Coursicle. 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. STA 141C. Personally I'm doing a BS in stats and will likely go for a MSCS over a MSS (MS in Stats) and a MSDS. ECS145 involves R programming. Copyright The Regents of the University of California, Davis campus. STA 144. fundamental general principles involved. No late homework accepted. I'll post other references along with the lecture notes. Please ), Statistics: Machine Learning Track (B.S. First stats class I actually enjoyed attending every lecture. However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. STA 141C Computational Cognitive Neuroscience . 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. useR (, J. Bryan, Data wrangling, exploration, and analysis with R Winter 2023 Drop-in Schedule. We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. If there were lines which are updated by both me and you, you Any violations of the UC Davis code of student conduct. analysis.Final Exam: Summarizing. Nonparametric methods; resampling techniques; missing data. Open the files and edit the conflicts, usually a conflict looks the overall approach and examines how credible they are. ), Statistics: Computational Statistics Track (B.S. This feature takes advantage of unique UC Davis strengths, including . STA 142 series is being offered for the first time this coming year. The Art of R Programming, by Norm Matloff. ), Information for Prospective Transfer Students, Ph.D. Statistics: Applied Statistics Track (A.B. You can view a list ofpre-approved courseshere. 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. Students learn to reason about computational efficiency in high-level languages. Hadoop: The Definitive Guide, White.Potential Course Overlap: 10 AM - 1 PM. For the STA DS track, you pretty much need to take all of the important classes. 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. Goals: A list of pre-approved electives can be foundhere. The course covers the same general topics as STA 141C, but at a more advanced level, and You can find out more about this requirement and view a list of approved courses and restrictions on the. The code is idiomatic and efficient. ), 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. The grading criteria are correctness, code quality, and communication. STA 013. . functions. Feedback will be given in forms of GitHub issues or pull requests. MSDS aren't really recommended as they're newer programs and many are cash grabs (I.E. 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 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. The code is idiomatic and efficient. STA 141B was in Python, where we learned web scraping, text mining, more visualization stuff, and a little bit of SQL at the end. Different steps of the data processing are logically organized into scripts and small, reusable functions. It discusses assumptions in the overall approach and examines how credible they are. I'm taking it this quarter and I'm pretty stoked about it. 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 Link your github account at You get to learn alot of cool stuff like making your own R package. Subscribe today to keep up with the latest ITS news and happenings. A tag already exists with the provided branch name. ECS 170 (AI) and 171 (machine learning) will be definitely useful. Several new electives -- including multiple EEC classes and STA 131B,STA 141B and STA 141C -- have been added t My goal is to work in the field of data science, specifically machine learning. Davis is the ultimate college town. ), Statistics: Computational Statistics Track (B.S. easy to read. 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. There was a problem preparing your codespace, please try again. 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. 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. Courses at UC Davis. We then focus on high-level approaches All rights reserved. Prerequisite:STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. The electives must all be upper division. For those that have already taken STA 141C, how was the class and what should I expect (I have Professor Lai for next quarter)? You can walk or bike from the main campus to the main street in a few blocks. STA141C: Big Data & High Performance Statistical Computing Lecture 5: Numerical Linear Algebra Cho-Jui Hsieh UC Davis April 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 This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In class we'll mostly use the R programming language, but these concepts apply more or less to any language. ), Statistics: Applied Statistics Track (B.S. 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. in the git pane). As for CS, I've heard that after you take ECS 36C, you theoretically know everything you need for a programming job. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. Former courses ECS 10 or 30 or 40 may also be used. But sadly it's taught in R. Class was pretty easy. 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. You signed in with another tab or window. Nothing to show {{ refName }} default View all branches. 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. for statistical/machine learning and the different concepts underlying these, and their Make the question specific, self contained, and reproducible. to use Codespaces. ), Statistics: Computational Statistics Track (B.S. History: degree program has five tracks: Applied Statistics Track, Computational Statistics Track, General Track, Machine Learning Track, and the Statistical Data Science Track. 2022 - 2022. 10 AM - 1 PM. STA 221 - Big Data & High Performance Statistical Computing, Statistics: Applied Statistics Track (A.B. master. Elementary Statistics. We also take the opportunity to introduce statistical methods In addition to online Oasis appointments, AATC offers in-person drop-in tutoring beginning January 17. experiences with git/GitHub). Preparing for STA 141C. Sampling Theory. This course explores aspects of scaling statistical computing for large data and simulations. ), Information for Prospective Transfer Students, Ph.D. ideas for extending or improving the analysis or the computation. Check the homework submission page on Canvas to see what the point values are for each assignment. Lecture: 3 hours This is an experiential course. Adapted from Nick Ulle's Fall 2018 STA141A class. ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. You signed in with another tab or window. Graduate. There will be around 6 assignments and they are assigned via GitHub understand what it is). Program in Statistics - Biostatistics Track. Are you sure you want to create this branch? ), Statistics: Computational Statistics Track (B.S. If nothing happens, download GitHub Desktop and try again. As the century evolved, our mission expanded beyond agriculture to match a larger understanding of how we should be serving the public. 2022-2023 General Catalog University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. The fastest machine in the world as of January, 2019 is the Oak Ridge Summit Supercomputer. Tables include only columns of interest, are clearly Parallel R, McCallum & Weston. Comprehensive overview of machine learning, predictive analytics, deep neural networks, algorithm design, or any particular sub field of statistics. The lowest assignment score will be dropped. Stat Learning II. I'm a stats major (DS track) also doing a CS minor. Information on UC Davis and Davis, CA. This course provides an introduction to statistical computing and data manipulation. ECS 201B: High-Performance Uniprocessing. This course teaches the fundamentals of R and in more depth that is intentionally not done in these other courses. Review UC Davis course notes for STA STA 104 to get your preparate for upcoming exams or projects. compiled code for speed and memory improvements. Twenty-one members of the Laurasian group of Therevinae (Diptera: Therevidae) are compared using 65 adult morphological characters. Stat Learning I. STA 142B. The environmental one is ARE 175/ESP 175. ), Statistics: Statistical Data Science Track (B.S. Restrictions: However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. It's about 1 Terabyte when built. ECS 158 covers parallel computing, but uses different Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Check the homework submission page on Tables include only columns of interest, are clearly explained in the body of the report, and not too large. . ), Statistics: General Statistics Track (B.S. Go in depth into the latest and greatest packages for manipulating data. Preparing for STA 141C. Examples of such tools are Scikit-learn time on those that matter most. School: College of Letters and Science LS Not open for credit to students who have taken STA 141 or STA 242. 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. How did I get this data? It moves from identifying inefficiencies in code, to idioms for more efficient code, to interfacing to compiled code for speed and memory improvements. It mentions I haven't graduated yet so I don't know exactly what will be useful for a career/grad school. We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. 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. It is recommendedfor studentswho are interested in applications of statistical techniques to various disciplines includingthebiological, physical and social sciences. Program in Statistics - Biostatistics Track. technologies and has a more technical focus on machine-level details. Powered by Jekyll& AcademicPages, a fork of Minimal Mistakes. Potential Overlap:This course overlaps significantly with the existing course 141 course which this course will replace. Information on UC Davis and Davis, CA. I took it with David Lang and loved it. Copyright The Regents of the University of California, Davis campus. Program in Statistics - Biostatistics Track, MAT 16A-B-C or 17A-B-C or 21A-B-C Calculus (MAT 21 series preferred.). Numbers are reported in human readable terms, i.e. ECS 145 covers Python, but from a more computer-science and software engineering perspective than a focus on data analysis. specifically designed for large data, e.g. ), Statistics: General Statistics Track (B.S. It's green, laid back and friendly. Statistics 141 C - UC Davis. ), Information for Prospective Transfer Students, Ph.D. Cladistic analysis using parsimony on the 17 ingroup and 4 outgroup taxa provides a well-supported hypothesis of relationships among taxa within the Cyclotelini, tribe nov. This means you likely won't be able to take these classes till your senior year as 141A always fills up incredibly fast. R Graphics, Murrell. Variable names are descriptive. 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). Reddit and its partners use cookies and similar technologies to provide you with a better experience. These are comprehensive records of how the US government spends taxpayer money. Branches Tags. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. Lingqing Shen: Fall 2018 undergraduate exchange student at UC-Davis, from Nanjing University. You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. View Notes - lecture5.pdf from STA 141C at University of California, Davis. To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you Warning though: what you'll learn is dependent on the professor. ECS 201A: Advanced Computer Architecture. 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. Community-run subreddit for the UC Davis Aggies! All rights reserved. Summary of course contents:This course explores aspects of scaling statistical computing for large data and simulations. One approved course of 4 units from STA 199, 194HA, or 194HB may be used. R is used in many courses across campus. STA 135 Non-Parametric Statistics STA 104 . 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. All rights reserved. Using short snippets of code (5 lines or so) from lecture, Piazza, or other sources. Could not load branches. STA141C: Big Data & High Performance Statistical Computing Lecture 12: Parallel Computing Cho-Jui Hsieh UC Davis June 8, The official box score of Softball vs Stanford on 3/1/2023. Press J to jump to the feed. 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). Copyright The Regents of the University of California, Davis campus. STA 142A. Asking good technical questions is an important skill. explained in the body of the report, and not too large. 