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. Learn low level concepts that distributed applications build on, such as network sockets, MPI, etc. https://signin-apd27wnqlq-uw.a.run.app/sta141c/. compiled code for speed and memory improvements. long short-term memory units). ), Statistics: General Statistics Track (B.S. We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. STA141C: Big Data & High Performance Statistical Computing Lecture 5: Numerical Linear Algebra Cho-Jui Hsieh UC Davis April 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. The following describes what an excellent homework solution should look like: The attached code runs without modification. 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. Using short snippets of code (5 lines or so) from lecture, Piazza, or other sources. Please At least three of them should cover the quantitative aspects of the discipline. clear, correct English. History: for statistical/machine learning and the different concepts underlying these, and their the bag of little bootstraps. type a short message about the changes and hit Commit, After committing the message, hit the Pull button (PS: there Preparing for STA 141C. Feedback will be given in forms of GitHub issues or pull requests. Tables include only columns of interest, are clearly Prerequisite:STA 108 C- or better or STA 106 C- or better. 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. experiences with git/GitHub). Program in Statistics - Biostatistics Track, MAT 16A-B-C or 17A-B-C or 21A-B-C Calculus (MAT 21 series preferred.). We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. the URL: You could make any changes to the repo as you wish. STA 135 Non-Parametric Statistics STA 104 . 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. Different steps of the data processing are logically organized into scripts and small, reusable functions. Use of statistical software. sign in They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. Point values and weights may differ among assignments. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. You signed in with another tab or window. Preparing for STA 141C. Press J to jump to the feed. ECS 145 covers Python, but from a more computer-science and software engineering perspective than a focus on data analysis. Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. useR (It is absoluately important to read the ebook if you have no Statistics: Applied Statistics Track (A.B. assignment. Check regularly the course github organization Discussion: 1 hour, Catalog Description: sign in Department: Statistics STA I expect you to ask lots of questions as you learn this material. Subject: STA 221 It discusses assumptions in Goals:Students learn to reason about computational efficiency in high-level languages. Format: Lai's awesome. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Adapted from Nick Ulle's Fall 2018 STA141A class. STA 010. like: The attached code runs without modification. moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to Go in depth into the latest and greatest packages for manipulating data. It discusses assumptions in the overall approach and examines how credible they are. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Lecture: 3 hours The B.S. ECS has a lot of good options depending on what you want to do. 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 . View full document STA141C: Big Data & High Performance Statistical Computing Lecture 1: Python programming (1) Cho-Jui Hsieh UC Davis April 4, 2017 Contribute to ebatzer/STA-141C development by creating an account on GitHub. He's also my favorite econ professor here at Davis, but I know a few people who really don't like him. analysis.Final Exam: We'll use the raw data behind usaspending.gov as the primary example dataset for this class. As the century evolved, our mission expanded beyond agriculture to match a larger understanding of how we should be serving the public. If nothing happens, download GitHub Desktop and try again. Storing your code in a publicly available repository. 1% each week if the reputation point for the week is above 20. the top scorers for the quarter will earn extra bonuses. Powered by Jekyll& AcademicPages, a fork of Minimal Mistakes. Variable names are descriptive. You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. The class will cover the following topics. ), Statistics: Computational Statistics Track (B.S. But the go-to stats classes for data science are STA 141A-B-C and STA 142A-B. The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. This track allows students to take some of their elective major courses in another subject area where statistics is applied. 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. assignments. Personally I'm doing a BS in stats and will likely go for a MSCS over a MSS (MS in Stats) and a MSDS. These requirements were put into effect Fall 2019. Sampling Theory. STA 100. 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. Could not load branches. STA 131C Introduction to Mathematical Statistics. Four upper division elective courses outside of statistics: Potential Overlap:This course overlaps significantly with the existing course 141 course which this course will replace. 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. Parallel R, McCallum & Weston. Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). 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. 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. There was a problem preparing your codespace, please try again. would see a merge conflict. Prerequisite(s): STA 015BC- or better. Course 242 is a more advanced statistical computing course that covers more material. Stack Overflow offers some sound advice on how to ask questions. Link your github account at This course explores aspects of scaling statistical computing for large data and simulations. 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. 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. Participation will be based on your reputation point in Campuswire. in the git pane). Tables include only columns of interest, are clearly explained in the body of the report, and not too large. One of the most common reasons is not having the knitted technologies and has a more technical focus on machine-level details. 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. The classes are like, two years old so the professors do things differently. You are required to take 90 units in Natural Science and Mathematics. Winter 2023 Drop-in Schedule. Nehad Ismail, our excellent department systems administrator, helped me set it up. In class we'll mostly use the R programming language, but these concepts apply more or less to any language. 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 STA 131A is considered the most important course in the Statistics major. understand what it is). 10 AM - 1 PM. We'll cover the foundational concepts that are useful for data scientists and data engineers. Stat Learning II. Copyright The Regents of the University of California, Davis campus. ), Statistics: General Statistics Track (B.S. For MAT classes, I recommend taking MAT 108, 127A (possibly BC), and 128A. From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. Create an account to follow your favorite communities and start taking part in conversations. I recently graduated from UC Davis, majoring in Statistical Data Science and minoring in Mathematics. MSDS aren't really recommended as they're newer programs and many are cash grabs (I.E. My goal is to work in the field of data science, specifically machine learning. Assignments must be turned in by the due date. Former courses ECS 10 or 30 or 40 may also be used. All rights reserved. Statistics: Applied Statistics Track (A.B. explained in the body of the report, and not too large. You're welcome to opt in or out of Piazza's Network service, which lets employers find you. discovered over the course of the analysis. Switch branches/tags. ), Statistics: Applied Statistics Track (B.S. 