html files uploaded, 30% of the grade of that assignment will be 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. How did I get this data? It's forms the core of statistical knowledge. Using other people's code without acknowledging it. 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). type a short message about the changes and hit Commit, After committing the message, hit the Pull button (PS: there No late assignments Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. If nothing happens, download GitHub Desktop and try again. I took it with David Lang and loved it. History: to use Codespaces. Variable names are descriptive. I expect you to ask lots of questions as you learn this material. University of California, Davis Non-Degree UC & NUS Reciprocal Exchange Program Computer Science and Engineering. the URL: You could make any changes to the repo as you wish. First stats class I actually enjoyed attending every lecture. Summary of course contents:This course explores aspects of scaling statistical computing for large data and simulations. Check the homework submission page on Lai's awesome. If there is any cheating, then we will have an in class exam. . Prerequisite: STA 108 C- or better or STA 106 C- or better. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. STA 141C Big Data & High Performance Statistical Computing (Final Project on yahoo.com Traffic Analytics) Regrade requests must be made within one week of the return of the 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. STA 141C Computational Cognitive Neuroscience . Feedback will be given in forms of GitHub issues or pull requests. Different steps of the data processing are logically organized into scripts and small, reusable functions. Information on UC Davis and Davis, CA. Yes Final Exam, University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. 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. Press question mark to learn the rest of the keyboard shortcuts. Acknowledge where it came from in a comment or in the assignment. Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). If nothing happens, download GitHub Desktop and try again. Course 242 is a more advanced statistical computing course that covers more material. One of the most common reasons is not having the knitted ), Statistics: Computational Statistics Track (B.S. Learn low level concepts that distributed applications build on, such as network sockets, MPI, etc. Use Git or checkout with SVN using the web URL. 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. STA 141C. Winter 2023 Drop-in Schedule. 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. I recently graduated from UC Davis, majoring in Statistical Data Science and minoring in Mathematics. The grading criteria are correctness, code quality, and communication. Are you sure you want to create this branch? They learn how and why to simulate random processes, and are introduced to statistical methods they do not see in other courses. All rights reserved. For the elective classes, I think the best ones are: STA 104 and 145. Potential Overlap:ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. 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) All rights reserved. Four upper division elective courses outside of statistics: The Art of R Programming, by Norm Matloff. deducted if it happens. Parallel R, McCallum & Weston. The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. ECS 220: Theory of Computation. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. (, RStudio 1.3.1093 (check your RStudio Version), Knowledge about git and GitHub: read Happy Git and GitHub for the Tables include only columns of interest, are clearly STA 131C Introduction to Mathematical Statistics. Numbers are reported in human readable terms, i.e. Statistics drop-in takes place in the lower level of Shields Library. 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. 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. indicate what the most important aspects are, so that you spend your Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Copyright The Regents of the University of California, Davis campus. Several new electives -- including multiple EEC classes and STA 131B,STA 141B and STA 141C -- have been added t Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. assignments. functions, as well as key elements of deep learning (such as convolutional neural networks, and The electives are chosen with andmust be approved by the major adviser. Former courses ECS 10 or 30 or 40 may also be used. Online with Piazza. STA 141C Combinatorics MAT 145 . Could not load branches. STA141C: Big Data & High Performance Statistical Computing Lecture 12: Parallel Computing Cho-Jui Hsieh UC Davis June 8, ), Statistics: General Statistics Track (B.S. assignment. 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 classes are like, two years old so the professors do things differently. Lecture: 3 hours ), Statistics: Applied Statistics Track (B.S. A tag already exists with the provided branch name. The style is consistent and easy to read. Nehad Ismail, our excellent department systems administrator, helped me set it up. ECS 158 covers parallel computing, but uses different Lai's awesome. 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. 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. the bag of little bootstraps.Illustrative Reading: You can walk or bike from the main campus to the main street in a few blocks. Including a handful of lines of code is usually fine. ECS 170 (AI) and 171 (machine learning) will be definitely useful. Program in Statistics - Biostatistics Track. ), Statistics: Applied Statistics Track (B.S. The style is consistent and This is the markdown for the code used in the first . Plots include titles, axis labels, and legends or special annotations where appropriate. Point values and weights may differ among assignments. Replacement for course STA 141. Format: We'll cover the foundational concepts that are useful for data scientists and data engineers. We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. technologies and has a more technical focus on machine-level details. Go in depth into the latest and greatest packages for manipulating data. Elementary Statistics. UC Berkeley and Columbia's MSDS programs). 