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. Program in Statistics - Biostatistics Track, Linear model theory (10-12 lect) (a) LS-estimation; (b) Simple linear regression (normal model): (i) MLEs / LSEs: unbiasedness; joint distribution of MLE's; (ii) prediction; (iii) confidence intervals (iv) testing hypothesis about regression coefficients (c) General (normal) linear model (MLEs; hypothesis testing (d) ANOVA, Goodness-of-fit (3 lect) (a) chi^2 test (b) Kolmogorov-Smirnov test (c) Wilcoxon test. The style is consistent and We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. Davis, California 10 reviews . All STA courses at the University of California, Davis (UC Davis) in Davis, California. Use Git or checkout with SVN using the web URL. Assignments must be turned in by the due date. 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 Could not load branches. ), Statistics: Statistical Data Science Track (B.S. Former courses ECS 10 or 30 or 40 may also be used. STA 141C Big Data & High Performance Statistical Computing. I encourage you to talk about assignments, but you need to do your own work, and keep your work private. Information on UC Davis and Davis, CA. A.B. 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. indicate what the most important aspects are, so that you spend your 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. specifically designed for large data, e.g. Point values and weights may differ among assignments. We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. Its such an interesting class. The grading criteria are correctness, code quality, and communication. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Nonparametric methods; resampling techniques; missing data. Additionally, some statistical methods not taught in other courses are introduced in this course. 1. 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). We also take the opportunity to introduce statistical methods ), Information for Prospective Transfer Students, Ph.D. For the STA DS track, you pretty much need to take all of the important classes. The code is idiomatic and efficient. STA 141C (Spring 2019, 2021) Big data and Statistical Computing - STA 221 (Spring 2020) Department seminar series (STA 2 9 0) organizer for Winter 2020 MSDS aren't really recommended as they're newer programs and many are cash grabs (I.E. ), Statistics: Statistical Data Science Track (B.S. This individualized program can lead to graduate study in pure or applied mathematics, elementary or secondary level teaching, or to other professional goals. Lai's awesome. ECS has a lot of good options depending on what you want to do. (, RStudio 1.3.1093 (check your RStudio Version), Knowledge about git and GitHub: read Happy Git and GitHub for the If there is any cheating, then we will have an in class exam. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Plots include titles, axis labels, and legends or special annotations where appropriate. ), Statistics: Statistical Data Science Track (B.S. These requirements were put into effect Fall 2019. Branches Tags. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. STA 010. View full document STA141C: Big Data & High Performance Statistical Computing Lecture 1: Python programming (1) Cho-Jui Hsieh UC Davis April 4, 2017 Tables include only columns of interest, are clearly explained in the body of the report, and not too large. Review UC Davis course notes for STA STA 104 to get your preparate for upcoming exams or projects. Academia.edu is a platform for academics to share research papers. Statistical Thinking. View Notes - lecture12.pdf from STA 141C at University of California, Davis. 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. Units: 4.0 I'd also recommend ECN 122 (Game Theory). Replacement for course STA 141. ECS 220: Theory of Computation. We also learned in the last week the most basic machine learning, k-nearest neighbors. 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 Any MAT course numbered between 100-189, excluding MAT 111* (3-4) varies; see university catalog All rights reserved. You signed in with another tab or window. I recently graduated from UC Davis, majoring in Statistical Data Science and minoring in Mathematics. Catalog Description: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. The largest tables are around 200 GB and have 100's of millions of rows. This course explores aspects of scaling statistical computing for large data and simulations. Subscribe today to keep up with the latest ITS news and happenings. It moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to compiled code for speed and memory improvements. Restrictions: We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. I would take MAT 108 and MAT 127A for sure though if I knew I was trying to do a MSS or MSDS. ), Statistics: General Statistics Track (B.S. From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. Currently ACO PhD student at Tepper School of Business, CMU. The B.S. in Statistics-Applied Statistics Track emphasizes statistical applications. Comprehensive overview of machine learning, predictive analytics, deep neural networks, algorithm design, or any particular sub field of statistics. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. ECS 201A: Advanced Computer Architecture. It's green, laid back and friendly. 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. For a current list of faculty and staff advisors, see Undergraduate Advising. It's about 1 Terabyte when built. Lecture: 3 hours Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Title:Big Data & High Performance Statistical Computing I'm a stats major (DS track) also doing a CS minor. These are all worth learning, but out of scope for this class. the following information: (Adapted from Nick Ulle and Clark Fitzgerald ). 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. I expect you to ask lots of questions as you learn this material. for statistical/machine learning and the different concepts underlying these, and their The PDF will include all information unique to this page. The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. Examples of such tools are Scikit-learn Create an account to follow your favorite communities and start taking part in conversations. Please Contribute to ebatzer/STA-141C development by creating an account on GitHub. This track emphasizes statistical applications. Keep in mind these classes have their own prereqs which may include other ECS upper or lower divisions that I did not list. How did I get this data? 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 Any MAT course numbered between 100-189, excluding MAT 111* (3-4) varies; see university catalog It discusses assumptions in I took it with David Lang and loved it. STA 141C Computer Graphics ECS 175 Computer Vision ECS 174 Computer and Information Security ECS 235A Deep Learning ECS 289G Distributed Database Systems ECS 265 Programming Languages and. You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. Illustrative reading: Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. sign in ), Statistics: Computational Statistics Track (B.S. The official box score of Softball vs Stanford on 3/1/2023. Start early! This is to indicate what the most important aspects are, so that you spend your time on those that matter most. Information on UC Davis and Davis, CA. Oh yeah, since STA 141B is full for Winter Quarter, Im going to take STA 141C instead since the prereqs are STA 141B or STA 141A and ECS 32A at the same time. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. STA 131C Introduction to Mathematical Statistics. Prerequisite:STA 108 C- or better or STA 106 C- or better. Could not load tags. Use Git or checkout with SVN using the web URL. Four upper division elective courses outside of statistics: This track allows students to take some of their elective major courses in another subject area where statistics is applied. Stat Learning II. The environmental one is ARE 175/ESP 175. ), Statistics: Applied Statistics Track (B.S. Asking good technical questions is an important skill. Numbers are reported in human readable terms, i.e. In addition to online Oasis appointments, AATC offers in-person drop-in tutoring beginning January 17. Programming takes a long time, and you may also have to wait a long time for your job submission to complete on the cluster. Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. A tag already exists with the provided branch name. the bag of little bootstraps. It mentions STA 100. 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. This course provides an introduction to statistical computing and data manipulation. We'll use the raw data behind usaspending.gov as the primary example dataset for this class. They learn to map mathematical descriptions of statistical procedures to code, decompose a problem into sub-tasks, and to create reusable functions. Switch branches/tags. 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) . functions, as well as key elements of deep learning (such as convolutional neural networks, and Copyright The Regents of the University of California, Davis campus. No late assignments 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. Discussion: 1 hour. 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. 2022-2023 General Catalog compiled code for speed and memory improvements. The code is idiomatic and efficient. The electives are chosen with andmust be approved by the major adviser. All rights reserved. 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. 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. like: The attached code runs without modification. You signed in with another tab or window. Learn low level concepts that distributed applications build on, such as network sockets, MPI, etc. ), Information for Prospective Transfer Students, Ph.D. If nothing happens, download GitHub Desktop and try again. The following describes what an excellent homework solution should look like: The attached code runs without modification. ), Statistics: General Statistics Track (B.S. Goals: Prerequisite(s): STA 015BC- or better. in the git pane). Advanced R, Wickham. They should follow a coherent sequence in one single discipline where statistical methods and models are applied.