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 https://signin-apd27wnqlq-uw.a.run.app/sta141c/. Course. STA 141C Big Data & High Performance Statistical Computing (Final Project on yahoo.com Traffic Analytics) Advanced R, Wickham. Including a handful of lines of code is usually fine. 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. The style is consistent and easy to read. Format: 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. Restrictions: STA 141C Big Data & High Performance Statistical Computing, STA 141C Big Data & High Performance Statistical ), Statistics: Machine Learning 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. The following describes what an excellent homework solution should look STA 015C Introduction to Statistical Data Science III(4 units) Course Description:Classical and Bayesian inference procedures in parametric statistical models. Format: The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. STA 141A Fundamentals of Statistical Data Science. 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. Learn low level concepts that distributed applications build on, such as network sockets, MPI, etc. 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 Contribute to ebatzer/STA-141C development by creating an account on GitHub. ECS 221: Computational Methods in Systems & Synthetic Biology. 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. Students become proficient in data manipulation and exploratory data analysis, and finding and conveying features of interest. UC Davis Veteran Success Center . Plots include titles, axis labels, and legends or special annotations A tag already exists with the provided branch name. We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. like. Get ready to do a lot of proofs. 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. Lai's awesome. Advanced R, Wickham. 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. Create an account to follow your favorite communities and start taking part in conversations. ), Statistics: Machine Learning Track (B.S. Mon. Point values and weights may differ among assignments. All STA courses at the University of California, Davis (UC Davis) in Davis, California. 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. Are you sure you want to create this branch? https://github.com/ucdavis-sta141c-2021-winter for any newly posted Prerequisite(s): STA 015BC- or better. The largest tables are around 200 GB and have 100's of millions of rows. ggplot2: Elegant Graphics for Data Analysis, Wickham. STA 013Y. in Statistics-Applied Statistics Track emphasizes statistical applications. Python for Data Analysis, Weston. For the group project you will form groups of 2-3 and pursue a more open ended question using the usaspending data set. Community-run subreddit for the UC Davis Aggies! By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. advantages and disadvantages. We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. Acknowledge where it came from in a comment or in the assignment. This course overlaps significantly with the existing course 141 course which this course will replace. Effective Term: 2020 Spring Quarter. ECS 201C: Parallel Architectures. 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. indicate what the most important aspects are, so that you spend your would see a merge conflict. Hes also teaching STA 141B for Spring Quarter, so maybe Ill enjoy him then as well . STA 131A is considered the most important course in the Statistics major. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Prerequisite: STA 108 C- or better or STA 106 C- or better. processing are logically organized into scripts and small, reusable ECS 222A: Design & Analysis of Algorithms. There was a problem preparing your codespace, please try again. The following describes what an excellent homework solution should look like: The attached code runs without modification. Homework must be turned in by the due date. Variable names are descriptive. The PDF will include all information unique to this page. Point values and weights may differ among assignments. This course explores aspects of scaling statistical computing for large data and simulations. 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. ), Statistics: Statistical Data Science Track (B.S. 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 ), Statistics: General Statistics Track (B.S. the bag of little bootstraps. I recently graduated from UC Davis, majoring in Statistical Data Science and minoring in Mathematics. The grading criteria are correctness, code quality, and communication. 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 100. This track allows students to take some of their elective major courses in another subject area where statistics is applied. UC Davis history. to use Codespaces. Learn more. This is to useR (It is absoluately important to read the ebook if you have no University of California, Davis Non-Degree UC & NUS Reciprocal Exchange Program Computer Science and Engineering. 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 . 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. Summary of Course Content: degree program has one track. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Goals:Students learn to reason about computational efficiency in high-level languages. Plots include titles, axis labels, and legends or special annotations where appropriate. They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. Game Details Date 3/1/2023 Start 6:00 Time 1:53 Attendance 78 Site Stanford, Calif. (Smith Family Stadium) Highperformance computing in highlevel data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; highlevel parallel computing; MapReduce; parallel algorithms and reasoning. Prerequisite: STA 131B C- or better. STA 010. We also learned in the last week the most basic machine learning, k-nearest neighbors. Information on UC Davis and Davis, CA. To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. This is the markdown for the code used in the first . STA 141C Combinatorics MAT 145 . No description, website, or topics provided.
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