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. Discussion: 1 hour. For the group project you will form groups of 2-3 and pursue a more open ended question using the usaspending data set. I'm a stats major (DS track) also doing a CS minor. Make sure your posts don't give away solutions to the assignment. If nothing happens, download Xcode and try again. Advanced R, Wickham. (, G. Grolemund and H. Wickham, R for Data Science Goals: Stats classes: https://statistics.ucdavis.edu/courses/descriptions-undergrad. UC Davis history. This individualized program can lead to graduate study in pure or applied mathematics, elementary or secondary level teaching, or to other professional goals. Nice! Information on UC Davis and Davis, CA. STA 141C Big Data & High Performance Statistical Computing. Copyright The Regents of the University of California, Davis campus. Additionally, some statistical methods not taught in other courses are introduced in this course. View Notes - lecture12.pdf from STA 141C at University of California, Davis. From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. Title:Big Data & High Performance Statistical Computing ), Information for Prospective Transfer Students, Ph.D. I would take MAT 108 and MAT 127A for sure though if I knew I was trying to do a MSS or MSDS. You signed in with another tab or window. ), Information for Prospective Transfer Students, Ph.D. Career Alternatives A list of pre-approved electives can be foundhere. 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 College students fill up the tables at nearby restaurants and coffee shops with their laptops, homework and friends. 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). 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. ), Statistics: Machine Learning Track (B.S. But sadly it's taught in R. Class was pretty easy. The PDF will include all information unique to this page. 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. The official box score of Softball vs Stanford on 3/1/2023. ), Statistics: General Statistics Track (B.S. Using short snippets of code (5 lines or so) from lecture, Piazza, or other sources. 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) . Asking good technical questions is an important skill. STA 131A is considered the most important course in the Statistics major. in Statistics-Applied Statistics Track emphasizes statistical applications. ), Statistics: Machine Learning Track (B.S. 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, Catalog Description: 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. 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. Course 242 is a more advanced statistical computing course that covers more material. The A.B. 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. Check the homework submission page on Canvas to see what the point values are for each assignment. Nonparametric methods; resampling techniques; missing data. STA 13. J. Bryan, the STAT 545 TAs, J. Hester, Happy Git and GitHub for the ECS 203: Novel Computing Technologies. 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. to use Codespaces. The code is idiomatic and efficient. clear, correct English. Create an account to follow your favorite communities and start taking part in conversations. However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Warning though: what you'll learn is dependent on the professor. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? 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. Discussion: 1 hour. Parallel R, McCallum & Weston. https://github.com/ucdavis-sta141c-2021-winter for any newly posted We'll use the raw data behind usaspending.gov as the primary example dataset for this class. There was a problem preparing your codespace, please try again. Nothing to show The B.S. I'd also recommend ECN 122 (Game Theory). 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. fundamental general principles involved. Feel free to use them on assignments, unless otherwise directed. We also take the opportunity to introduce statistical methods 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. Make the question specific, self contained, and reproducible. https://signin-apd27wnqlq-uw.a.run.app/sta141c/. We also learned in the last week the most basic machine learning, k-nearest neighbors. This feature takes advantage of unique UC Davis strengths, including . We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. Prerequisite:STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). In class we'll mostly use the R programming language, but these concepts apply more or less to any language. Department: Statistics STA The following describes what an excellent homework solution should look STA 141A Fundamentals of Statistical Data Science. To make a request, send me a Canvas message with There was a problem preparing your codespace, please try again. Adapted from Nick Ulle's Fall 2018 STA141A class. processing are logically organized into scripts and small, reusable If you receive a Bachelor of Science intheCollege of Letters and Science you have an areabreadth requirement. All rights reserved. Stat Learning II. Stack Overflow offers some sound advice on how to ask questions. Its such an interesting class. Prerequisite:STA 108 C- or better or STA 106 C- or better. They learn to map mathematical descriptions of statistical procedures to code, decompose a problem into sub-tasks, and to create reusable functions. In addition to online Oasis appointments, AATC offers in-person drop-in tutoring beginning January 17. This is to experiences with git/GitHub). Branches Tags. Copyright The Regents of the University of California, Davis campus. 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. The ones I think that are helpful are: ECS 122A (possibly B), 130, 145, 158, 163, 165A (possibly B), 170, 171, 173, and 174. Hes also teaching STA 141B for Spring Quarter, so maybe Ill enjoy him then as well . View full document STA141C: Big Data & High Performance Statistical Computing Lecture 1: Python programming (1) Cho-Jui Hsieh UC Davis April 4, 2017 MSDS aren't really recommended as they're newer programs and many are cash grabs (I.E. I'll post other references along with the lecture notes. Press J to jump to the feed. ECS 221: Computational Methods in Systems & Synthetic Biology. I downloaded the raw Postgres database. - Thurs. ECS 145 covers Python, but from a more computer-science and software engineering perspective than a focus on data analysis. Plots include titles, axis labels, and legends or special annotations sign in 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. It's about 1 Terabyte when built. STA 141A Fundamentals of Statistical Data Science; prereq STA 108 with C- or better or 106 with C- or better. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. Program in Statistics - Biostatistics Track. Restrictions: Summarizing. Students become proficient in data manipulation and exploratory data analysis, and finding and conveying features of interest. The town of Davis helps our students thrive. Catalog Description:Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. Stat Learning I. STA 142B. School: College of Letters and Science LS specifically designed for large data, e.g. A tag already exists with the provided branch name. Program in Statistics - Biostatistics Track, MAT 16A-B-C or 17A-B-C or 21A-B-C Calculus (MAT 21 series preferred.). ECS 145 covers Python, but from a more computer-science and software engineering perspective than a focus on data analysis.
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