sta 141c uc davis
You signed in with another tab or window. 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 In class we'll mostly use the R programming language, but these concepts apply more or less to any language. assignments. Program in Statistics - Biostatistics Track. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. They should follow a coherent sequence in one single discipline where statistical methods and models are applied. Press J to jump to the feed. like: The attached code runs without modification. the bag of little bootstraps.Illustrative Reading: This is an experiential course. UC Davis history. If nothing happens, download GitHub Desktop and try again. Goals:Students learn to reason about computational efficiency in high-level languages. Numbers are reported in human readable terms, i.e. He's also my favorite econ professor here at Davis, but I know a few people who really don't like him. Lai's awesome. 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. ideas for extending or improving the analysis or the computation. Hadoop: The Definitive Guide, White.Potential Course Overlap: the following information: (Adapted from Nick Ulle and Clark Fitzgerald ). Lecture: 3 hours Students will learn how to work with big data by actually working with big data. I would take MAT 108 and MAT 127A for sure though if I knew I was trying to do a MSS or MSDS. Review UC Davis course notes for STA STA 104 to get your preparate for upcoming exams or projects. Tables include only columns of interest, are clearly explained in the body of the report, and not too large. specifically designed for large data, e.g. Use of statistical software. Nehad Ismail, our excellent department systems administrator, helped me set it up. fundamental general principles involved. 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 ), Information for Prospective Transfer Students, Ph.D. Discussion: 1 hour. Catalog Description:Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. to use Codespaces. I'm taking it this quarter and I'm pretty stoked about it. It moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to compiled code for speed and memory improvements. View Notes - lecture9.pdf from STA 141C at University of California, Davis. 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. There will be around 6 assignments and they are assigned via GitHub The electives are chosen with andmust be approved by the major adviser. - Thurs. 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. Those classes have prerequisites, so taking STA 32 and STA 108 is probably the best if you want to take them. If there is any cheating, then we will have an in class exam. deducted if it happens. They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. View Notes - lecture5.pdf from STA 141C at University of California, Davis. STA141C: Big Data & High Performance Statistical Computing Lecture 5: Numerical Linear Algebra Cho-Jui Hsieh UC Davis April We'll cover the foundational concepts that are useful for data scientists and data engineers. As for CS, I've heard that after you take ECS 36C, you theoretically know everything you need for a programming job. Additionally, some statistical methods not taught in other courses are introduced in this course. 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. 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. ), Statistics: Machine Learning Track (B.S. All rights reserved. From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. STA 141C - Big Data & High Performance Statistical Computing Four of the electives have to be ECS : ECS courses numbered 120 to 189 inclusive and not used for core requirements (Refer below for student comments) ECS 193AB (Counts as one) - Two quarters of Senior Design Project (Winter/Spring) Asking good technical questions is an important skill. ECS 124 and 129 are helpful if you want to get into bioinformatics. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. School: College of Letters and Science LS Summary of course contents: Units: 4.0 College students fill up the tables at nearby restaurants and coffee shops with their laptops, homework and friends. Lecture: 3 hours 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. 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). 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). By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. Subscribe today to keep up with the latest ITS news and happenings. technologies and has a more technical focus on machine-level details. We then focus on high-level approaches However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. Discussion: 1 hour, Catalog Description: All rights reserved. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. Prerequisite:STA 108 C- or better or STA 106 C- or better. Prerequisite: STA 108 C- or better or STA 106 C- or better. Open the files and edit the conflicts, usually a conflict looks Plots include titles, axis labels, and legends or special annotations where appropriate. Prerequisite: STA 131B C- or better. Replacement for course STA 141. STA 141C Computational Cognitive Neuroscience . ), Statistics: General Statistics Track (B.S. Regrade requests must be made within one week of the return of the Assignments must be turned in by the due date. long short-term memory units). History: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Statistics drop-in takes place in the lower level of Shields Library. 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. 