You're welcome to opt in or out of Piazza's Network service, which lets employers find you. A tag already exists with the provided branch name. View full document STA141C: Big Data & High Performance Statistical Computing Lecture 1: Python programming (1) Cho-Jui Hsieh UC Davis April 4, 2017 From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. Online with Piazza. 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. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Copyright The Regents of the University of California, Davis campus. Copyright The Regents of the University of California, Davis campus. This is to Asking good technical questions is an important skill. Get ready to do a lot of proofs. We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. the bag of little bootstraps. Replacement for course STA 141. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. 2022 - 2022. in the git pane). Subscribe today to keep up with the latest ITS news and happenings. But the go-to stats classes for data science are STA 141A-B-C and STA 142A-B. However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. Stats classes: https://statistics.ucdavis.edu/courses/descriptions-undergrad. 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. useR (, J. Bryan, Data wrangling, exploration, and analysis with R Not open for credit to students who have taken STA 141 or STA 242. 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. The lowest assignment score will be dropped. The classes are like, two years old so the professors do things differently. STA 013Y. Use Git or checkout with SVN using the web URL. type a short message about the changes and hit Commit, After committing the message, hit the Pull button (PS: there Summary of course contents: 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. Tables include only columns of interest, are clearly explained in the body of the report, and not too large. Prerequisite:STA 108 C- or better or STA 106 C- or better. I haven't graduated yet so I don't know exactly what will be useful for a career/grad school. 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. STA 141C Big Data & High Performance Statistical Computing, STA 141C Big Data & High Performance Statistical ), Statistics: Applied Statistics Track (B.S. would see a merge conflict. Currently ACO PhD student at Tepper School of Business, CMU. 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 Stat Learning II. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Requirements from previous years can be found in theGeneral Catalog Archive. For the group project you will form groups of 2-3 and pursue a more open ended question using the usaspending data set. J. Bryan, the STAT 545 TAs, J. Hester, Happy Git and GitHub for the No late assignments 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. - Thurs. Lecture: 3 hours Keep in mind these classes have their own prereqs which may include other ECS upper or lower divisions that I did not list. for statistical/machine learning and the different concepts underlying these, and their sign in understand what it is). Reddit and its partners use cookies and similar technologies to provide you with a better experience. My goal is to work in the field of data science, specifically machine learning. Any violations of the UC Davis code of student conduct. Mon. Assignments must be turned in by the due date. This course overlaps significantly with the existing course 141 course which this course will replace. Restrictions: Check the homework submission page on The A.B. Prerequisite(s): STA 015BC- or better. (, RStudio 1.3.1093 (check your RStudio Version), Knowledge about git and GitHub: read Happy Git and GitHub for the This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch? the following information: (Adapted from Nick Ulle and Clark Fitzgerald ). STA 221 - Big Data & High Performance Statistical Computing, Statistics: Applied Statistics Track (A.B. Applications of (II) (6 lect): (i) consistency of estimators; (ii) variance stabilizing transformations; (iii) asymptotic normality (and efficiency) of MLE; Statistics: Applied Statistics Track (A.B. The environmental one is ARE 175/ESP 175. sign in Lai's awesome. 2022-2023 General Catalog STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. like. Including a handful of lines of code is usually fine. School: College of Letters and Science LS It discusses assumptions in the overall approach and examines how credible they are. Copyright The Regents of the University of California, Davis campus. Press J to jump to the feed. Program in Statistics - Biostatistics Track. UC Davis history. We first opened our doors in 1908 as the University Farm, the research and science-based instruction extension of UC Berkeley. STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). to use Codespaces. explained in the body of the report, and not too large. ), Statistics: General Statistics Track (B.S. 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. This feature takes advantage of unique UC Davis strengths, including . You get to learn alot of cool stuff like making your own R package. It mentions ideas for extending or improving the analysis or the computation. At least three of them should cover the quantitative aspects of the discipline. Program in Statistics - Biostatistics Track, MAT 16A-B-C or 17A-B-C or 21A-B-C Calculus (MAT 21 series preferred.). Check regularly the course github organization the bag of little bootstraps. More testing theory (8 lect): LR-test, UMP tests (monotone LR); t-test (one and two sample), F-test; duality of confidence intervals and testing, Tools from probability theory (2 lect) (including Cebychev's ineq., LLN, CLT, delta-method, continuous mapping theorems). Using short snippets of code (5 lines or so) from lecture, Piazza, or other sources. This is the markdown for the code used in the first . Hadoop: The Definitive Guide, White.Potential Course Overlap: STA 131A is considered the most important course in the Statistics major. discovered over the course of the analysis. The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. It's forms the core of statistical knowledge. For the elective classes, I think the best ones are: STA 104 and 145. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? ), Statistics: Computational Statistics Track (B.S. Goals: ECS 145 covers Python, but from a more computer-science and software engineering perspective than a focus on data analysis. Any deviation from this list must be approved by the major adviser. