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. type a short message about the changes and hit Commit, After committing the message, hit the Pull button (PS: there Copyright The Regents of the University of California, Davis campus. ECS has a lot of good options depending on what you want to do. ), Statistics: Computational Statistics Track (B.S. ), Statistics: Machine Learning Track (B.S. UC Davis history. You'll learn about continuous and discrete probability distributions, CLM, expected values, and more. The prereqs for 142A are STA 141A and 131A/130A/MAT 135 while the prereqs for 142B are 142A and 131B/130B. Open RStudio -> New Project -> Version Control -> Git -> paste The code is idiomatic and efficient. Statistical Thinking. Advanced R, Wickham. Community-run subreddit for the UC Davis Aggies! We then focus on high-level approaches to parallel and distributed computing for data analysis and machine learning and the fundamental general principles involved. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. explained in the body of the report, and not too large. The Art of R Programming, Matloff. Prerequisite:STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). 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. For the group project you will form groups of 2-3 and pursue a more open ended question using the usaspending data set. STA 141C Combinatorics MAT 145 . Python for Data Analysis, 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. You're welcome to opt in or out of Piazza's Network service, which lets employers find you. Link your github account at Units: 4.0 Assignments must be turned in by the due date. Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. Open the files and edit the conflicts, usually a conflict looks How did I get this data? This course explores aspects of scaling statistical computing for large data and simulations. easy to read. School: College of Letters and Science LS The Department offers a minor program in Statistics that consists of five upper division level courses focusing on the fundamentals of mathematical statistics and of the most widely used applied statistical methods. Graduate. We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. Elementary Statistics. Keep in mind these classes have their own prereqs which may include other ECS upper or lower divisions that I did not list. Summarizing. where appropriate. The A.B. A tag already exists with the provided branch name. Program in Statistics - Biostatistics Track, Linear model theory (10-12 lect) (a) LS-estimation; (b) Simple linear regression (normal model): (i) MLEs / LSEs: unbiasedness; joint distribution of MLE's; (ii) prediction; (iii) confidence intervals (iv) testing hypothesis about regression coefficients (c) General (normal) linear model (MLEs; hypothesis testing (d) ANOVA, Goodness-of-fit (3 lect) (a) chi^2 test (b) Kolmogorov-Smirnov test (c) Wilcoxon test. STA 141C: Big Data & High Performance Statistical Computing (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 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Subject: STA 221 Get ready to do a lot of proofs. assignment. Point values and weights may differ among assignments. To resolve the conflict, locate the files with conflicts (U flag Create an account to follow your favorite communities and start taking part in conversations. STA 131A is considered the most important course in the Statistics major. STA141C: Big Data & High Performance Statistical Computing Lecture 12: Parallel Computing Cho-Jui Hsieh UC Davis June 8, ), Statistics: General Statistics Track (B.S. ), Information for Prospective Transfer Students, Ph.D. ), Statistics: General Statistics Track (B.S. Potential Overlap:ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. Restrictions: The town of Davis helps our students thrive. Format: Lecture: 3 hours STA 141B Data Science Capstone Course STA 160 . Courses at UC Davis. 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. discovered over the course of the analysis. Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). useR (, J. Bryan, Data wrangling, exploration, and analysis with R Prerequisite: STA 108 C- or better or STA 106 C- or better. is a sub button Pull with rebase, only use it if you truly 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. 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. STA 135 Non-Parametric Statistics STA 104 . The report points out anomalies or notable aspects of the data discovered over the course of the analysis. ECS 201C: Parallel Architectures. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. the overall approach and examines how credible they are. 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. to parallel and distributed computing for data analysis and machine learning and the University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. You can view a list ofpre-approved courseshere. Prerequisite(s): STA 015BC- or better. College students fill up the tables at nearby restaurants and coffee shops with their laptops, homework and friends. Effective Term: 2020 Spring Quarter. Learn low level concepts that distributed applications build on, such as network sockets, MPI, etc. I'm trying to get into ECS 171 this fall but everyone else has the same idea. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Statistics drop-in takes place in the lower level of Shields Library. STA 010. We also explore different languages and frameworks indicate what the most important aspects are, so that you spend your ), Statistics: Statistical Data Science Track (B.S. They should follow a coherent sequence in one single discipline where statistical methods and models are applied. All rights reserved. 