This is your opportunity to pursue a question that you are personally interested in as you create a public 'portfolio project' that shows off your big data processing skills to potential employers or admissions committees. I haven't graduated yet so I don't know exactly what will be useful for a career/grad school. The grading criteria are correctness, code quality, and communication. The code is idiomatic and efficient. J. Bryan, the STAT 545 TAs, J. Hester, Happy Git and GitHub for the The report points out anomalies or notable aspects of the data discovered over the course of the analysis. Furthermore, the combination of topics covered in this course (computational fundamentals, exploratory data analysis and visualization, and simulation) is unique to this course. Keep in mind these classes have their own prereqs which may include other ECS upper or lower divisions that I did not list. 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. ), Statistics: Statistical Data Science Track (B.S. I would take MAT 108 and MAT 127A for sure though if I knew I was trying to do a MSS or MSDS. Warning though: what you'll learn is dependent on the professor. Writing is clear, correct English. Open RStudio -> New Project -> Version Control -> Git -> paste All rights reserved. Preparing for STA 141C. To fetch updates go to the git pane in RStudio click the "Commit" button and check the files changed by you Statistics drop-in takes place in the lower level of Shields Library. Acknowledge where it came from in a comment or in the assignment. Prerequisite:STA 108 C- or better or STA 106 C- or better. ECS 145 covers Python, but from a more computer-science and software engineering perspective than a focus on data analysis. indicate what the most important aspects are, so that you spend your Winter 2023 Drop-in Schedule. All rights reserved. STA 141B: Data & Web Technologies for Data Analysis (previously has used Python) STA 141C: Big Data & High Performance Statistical Computing STA 144: Sample Theory of Surveys STA 145: Bayesian Statistical Inference STA 160: Practice in Statistical Data Science STA 206: Statistical Methods for Research I STA 207: Statistical Methods for Research II compiled code for speed and memory improvements. It's green, laid back and friendly. 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) ECS 145 covers Python, 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. master. From their website: USA Spending tracks federal spending to ensure taxpayers can see how their money is being used in communities across America. ), Information for Prospective Transfer Students, Ph.D. Restrictions: 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. We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages. 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. All rights reserved. R Graphics, Murrell. Prerequisite: STA 108 C- or better or STA 106 C- or better. This course teaches the fundamentals of R and in more depth that is intentionally not done in these other courses. ECS145 involves R programming. ), Statistics: Statistical Data Science Track (B.S. Additionally, some statistical methods not taught in other courses are introduced in this course. 1% each week if the reputation point for the week is above 20. the top scorers for the quarter will earn extra bonuses. These requirements were put into effect Fall 2019. The course covers the same general topics as STA 141C, but at a more advanced level, and includes additional topics on research-level tools. Not open for credit to students who have taken STA 141 or STA 242. Lai's awesome. ECS 158 covers parallel computing, but uses different technologies and has a more technical, machine-level focus. ideas for extending or improving the analysis or the computation. When I took it, STA 141A was coding and data visualization in R, and doing analysis based on our code and visuals. easy to read. This is an experiential course. The style is consistent and easy to read. 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. Stack Overflow offers some sound advice on how to ask questions. but from a more computer-science and software engineering perspective than a focus on data For the STA DS track, you pretty much need to take all of the important classes. Personally I'm doing a BS in stats and will likely go for a MSCS over a MSS (MS in Stats) and a MSDS. Lecture: 3 hours MAT 108 - Introduction to Abstract Mathematics new message. STA 131A is considered the most important course in the Statistics major. Please Information on UC Davis and Davis, CA. MSDS aren't really recommended as they're newer programs and many are cash grabs (I.E. As the century evolved, our mission expanded beyond agriculture to match a larger understanding of how we should be serving the public. STA 141C Combinatorics MAT 145 . Subscribe today to keep up with the latest ITS news and happenings. understand what it is). They develop ability to transform complex data as text into data structures amenable to analysis. Please 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. Create an account to follow your favorite communities and start taking part in conversations. Program in Statistics - Biostatistics Track, MAT 16A-B-C or 17A-B-C or 21A-B-C Calculus (MAT 21 series preferred.). STA 144. Goals:Students learn to reason about computational efficiency in high-level languages. would see a merge conflict. degree program has five tracks: Applied Statistics Track, Computational Statistics Track, General Track, Machine Learning Track, and the Statistical Data Science Track. 1. One of the most common reasons is not having the knitted I encourage you to talk about assignments, but you need to do your own work, and keep your work private. discovered over the course of the analysis. in Statistics-Applied Statistics Track emphasizes statistical applications. R is used in many courses across campus. Nonparametric methods; resampling techniques; missing data. 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 Graduate. There was a problem preparing your codespace, please try again. assignment. 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). One approved course of 4 units from STA 199, 194HA, or 194HB may be used. Career Alternatives ECS 220: Theory of Computation. hushuli/STA-141C. Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). sign in Examples of such tools are Scikit-learn in the git pane). STA 142 series is being offered for the first time this coming year. Advanced R, Wickham. ), Statistics: Statistical Data Science Track (B.S. This is the markdown for the code used in the first . They will be able to use different approaches, technologies and languages to deal with large volumes of data and computationally intensive methods. Press question mark to learn the rest of the keyboard shortcuts. However, the focus of that course is very different, focusing on more fundamental computer science tasks and also comparing high-level scripting languages. View Notes - lecture9.pdf from STA 141C at University of California, Davis. long short-term memory units). ), Statistics: General Statistics Track (B.S. Information on UC Davis and Davis, CA. These are all worth learning, but out of scope for this class. Adapted from Nick Ulle's Fall 2018 STA141A class. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. 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. 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. Coursicle. STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. School: College of Letters and Science LS The A.B. ECS 158 covers parallel computing, but uses different Advanced R, Wickham. 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. 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 Feedback will be given in forms of GitHub issues or pull requests. Prerequisite:STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). There will be around 6 assignments and they are assigned via GitHub 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 We also take the opportunity to introduce statistical methods specifically designed for large data, e.g. All rights reserved. Lecture content is in the lecture directory. We also explore different languages and frameworks for statistical/machine learning and the different concepts underlying these, and their advantages and disadvantages.
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