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. But sadly it's taught in R. Class was pretty easy. For a current list of faculty and staff advisors, see Undergraduate Advising. Warning though: what you'll learn is dependent on the professor. All rights reserved. Program in Statistics - Biostatistics Track, MAT 16A-B-C or 17A-B-C or 21A-B-C Calculus (MAT 21 series preferred.). Copyright The Regents of the University of California, Davis campus. There was a problem preparing your codespace, please try again. Switch branches/tags. This course overlaps significantly with the existing course 141 course which this course will replace. Course. understand what it is). Reddit and its partners use cookies and similar technologies to provide you with a better experience. STA 142 series is being offered for the first time this coming year. The style is consistent and easy to read. Its such an interesting class. Information on UC Davis and Davis, CA. STA141C: Big Data & High Performance Statistical Computing Lecture 12: Parallel Computing Cho-Jui Hsieh UC Davis June 8, ggplot2: Elegant Graphics for Data Analysis, Wickham. Any deviation from this list must be approved by the major adviser. The grading criteria are correctness, code quality, and communication. . ECS 220: Theory of Computation. However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. clear, correct English. The environmental one is ARE 175/ESP 175. ECS 145 covers Python, degree program has five tracks: Applied Statistics Track, Computational Statistics Track, General Track, Machine Learning Track, and the Statistical Data Science Track. This course teaches the fundamentals of R and in more depth that is intentionally not done in these other courses. https://github.com/ucdavis-sta141c-2021-winter for any newly posted Please One approved course of 4 units from STA 199, 194HA, or 194HB may be used. UC Davis Veteran Success Center . You signed in with another tab or window. I haven't graduated yet so I don't know exactly what will be useful for a career/grad school. I encourage you to talk about assignments, but you need to do your own work, and keep your work private. 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 commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Open RStudio -> New Project -> Version Control -> Git -> paste for statistical/machine learning and the different concepts underlying these, and their No description, website, or topics provided. STA 141C Big Data & High Performance Statistical Computing (Final Project on yahoo.com Traffic Analytics) 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. We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. . In addition to online Oasis appointments, AATC offers in-person drop-in tutoring beginning January 17. You are required to take 90 units in Natural Science and Mathematics. are accepted. 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. course materials for UC Davis STA141C: Big Data & High Performance Statistical Computing. Storing your code in a publicly available repository. ECS 145 covers Python, but from a more computer-science and software engineering perspective than a focus on data analysis. The PDF will include all information unique to this page. This track allows students to take some of their elective major courses in another subject area where statistics is applied. California'scollege town. First offered Fall 2016. Use Git or checkout with SVN using the web URL. Requirements from previous years can be found in theGeneral Catalog Archive. 1% each week if the reputation point for the week is above 20. the top scorers for the quarter will earn extra bonuses. View full document STA141C: Big Data & High Performance Statistical Computing Lecture 1: Python programming (1) Cho-Jui Hsieh UC Davis April 4, 2017 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. As the century evolved, our mission expanded beyond agriculture to match a larger understanding of how we should be serving the public. Statistics: Applied Statistics Track (A.B. indicate what the most important aspects are, so that you spend your The prereqs for 142A are STA 141A and 131A/130A/MAT 135 while the prereqs for 142B are 142A and 131B/130B. 2022-2023 General Catalog Nonparametric methods; resampling techniques; missing data. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. It's green, laid back and friendly. For the group project you will form groups of 2-3 and pursue a more open ended question using the usaspending data set. The class will cover the following topics. 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. Get ready to do a lot of proofs. discovered over the course of the analysis. A tag already exists with the provided branch name. The official box score of Softball vs Stanford on 3/1/2023. STA 221 - Big Data & High Performance Statistical Computing, Statistics: Applied Statistics Track (A.B. Information on UC Davis and Davis, CA. ), Statistics: Applied Statistics Track (B.S. I'll post other references along with the lecture notes. Check that your question hasn't been asked. Stats classes: https://statistics.ucdavis.edu/courses/descriptions-undergrad. Students become proficient in data manipulation and exploratory data analysis, and finding and conveying features of interest. 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 One of the most common reasons is not having the knitted easy to read. It is recommendedfor studentswho are interested in applications of statistical techniques to various disciplines includingthebiological, physical and social sciences. ), Statistics: Applied Statistics Track (B.S. Currently ACO PhD student at Tepper School of Business, CMU. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. 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. Comprehensive overview of machine learning, predictive analytics, deep neural networks, algorithm design, or any particular sub field of statistics. Participation will be based on your reputation point in Campuswire. ), Statistics: Computational Statistics Track (B.S. 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. Learn more. assignment. These are all worth learning, but out of scope for this class. This track emphasizes statistical applications. Acknowledge where it came from in a comment or in the assignment. 