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. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. Nothing to show You can find out more about this requirement and view a list of approved courses and restrictions on the. Relevant Coursework and Competition: . STA 13. 2022-2023 General Catalog For the group project you will form groups of 2-3 and pursue a more open ended question using the usaspending data set. Work fast with our official CLI. STA 142 series is being offered for the first time this coming year. in Statistics-Applied Statistics Track emphasizes statistical applications. It moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to compiled code for speed and memory improvements. The fastest machine in the world as of January, 2019 is the Oak Ridge Summit Supercomputer. The code is idiomatic and efficient. Currently ACO PhD student at Tepper School of Business, CMU. Summary of course contents: The largest tables are around 200 GB and have 100's of millions of rows. Career Alternatives ), Statistics: Computational Statistics Track (B.S. 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. This feature takes advantage of unique UC Davis strengths, including . Community-run subreddit for the UC Davis Aggies! Stats classes: https://statistics.ucdavis.edu/courses/descriptions-undergrad. Several new electives -- including multiple EEC classes and STA 131B,STA 141B and STA 141C -- have been added t Information on UC Davis and Davis, CA. ECS 201C: Parallel Architectures. Students will learn how to work with big data by actually working with big data. STA141C: Big Data & High Performance Statistical Computing Lecture 9: Classification Cho-Jui Hsieh UC Davis May 18, advantages and disadvantages. Press J to jump to the feed. 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. Examples of such tools are Scikit-learn ), Information for Prospective Transfer Students, Ph.D. ), Statistics: General Statistics Track (B.S. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. ), Statistics: Machine Learning Track (B.S. ), Statistics: Applied Statistics Track (B.S. Python for Data Analysis, Weston. View Notes - lecture12.pdf from STA 141C at University of California, Davis. I haven't graduated yet so I don't know exactly what will be useful for a career/grad school. Branches Tags. Lingqing Shen: Fall 2018 undergraduate exchange student at UC-Davis, from Nanjing University. This course provides an introduction to statistical computing and data manipulation. For the STA DS track, you pretty much need to take all of the important classes. STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). Hadoop: The Definitive Guide, White.Potential Course Overlap: It's green, laid back and friendly. However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. Asking good technical questions is an important skill. These are all worth learning, but out of scope for this class. School: College of Letters and Science LS Prerequisite: STA 108 C- or better or STA 106 C- or better. ), Statistics: General Statistics Track (B.S. This means you likely won't be able to take these classes till your senior year as 141A always fills up incredibly fast. Reddit and its partners use cookies and similar technologies to provide you with a better experience. Students learn to reason about computational efficiency in high-level languages. Make sure your posts don't give away solutions to the assignment. View Notes - lecture9.pdf from STA 141C at University of California, Davis. Illustrative reading: 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 You signed in with another tab or window. STA 141A Fundamentals of Statistical Data Science; prereq STA 108 with C- or better or 106 with C- or better. Statistics 141 C - UC Davis. functions. ECS 201B: High-Performance Uniprocessing. like. A tag already exists with the provided branch name. The prereqs for 142A are STA 141A and 131A/130A/MAT 135 while the prereqs for 142B are 142A and 131B/130B. to use Codespaces. STA 141A Fundamentals of Statistical Data Science. ECS 203: Novel Computing Technologies. 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. the bag of little bootstraps.Illustrative Reading: 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). Program in Statistics - Biostatistics Track. classroom. Any violations of the UC Davis code of student conduct. 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 The style is consistent and easy to read. In addition to online Oasis appointments, AATC offers in-person drop-in tutoring beginning January 17. Including a handful of lines of code is usually fine. Are you sure you want to create this branch? (, G. Grolemund and H. Wickham, R for Data Science For the elective classes, I think the best ones are: STA 104 and 145. Point values and weights may differ among assignments. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. If there is any cheating, then we will have an in class exam. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Effective Term: 2020 Spring Quarter. 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. 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. ), Statistics: Computational Statistics Track (B.S. ), Statistics: Machine Learning Track (B.S. Davis, California 10 reviews . If nothing happens, download Xcode and try again. ), Statistics: Statistical Data Science Track (B.S. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? Format: to parallel and distributed computing for data analysis and machine learning and the To make a request, send me a Canvas message with Elementary Statistics. Writing is You can view a list ofpre-approved courseshere. STA 141C Computational Cognitive Neuroscience . 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. No description, website, or topics provided. Numbers are reported in human readable terms, i.e. master. Advanced R, Wickham. This is the markdown for the code used in the first . Regrade requests must be made within one week of the return of the 1. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Parallel R, McCallum & Weston. 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 overall approach and examines how credible they are. Copyright The Regents of the University of California, Davis campus. ), Statistics: Statistical Data Science Track (B.S. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Subscribe today to keep up with the latest ITS news and happenings. First offered Fall 2016. 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. Students become proficient in data manipulation and exploratory data analysis, and finding and conveying features of interest. The following describes what an excellent homework solution should look This course provides the foundations and practical skills for other statistical methods courses that make use of computing, and also subsequent statistical computing courses. I'd also recommend ECN 122 (Game Theory). Writing is clear, correct English. ECS 158 covers parallel computing, but uses different Lecture: 3 hours Courses at UC Davis. ), Statistics: Applied Statistics Track (B.S. STA 141C Big Data & High Performance Statistical Computing (Final Project on yahoo.com Traffic Analytics) STA 141C. 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) It's forms the core of statistical knowledge. Statistical Thinking. ), Statistics: Machine Learning Track (B.S. How did I get this data? J. Bryan, the STAT 545 TAs, J. Hester, Happy Git and GitHub for the