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. Community-run subreddit for the UC Davis Aggies! ECS 124 and 129 are helpful if you want to get into bioinformatics. All STA courses at the University of California, Davis (UC Davis) in Davis, California. Summary of course contents: Variable names are descriptive. The grading criteria are correctness, code quality, and communication. Nice! At least three of them should cover the quantitative aspects of the discipline. Discussion: 1 hour. the following information: (Adapted from Nick Ulle and Clark Fitzgerald ). In class we'll mostly use the R programming language, but these concepts apply more or less to any language. The B.S. My goal is to work in the field of data science, specifically machine learning. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We also take the opportunity to introduce statistical methods We first opened our doors in 1908 as the University Farm, the research and science-based instruction extension of UC Berkeley. It discusses assumptions in Game Details Date 3/1/2023 Start 6:00 Time 1:53 Attendance 78 Site Stanford, Calif. (Smith Family Stadium) Program in Statistics - Biostatistics Track. 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. Winter 2023 Drop-in Schedule. Press J to jump to the feed. STA 13. analysis.Final Exam: Course. Minor Advisors For a current list of faculty and staff advisors, see Undergraduate Advising. ECS 201B: High-Performance Uniprocessing. Including a handful of lines of code is usually fine. like: The attached code runs without modification. Variable names are descriptive. All rights reserved. Different steps of the data processing are logically organized into scripts and small, reusable functions. STA 141C Big Data & High Performance Statistical Computing (Final Project on yahoo.com Traffic Analytics) STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). 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. These are all worth learning, but out of scope for this class. This course provides an introduction to statistical computing and data manipulation. 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. Nehad Ismail, our excellent department systems administrator, helped me set it up. ), Information for Prospective Transfer Students, Ph.D. View full document STA141C: Big Data & High Performance Statistical Computing Lecture 1: Python programming (1) Cho-Jui Hsieh UC Davis April 4, 2017 You may find these books useful, but they aren't necessary for the course. Summary of Course Content: To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you 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. To make a request, send me a Canvas message with We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. 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. STA 141A Fundamentals of Statistical Data Science; prereq STA 108 with C- or better or 106 with C- or better. They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. This feature takes advantage of unique UC Davis strengths, including . Acknowledge where it came from in a comment or in the assignment. ECS 222A: Design & Analysis of Algorithms. Prerequisite: STA 131B C- or better. You can find out more about this requirement and view a list of approved courses and restrictions on the. First stats class I actually enjoyed attending every lecture. One of the most common reasons is not having the knitted This course explores aspects of scaling statistical computing for large data and simulations. specifically designed for large data, e.g. the URL: You could make any changes to the repo as you wish. ), Statistics: Statistical Data Science Track (B.S. STA 141C Computational Cognitive Neuroscience . ECS 203: Novel Computing Technologies. If nothing happens, download Xcode and try again. Information on UC Davis and Davis, CA. 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 Numbers are reported in human readable terms, i.e. The lowest assignment score will be dropped. All rights reserved. The report points out anomalies or notable aspects of the data 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. 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. Relevant Coursework and Competition: . but from a more computer-science and software engineering perspective than a focus on data in Statistics-Applied Statistics Track emphasizes statistical applications. 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. The code is idiomatic and efficient. 1% each week if the reputation point for the week is above 20. the top scorers for the quarter will earn extra bonuses. View Notes - lecture12.pdf from STA 141C at University of California, Davis. The official box score of Softball vs Stanford on 3/1/2023. ), Information for Prospective Transfer Students, Ph.D. . the bag of little bootstraps. As for CS, I've heard that after you take ECS 36C, you theoretically know everything you need for a programming job. If nothing happens, download Xcode and try again. 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). This is to There will be around 6 assignments and they are assigned via GitHub R is used in many courses across campus. R Graphics, Murrell. Students will learn how to work with big data by actually working with big data. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This course teaches the fundamentals of R and in more depth that is intentionally not done in these other courses. Make the question specific, self contained, and reproducible. ), Information for Prospective Transfer Students, Ph.D. advantages and disadvantages. the bag of little bootstraps. Several new electives -- including multiple EEC classes and STA 131B,STA 141B and STA 141C -- have been added t ), Statistics: Machine Learning Track (B.S. ), Statistics: Applied 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. STA 141A Fundamentals of Statistical Data Science. ), Statistics: General Statistics Track (B.S. Its such an interesting class. 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 Asking good technical questions is an important skill. Summary of course contents: UC Berkeley and Columbia's MSDS programs). 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. For the STA DS track, you pretty much need to take all of the important classes. 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 However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. MSDS aren't really recommended as they're newer programs and many are cash grabs (I.E. ), Statistics: Applied Statistics Track (B.S. You signed in with another tab or window. I'm actually quite excited to take them. Stat Learning I. STA 142B. 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. Davis, California 10 reviews . To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. Learn more. Parallel R, McCallum & Weston. Four upper division elective courses outside of statistics: Any violations of the UC Davis code of student conduct. Lingqing Shen: Fall 2018 undergraduate exchange student at UC-Davis, from Nanjing University. ECS 170 (AI) and 171 (machine learning) will be definitely useful. Make sure your posts don't give away solutions to the assignment. You signed in with another tab or window. Examples of such tools are Scikit-learn When I took it, STA 141A was coding and data visualization in R, and doing analysis based on our code and visuals. processing are logically organized into scripts and small, reusable If nothing happens, download GitHub Desktop and try again. If there were lines which are updated by both me and you, you Governance, International Baccalaureate Credit & Chart, Cal Aggie Student Alumni Association (SAA), University Policies on Nondiscrimination, Sexual Harassment/Sexual Violence, Student Records & Privacy, Campus Security, Crime Awareness, and Alcohol & Drug Abuse Prevention, Office of Educational Opportunity & Enrichment Services, Nondiscrimination & Sexual Harassment/Sexual Violence Prevention, Associated Students, University of California at Davis (ASUCD), CalTeach/Mathematics & Science Teaching Program (CalTeach/MAST), Center for Advocacy, Resources & Education (CARE), Center for Chicanx/Latinx Academic Student Success (CCLASS), Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual Resource Center (LGBTQIARC), Native American Academic Student Success Center (NAASSC), Services for International Students & Scholars (SISS), Strategic Asian and Pacific Islander Retention Initiative (SAandPIRI), Women's Resources & Research Center (WRRC), Academic Information, Policies, & Regulations, American History & Institutions Requirement, African American & African Studies, Bachelor of Arts, African American & African Studies, Minor, Agricultural & Environmental Chemistry (Graduate Group), Agricultural & Environmental Chemistry, Master of Science, Agricultural & Environmental Chemistry, Doctor of Philosophy, Agricultural & Resource Economics, Master of Science, Agricultural & Resource Economics, Master of Science/Master of Business Administration, Agricultural & Resource Economics, Doctor of Philosophy, Managerial Economics, Bachelor of Science, Agricultural & Environmental Education, Bachelor of Science, Animal Science & Management, Bachelor of Science, Applied Mathematics, Doctor of Philosophy, Social, Ethnic & Gender Relations, Minor, Atmospheric Science, Doctor of Philosophy, Biochemistry, Molecular, Cellular & Developmental Biology (Graduate Group), Biochemistry, Molecular, Cellular & Developmental Biology, Master of Science, Biochemistry, Molecular, Cellular & Developmental Biology, Doctor of Philosophy, Agricultural & Environmental Technology, Bachelor of Science, Biological Systems Engineering, Bachelor of Science, Biological Systems Engineering, Bachelor of Science/Master of Science Integrated, Biological Systems Engineering, Master of Engineering, Biological Systems Engineering, Master of Science, Biological Systems Engineering, Doctor of Engineering, Biological Systems Engineering, Doctor of Philosophy, Quantitative Biology & Bioinformatics, Minor, Biomedical Engineering, Bachelor of Science, Biomedical Engineering, Master of Science, Biomedical Engineering, Doctor of Philosophy, Biochemical Engineering, Bachelor of Science, Chemical Engineering, Bachelor of Science, Chemical Engineering, Master of Engineering, Chemical Engineering, Doctor of Philosophy, Chemistry & Chemical Biology, Master of Science, Chemistry & Chemical Biology, Doctor of Philosophy, Pharmaceutical 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Engineering, Bachelor of Science, Electrical & Computer Engineering, Bachelor of Science/Master of Science, Electrical & Computer Engineering, Master of Science, Electrical & Computer Engineering, Doctor of Philosophy, Electrical Engineering, Bachelor of Science, Environmental Policy & Management (Graduate Group), Environmental Policy & Management, Master of Science, Environmental Policy Analysis & Planning, Bachelor of Science, Environmental Policy Analysis & Planning, Minor, Environmental Science & Management, Bachelor of Science, Environmental