teaching, or to other professional goals. Copyright The Regents of the University of California, Davis campus. https://github.com/ucdavis-sta141c-2021-winter for any newly posted Prerequisite: STA 108 C- or better or STA 106 C- or better. Advanced R, Wickham. History: Stat Learning I. STA 142B. 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) We then focus on high-level approaches processing are logically organized into scripts and small, reusable UC Davis history. If nothing happens, download Xcode and try again. Yes Final Exam, University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? Lecture: 3 hours ), Statistics: Machine Learning Track (B.S. new message. like: The attached code runs without modification. Asking good technical questions is an important skill. Work fast with our official CLI. Four upper division elective courses outside of statistics: compiled code for speed and memory improvements. We first opened our doors in 1908 as the University Farm, the research and science-based instruction extension of UC Berkeley. ECS 158 covers parallel computing, but uses different experiences with git/GitHub). The Department offers a minor program in Statistics that consists of five upper division level courses focusing on the fundamentals of mathematical statistics and of the most widely used applied statistical methods. Canvas to see what the point values are for each assignment. 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. ), Statistics: Machine Learning Track (B.S. R is used in many courses across campus. useR (It is absoluately important to read the ebook if you have no functions. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Units: 4.0 All rights reserved. Prerequisite:STA 108 C- or better or STA 106 C- or better. If there were lines which are updated by both me and you, you