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 Minor Advisors For a current list of faculty and staff advisors, see Undergraduate Advising. They develop ability to transform complex data as text into data structures amenable to analysis. We also take the opportunity to introduce statistical methods 1. A tag already exists with the provided branch name. If there were lines which are updated by both me and you, you useR (, J. Bryan, Data wrangling, exploration, and analysis with R 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. Feedback will be given in forms of GitHub issues or pull requests. Elementary Statistics. All STA courses at the University of California, Davis (UC Davis) in Davis, California. check all the files with conflicts and commit them again with a 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. This course explores aspects of scaling statistical computing for large data and simulations. ECS 222A: Design & Analysis of Algorithms. 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. Parallel R, McCallum & Weston. View Notes - lecture12.pdf from STA 141C at University of California, Davis. We first opened our doors in 1908 as the University Farm, the research and science-based instruction extension of UC Berkeley. No late homework accepted. Prerequisite(s): STA 015BC- or better. ), Statistics: Machine Learning Track (B.S. This track allows students to take some of their elective major courses in another subject area where statistics is applied, Statistics: Applied Statistics Track (A.B. 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. to use Codespaces. Summary of course contents:This course explores aspects of scaling statistical computing for large data and simulations. 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. Discussion: 1 hour. Examples of such tools are Scikit-learn All rights reserved. This is the markdown for the code used in the first . Twenty-one members of the Laurasian group of Therevinae (Diptera: Therevidae) are compared using 65 adult morphological characters. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. How did I get this data? in Statistics-Applied Statistics Track emphasizes statistical applications. Former courses ECS 10 or 30 or 40 may also be used. functions, as well as key elements of deep learning (such as convolutional neural networks, and Relevant Coursework and Competition: . the bag of little bootstraps. 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. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? Courses at UC Davis. ), Information for Prospective Transfer Students, Ph.D. You may find these books useful, but they aren't necessary for the course. I'm a stats major (DS track) also doing a CS minor. the overall approach and examines how credible they are. You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. Could not load branches. ), Statistics: Statistical Data Science Track (B.S. sign in UC Berkeley and Columbia's MSDS programs). Stat Learning II. R is used in many courses across campus. The electives must all be upper division. 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. My goal is to work in the field of data science, specifically machine learning. Students learn to reason about computational efficiency in high-level languages. Start early! 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. Game Details Date 3/1/2023 Start 6:00 Time 1:53 Attendance 78 Site Stanford, Calif. (Smith Family Stadium) ), Statistics: Applied Statistics Track (B.S. STA 141A Fundamentals of Statistical Data Science; prereq STA 108 with C- or better or 106 with C- or better. Variable names are descriptive. This course teaches the fundamentals of R and in more depth that is intentionally not done in these other courses. There was a problem preparing your codespace, please try again. No late assignments ECS 201B: High-Performance Uniprocessing. STA 100. STA 013. . The classes are like, two years old so the professors do things differently. The code is idiomatic and efficient. ), Statistics: General Statistics Track (B.S. Advanced R, Wickham. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. The report points out anomalies or notable aspects of the data discovered over the course of the analysis. I'm actually quite excited to take them. The fastest machine in the world as of January, 2019 is the Oak Ridge Summit Supercomputer. Press question mark to learn the rest of the keyboard shortcuts. The code is idiomatic and efficient. The high-level themes and topics include doing exploratory data analysis, visualizing data graphically, reading and transforming data in complex formats, performing simulations, which are all essential skills for students working with data. Lingqing Shen: Fall 2018 undergraduate exchange student at UC-Davis, from Nanjing University. Department: Statistics STA Graduate. We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. Writing is clear, correct English. It mentions STA 13. ), Statistics: Machine Learning Track (B.S. ), Statistics: Computational Statistics Track (B.S. Preparing for STA 141C. Hes also teaching STA 141B for Spring Quarter, so maybe Ill enjoy him then as well . It discusses assumptions in the overall approach and examines how credible they are. STA141C: Big Data & High Performance Statistical Computing Lecture 9: Classification Cho-Jui Hsieh UC Davis May 18, 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 Format: The following describes what an excellent homework solution should look Using other people's code without acknowledging it. Check regularly the course github organization Academia.edu is a platform for academics to share research papers. We also learned in the last week the most basic machine learning, k-nearest neighbors. Subject: STA 221 ), Statistics: General Statistics Track (B.S. 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.