Toxicology, Bachelor of Science, Evolution, Ecology & Biodiversity, Bachelor of Arts, Evolution, Ecology & Biodiversity, Bachelor of Science, Evolution, Ecology & Biodiversity, Minor, French & Francophone Studies, Master of Arts, French & Francophone Studies, Doctor of Philosophy, Gender, Sexuality, & Women's Studies, Bachelor of Arts, Gender, Sexuality, & Women's Studies, Minor, Latin American & Hemispheric Studies, Minor, Horticulture & Agronomy (Graduate Group), Horticulture & Agronomy, Master of Science, Horticulture & Agronomy, Doctor of Philosophy, Community & Regional Development, Bachelor of Science, Landscape Architecture, Bachelor of Science, Sustainable Environmental Design, Bachelor of Science, Hydrologic Sciences, Doctor of Philosophy, Biological Sciences, Bachelor of Arts, Individual, Biological Sciences, Bachelor of Science, Individual, Integrative Genetics & Genomics (Graduate Group), Integrative Genetics & Genomics, Master of Science, Integrative Genetics & Genomics, Doctor of Philosophy, Integrative Pathobiology (Graduate Group), Integrative Pathobiology, Master of Science, Integrative Pathobiology, Doctor of Philosophy, International Agricultural Development (Graduate Group), International Agricultural Development, Master of Science, Sustainable Agriculture & Food Systems, Bachelor of Science, Materials Science & Engineering, Bachelor of Science, Materials Science & Engineering, Master of Engineering, Materials Science & Engineering, Master of Science, Materials Science & Engineering, Doctor of Philosophy, Mathematical & Scientific Computation, Bachelor of Science, Mathematical Analytics & Operations Research, Bachelor of Science, Aerospace Science & Engineering, Bachelor of Science, Mechanical Engineering, Bachelor of Science, Mechanical & Aerospace Engineering, Master of Science, Mechanical & Aerospace Engineering, Doctor of Philosophy, Medieval & Early Modern Studies, Bachelor of Arts, Molecular & Medical Microbiology, Bachelor of Arts, Molecular & Medical Microbiology, Bachelor of Science, Middle East/South Asia Studies, Bachelor of Arts, Biochemistry & Molecular Biology, Bachelor of Science, Genetics & Genomics, Bachelor of Science, Molecular, Cellular, & Integrative Physiology (Graduate Group), Molecular, Cellular, & Integrative Physiology, Master of Science, Molecular, Cellular, & Integrative Physiology, Doctor of Philosophy, Native American Studies, Bachelor of Arts, Native American Studies, Doctor of Philosophy, Neurobiology, Physiology, & Behavior, Bachelor of Science, Nursing Science & Health-Care Leadership, Doctor of Nursing PracticeFamily Nurse Practitioner Degree Program, Family Nurse Practitioner Program, Master of Science, Nursing Science & Health-Care Leadership, Doctor of Philosophy, Physician Assistant Studies, Master of Health Services, Maternal & Child Nutrition, Master of Advanced Study, Nutritional Biology, Doctor of Philosophy, Performance Studies, Doctor of Philosophy, Pharmacology & Toxicology (Graduate Group), Pharmacology & Toxicology, Master of Science, Pharmacology & Toxicology, Doctor of Philosophy, Systems & Synthetic Biology, Bachelor of Science, Global Disease Biology, Bachelor of Science, Agricultural Systems & Environment, Minor, Ecological Management & Restoration, Bachelor of Science, Environmental Horticulture & Urban Forestry, Bachelor of Science, International Agricultural Development, Bachelor of Science, International Agricultural Development, Minor, International Relations, Bachelor of Arts, Political SciencePublic Service, Bachelor of Arts, Political Science, Master of Arts/Doctor of Jurisprudence, Preventive Veterinary Medicine (Graduate Group), Public Health Sciences, Doctor of Philosophy, Science & Technology Studies, Bachelor of Arts, Soils & Biogeochemistry (Graduate Group), Soils & Biogeochemistry, Master of Science, Soils & Biogeochemistry, Doctor of Philosophy, Transportation Technology & Policy (Graduate Group), Transportation Technology & Policy, Master of Science, Transportation Technology & Policy, Doctor of Philosophy, Viticulture & Enology, Bachelor of Science, Viticulture & Enology, Master of Science, Wildlife, Fish & Conservation Biology, Bachelor of Science, Wildlife, Fish & Conservation Biology, Minor, African American & African Studies (AAS), Agricultural & Environmental Chemistry (AGC), Agricultural & Environmental Technology (TAE), Anatomy, Physiology, & Cell Biology (APC), Applied Biological Systems Technology (ABT), Biochemistry, Molecular, Cellular, & Developmental Biology (BCB), Environmental Science & Management (ESM), Future Undergraduate Science Educators (FSE), Gender, Sexuality, & Women's Studies (GSW), International Agricultural Development (IAD), Management; Working Professional Bay Area (MGB), Masters Preventive Veterinary Medicine (MPM), Mechanical & Aeronautical Engineering (MAE), Molecular, Cellular, & Integrative Physiology (MCP), Neurobiology, Physiology, & Behavior (NPB), Pathology, Microbiology, & Immunology (PMI), Physical Medicine & Rehabilitation (PMR), Social Theory & Comparative History (STH), Sustainable Agriculture & Food Systems (SAF), Transportation Technology & Policy (TTP), Wildlife, Fish, & Conservation Biology (WFC), Applied Statistics for Biological Sciences, Applied Statistical Methods: Analysis of Variance, Applied Statistical Methods: Regression Analysis, Advanced Applied Statistics for the Biological Sciences, Applied Statistical Methods: Nonparametric Statistics, Data & Web Technologies for Data Analysis, Big Data & High Performance Statistical Computing.

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