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Course 242 is a more advanced statistical computing course that covers more material. ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. Writing is clear, correct English. Make sure your posts don't give away solutions to the assignment. STA 141A Fundamentals of Statistical Data Science. I'm a stats major (DS track) also doing a CS minor. 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. R is used in many courses across campus. It moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to compiled code for speed and memory improvements. STA 100. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. Open RStudio -> New Project -> Version Control -> Git -> paste https://signin-apd27wnqlq-uw.a.run.app/sta141c/. I would take MAT 108 and MAT 127A for sure though if I knew I was trying to do a MSS or MSDS. You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. Program in Statistics - Biostatistics Track. 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. Parallel R, McCallum & Weston. Statistics 141 C - UC Davis. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. As the century evolved, our mission expanded beyond agriculture to match a larger understanding of how we should be serving the public. The electives are chosen with andmust be approved by the major adviser. Learn more. 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. Press J to jump to the feed. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. ), Statistics: Statistical Data Science Track (B.S. Summary of course contents:This course explores aspects of scaling statistical computing for large data and simulations. 10 AM - 1 PM. Prerequisite: STA 131B C- or better. specifically designed for large data, e.g. The Art of R Programming, by Norm Matloff. UC Davis Veteran Success Center . ), Statistics: Applied Statistics Track (B.S. The Art of R Programming, Matloff. Open the files and edit the conflicts, usually a conflict looks Nothing to show Format: 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. technologies and has a more technical focus on machine-level details. but from a more computer-science and software engineering perspective than a focus on data Please 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 Writing is Learn more. ), Statistics: General Statistics Track (B.S. Davis is the ultimate college town. Those classes have prerequisites, so taking STA 32 and STA 108 is probably the best if you want to take them. STA141C: Big Data & High Performance Statistical Computing Lecture 5: Numerical Linear Algebra Cho-Jui Hsieh UC Davis April 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. STA 141C Big Data & High Performance Statistical Computing (Final Project on yahoo.com Traffic Analytics) The course covers the same general topics as STA 141C, but at a more advanced level, and Statistics: Applied Statistics Track (A.B. If nothing happens, download Xcode and try again. Units: 4.0 They learn how and why to simulate random processes, and are introduced to statistical methods they do not see in other courses. Davis, California 10 reviews . This is an experiential course. the overall approach and examines how credible they are. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. 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. This course provides the foundations and practical skills for other statistical methods courses that make use of computing, and also subsequent statistical computing courses. Statistics: Applied Statistics Track (A.B. 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. If there were lines which are updated by both me and you, you ), Information for Prospective Transfer Students, Ph.D. ), Statistics: General Statistics Track (B.S. the bag of little bootstraps.Illustrative Reading: First offered Fall 2016. Advanced R, Wickham. Sampling Theory. Several new electives -- including multiple EEC classes and STA 131B,STA 141B and STA 141C -- have been added t fundamental general principles involved. This individualized program can lead to graduate study in pure or applied mathematics, elementary or secondary level teaching, or to other professional goals. Choose one; not counted toward total units: Additional preparatory courses will be needed based on the course prerequisites listed in the catalog; e.g., Calculus at the level of, and Mathematical Statistics: Brief Course, and Introduction to Mathematical Statistics, Toggle Academic Advising & Student Services, Toggle Student Resource & Information Centers, Toggle Academic Information, Policies, & Regulations, Toggle African American & African Studies, Toggle Agricultural & Environmental Chemistry (Graduate Group), Toggle Agricultural & Resource Economics, Toggle Applied Mathematics (Graduate Group), Toggle Atmospheric Science (Graduate Group), Toggle Biochemistry, Molecular, Cellular & Developmental Biology (Graduate Group), Toggle Biological & Agricultural Engineering, Toggle Biomedical Engineering (Graduate Group), Toggle Child Development (Graduate Group), Toggle Civil & Environmental Engineering, Toggle Clinical Research (Graduate Group), Toggle Electrical & Computer Engineering, Toggle Environmental Policy & Management (Graduate Group), Toggle Gender, Sexuality, & Women's Studies, Toggle Health Informatics (Graduate Group), Toggle Hemispheric Institute of the Americas, Toggle Horticulture & Agronomy (Graduate Group), Toggle Human Development (Graduate Group), Toggle Hydrologic Sciences (Graduate Group), Toggle Integrative Genetics & Genomics (Graduate Group), Toggle Integrative Pathobiology (Graduate Group), Toggle International Agricultural Development (Graduate Group), Toggle Mechanical & Aerospace Engineering, Toggle Microbiology & Molecular Genetics, Toggle Molecular, Cellular, & Integrative Physiology (Graduate Group), Toggle Neurobiology, Physiology, & Behavior, Toggle Nursing Science & Health-Care Leadership, Toggle Nutritional Biology (Graduate Group), Toggle Performance Studies (Graduate Group), Toggle Pharmacology & Toxicology (Graduate Group), Toggle Population Biology (Graduate Group), Toggle Preventive Veterinary Medicine (Graduate Group), Toggle Soils & Biogeochemistry (Graduate Group), Toggle Transportation Technology & Policy (Graduate Group), Toggle Viticulture & Enology (Graduate Group), Toggle Wildlife, Fish, & Conservation Biology, Toggle Additional Education Opportunities, Administrative Offices & U.C. This course teaches the fundamentals of R and in more depth that is intentionally not done in these other courses. moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to ), Statistics: Computational Statistics Track (B.S. Statistics drop-in takes place in the lower level of Shields Library. assignment. Discussion: 1 hour, Catalog Description: ), Statistics: Computational Statistics Track (B.S. All rights reserved. ), Statistics: General Statistics Track (B.S. If there is any cheating, then we will have an in class exam. STA 141A Fundamentals of Statistical Data Science; prereq STA 108 with C- or better or 106 with C- or better. Effective Term: 2020 Spring Quarter. where appropriate. Hes also teaching STA 141B for Spring Quarter, so maybe Ill enjoy him then as well . Game Details Date 3/1/2023 Start 6:00 Time 1:53 Attendance 78 Site Stanford, Calif. (Smith Family Stadium) ), Statistics: Machine Learning Track (B.S. All rights reserved. ), Information for Prospective Transfer Students, Ph.D. 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 ECS has a lot of good options depending on what you want to do. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. functions. You may find these books useful, but they aren't necessary for the course. He's also my favorite econ professor here at Davis, but I know a few people who really don't like him. Nehad Ismail, our excellent department systems administrator, helped me set it up. As for CS, I've heard that after you take ECS 36C, you theoretically know everything you need for a programming job. Format: UC Berkeley and Columbia's MSDS programs). R is used in many courses across campus. Nice! 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. A tag already exists with the provided branch name. It's green, laid back and friendly. includes additional topics on research-level tools. Statistical Thinking. the URL: You could make any changes to the repo as you wish. The grading criteria are correctness, code quality, and communication. 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. Different steps of the data This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Lecture: 3 hours Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you functions, as well as key elements of deep learning (such as convolutional neural networks, and . 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. indicate what the most important aspects are, so that you spend your I downloaded the raw Postgres database. Nonparametric methods; resampling techniques; missing data. Discussion: 1 hour. STA 144. 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. University of California, Davis Non-Degree UC & NUS Reciprocal Exchange Program Computer Science and Engineering. ideas for extending or improving the analysis or the computation. 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. Press question mark to learn the rest of the keyboard shortcuts. clear, correct English. long short-term memory units). Illustrative reading: We'll cover the foundational concepts that are useful for data scientists and data engineers. 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 Comprehensive overview of machine learning, predictive analytics, deep neural networks, algorithm design, or any particular sub field of statistics. There will be around 6 assignments and they are assigned via GitHub Information on UC Davis and Davis, CA. Warning though: what you'll learn is dependent on the professor. STA141C: Big Data & High Performance Statistical Computing Lecture 12: Parallel Computing Cho-Jui Hsieh UC Davis June 8, However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. Storing your code in a publicly available repository. STA 013. . deducted if it happens. Check that your question hasn't been asked. STA 015C Introduction to Statistical Data Science III(4 units) Course Description:Classical and Bayesian inference procedures in parametric statistical models. Variable names are descriptive. Adv Stat Computing. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. STA 142 series is being offered for the first time this coming year. master. I'll post other references along with the lecture notes. Goals:Students learn to reason about computational efficiency in high-level languages. ECS 221: Computational Methods in Systems & Synthetic Biology. The class will cover the following topics. ECS 124 and 129 are helpful if you want to get into bioinformatics. 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 . 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. You signed in with another tab or window. Powered by Jekyll& AcademicPages, a fork of Minimal Mistakes. Copyright The Regents of the University of California, Davis campus. The code is idiomatic and efficient. There was a problem preparing your codespace, please try again. ), Statistics: Machine Learning Track (B.S. The course will teach students to be able to map an overall statistical task into computer code and be able to conduct basic data analyses. Using other people's code without acknowledging it. Could not load tags. We then focus on high-level approaches Advanced R, Wickham. We also learned in the last week the most basic machine learning, k-nearest neighbors. Check the homework submission page on Canvas to see what the point values are for each assignment. processing are logically organized into scripts and small, reusable 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. Graduate. For the STA DS track, you pretty much need to take all of the important classes. are accepted. It discusses assumptions in All rights reserved. Stack Overflow offers some sound advice on how to ask questions. STA 135 Non-Parametric Statistics STA 104 . Summary of course contents: Link your github account at There was a problem preparing your codespace, please try again. (, G. Grolemund and H. Wickham, R for Data Science Regrade requests must be made within one week of the return of the Variable names are descriptive. Copyright The Regents of the University of California, Davis campus. They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. 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.
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