The Hoffman2 cluster is a powerful computational resource for the UCLA research community. This class is designed to clarify the process of porting your own applications on the cluster or using applications already available on the cluster. It also addresses how to port your workflow to the Hoffman2 and how to submit batch and run interactive applications.

Stata is a powerful and yet easy-to-use statistical package that runs on Windows, Macintosh and Unix platforms. This class is designed for people who are just getting started using Stata. The students in the class will have a hands-on experience using Stata for statistics, graphics and data management. The class notes are the scripts for the class available to the students in the class and to others on the Internet. The Stata class notes do not contain any of the…

This is a recurring weekly event from Tuesday, Sept. 4th - Tuesday, Oct. 2nd (5 courses total). Please attend all weeks if your time permits. No RSVP is necessary. For the past several weeks we have presented data frames in Python, R and Julia. Now that we are all familiar with Python Pandas data frame, we will start applying our knowledge to Machine learning. To guide us through the process we will use the course notes and exercises provided at the Google Machine Learning Crash…

RSVP FOR THE EVENT It is said that more than 80% of all data has a spatial component to it. As more and more research is leaning towards spatial analysis, this course offers an introduction to Geographic Information Systems, GIS, as a means to spatialize and analyze your data. As a hands-on course, you will learn how to download data, manipulate and join data to existing geographic boundaries, and create stunning cartographic representations. There are no pre-requisite requirements to take…

This class will address the process of creating Matlab standalone executables and running Matlab in batch, as well as running Abaqus python scripts. Example of how to submit array jobs using Abaqus, R and other applications. Can my simulations be submitted as array jobs? Bring your own problem. Also, Jupyter Notebooks: another way to run python and R on Hoffman2.

SAS is a powerful statistical package that runs on many platforms, including Windows and Unix. This class is designed for people who are just getting started using SAS. The students in the class will have a hands-on experience using SAS for statistics, graphics, and data management. The SAS class notes do not contain any of the computer output. The class notes are not meant to be a SAS textbook or a reference manual. The notes for the workshop can be…

RSVP FOR THE EVENT This entry-level workshop will introduce Python as a tool for digital researchers and scholars wanting to incorporate data on the web. The following Python libraries will be covered: requests, lxml, pandas, and numpy. Find other UCLA Library, Advanced Research Workshop Series

RSVP FOR THE EVENT 2-Day workshop, 9:00am - 5:00pm daily This course focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. Emphasis is placed on understanding how programs with macro code are processed. Learn how to perform text substitution in SAS code, automate and customize the production of SAS code, conditionally or iteratively construct SAS code and use macro variables and macro functions. This course can help prepare you for the following certification…

RSVP FOR THE EVENT 2-Day Workshop, 9am - 5pm Daily This course teaches you how to process SAS data using Structured Query Language (SQL). Learn how to query and subset data, summarize and present data, combine tables, including complex joins and merges, create and modify table views and indexes, replace multiple DATA and PROC steps with one SQL query. This course can help prepare you for the following certification exam(s): SAS Advanced Programming for SAS 9. Before attending this class, you should be able…

Python, originally developed as a general-purpose programming language, has gained its popularity in the scientific community in recent years owing to its elegant and easy-to-understand syntax and powerful libraries. Python fully supports both functional and object-oriented programming styles. This class will discuss a number useful features of Python in the context of scientific computing, and introduce a number of supporting packages, including numpy for fast array-based computations, h5py to access HDF5 files, and the use of Python in MPI-style parallel…

MPI (message passing interface) is the de facto standard for distributed-memory parallel scientific computing. While the entire MPI API is quite extensive, this class will cover a number of key ingredients commonly used in practice. This class is useful for those who plan to start writing MPI code, and for those who would like to know how MPI works in order to operate an existing MPI code. Working knowledge of C and Fortran programming languages will be very helpful to…

R is statistical package that runs on Windows, Macintosh and UNIX platforms. This class is designed for people who are just starting to use R. The students in the class will have a hands-on experience using R for doing statistics, graphics, and data management. The class notes are the scripts for the class. The R class notes do not contain any of the computer output. The class notes are not meant to be an R textbook or a reference manual.…

The Jupyter Notebook is a computing tool that allows users to edit and run Python, R, Julia (and many other programming languages) inside a web browser. Furthermore, it is a powerful tool that allows users to combine live code, text, and visualizations in an interactive, shareable, reproducible document. It has been growing in popularity in many scientific and research disciplines as a data exploration and analysis tool that encourages collaboration and reproducible science. This class will provide an interactive demonstration…

Git is a software tool that helps users manage changes to their software over time. Git will allow you to maintain a complete change history of every file, create branches for concurrent streams of changes, trace changes with annotations, and collaborate and share work with others. This interactive introduction will demonstrate how to use Git to track changes, to explore history, and to use web-based Git repositories to share work with others and collaborate.