IS&T RCS Spring 2023 Trainings

January 17 – February 23, 2023

Registration is open for the RCS Spring 2023 Trainings. We are offering a mix of sessions taught by software vendors, RCS staff members, and one student demonstration.

  • For hands-on sessions where you wish to use your own computer, please have the appropriate software installed on your computer before the session starts.
  • Tutorials are tagged based on experience required (Beginner, Intermediate, or Advanced), location (details below), and if they are new.
  • Most of our spring sessions are offered only live in-person but some are offered over Zoom; these have special considerations:
    • Please register at least three days in advance in order to be emailed the Zoom link.
    • Sessions will be recorded; keep your camera off if you do not want your image recorded. The recorded sessions may be made available to the BU community.
  • Videos and slides for past tutorials by RCS staff and vendors are available. Access to that page is restricted to the BU community and you must agree not to share the materials.

The IS&T Research Computing Services (RCS) group offers a tutorial series on programming, data analysis, high performance computing, and domain specific topics three times each year. These tutorials are free and open to all members of the Boston University community.

The RCS tutorials cover concepts, techniques, and tools which researchers can use in their own computing environments. Many are designed to help you make effective use of the Boston University Shared Computing Cluster (SCC). The RCS staff can also deliver extra, or customized, tutorial sessions to your course, group, or lab. Please contact us at help@scc.bu.edu if you are interested.

In addition to the sessions below, we are also offering a special JMP Workshop: Interactive Visualizations to Explore Multi-Dimensional Data on Tuesday, February 7 from 1:30pm – 3:00pm at 2 Cummington Mall, Room 107.
Register

Trainings Schedule

You may register for as many tutorials as you like. Registration is required and is accessed with your BU Kerberos password.

If you don’t have a Kerberos password, or if you find that a tutorial is full, or have any other questions, please send email to rcs-tutorial@bu.edu.

Tutorial Locations

BSC Biological Science Center, 2 Cummington Mall, Room 107
CAS CAS Building, 685 Commonwealth Avenue, Room 327
BUMC BU Medical Campus (BUMC) The BUMC sessions will be held in the “L” building at 72 E Concord St using room number L1110. The “L” building is the BUMC main instructional building and the 11th floor is accessible by elevator. The tutorial room is at the end of the hall on the left.
Zoom Online over Zoom Registered attendees will be sent via email the Zoom link for each tutorial 2-3 days before the tutorial starts and at this point registration for the tutorial will close.

Tutorial Descriptions and Times

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Vendor Presentations

Intermediate Introduction to Bayesian Statistics Using Stata (Lecture) New

Instructor: Chuck Huber, Director of Statistical Outreach, Stata

BSC Tuesday, January 24, 11:00am – 12:00pm

Bayesian analysis has become a popular tool for many statistical applications. Yet many data analysts have little training in the theory of Bayesian analysis and software used to fit Bayesian models. This talk will provide an intuitive introduction to the concepts of Bayesian analysis and demonstrate how to fit Bayesian models using Stata. No prior knowledge of Bayesian analysis is necessary and specific topics will include the relationship between likelihood functions, prior, and posterior distributions, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, and how to use Stata’s Bayes prefix to fit Bayesian models.

Beginner Introduction to Graphics with Stata (Lecture)

Instructor: Chuck Huber, Director of Statistical Outreach, Stata

BSC Tuesday, Janaury 24, 1:00pm – 3:00pm

This talk introduces the basics of using Stata graphics to explore your data, check the assumptions of your models, and present your results. I will demonstrate how to use margins and marginsplot to visualize the results of complex models. You will also learn how to customize the appearance of your graphs using graph schemes, formating options, and how to layer and combine graphs.

Intermediate Deep Learning with Images using MATLAB (Hands-on) New

Instructor: Ram Krishnamurthy, Senior Customer Success Engineer, MathWorks

BSC Wednesday, February 8, 1:00pm – 3:00pm

Deep learning is quickly becoming embedded in everyday applications. It’s becoming essential for students and educators to adopt this technology to solve complex real-world problems. MATLAB and Simulink provide a flexible and powerful platform to develop and automate data analysis, deep learning, AI, and simulation workflows in a wide range of domains and industries

In this hands-on workshop, you will write code and use MATLAB Online to:

  1. Train deep neural networks on GPUs in the cloud
  2. Create deep learning models from scratch for image data
  3. Explore pretrained models and use transfer learning
  4. Learn how you can deploy your code to embedded targets
  5. Discuss how you can interface with Python frameworks

Intermediate Machine Learning with MATLAB (Hands-on) New

Instructor: Ram Krishnamurthy, Senior Customer Success Engineer, MathWorks

BSC Thursday, February 16, 1:00pm – 3:00pm

Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

In this hands-on workshop, you will use MATLAB to apply Machine Learning techniques to Signal data:

  • Learn the fundamentals of machine learning (supervised learning, feature extraction, and hyperparameter tuning)
  • Build and evaluate machine learning models for classification and regression of signals
  • Perform automatic hyperparameter tuning and feature selection to optimize model performance
  • Learn how to deploy Machine Learning models

Beginner Using ArcGIS Pro for Georeferencing, Digitizing, and Working with Time Series Data (Hands-on)

Instructor: Brian Baldwin, Senior Solution Engineer – Education, ESRI

CAS Thursday, February 9, 1:00pm – 3:00pm

Wish you could easily visualize, share, or add notations to historical maps? ArcGIS Pro provides easy to use tools that users can employ to make historical maps come to life. After referencing your map in a real-world location, you can then add ‘digitized’ features, and share easily with users around the world. This session will also explore the various ways that users can create time-series data and how it can be shared.

Beginner Easily Build Dynamic Dashboard Applications in ArcGIS Online (Hands-on)

Instructor: Brian Baldwin, Senior Solution Engineer – Education, ESRI

BSC Thursday, February 23, 1:00pm – 3:00pm

GIS is no longer about creating static maps (well… unless you want to). With ArcGIS Online users can connect to thousands of authoritative real-time datasets, analyze this data, modify symbology, and then configure custom applications. This session will demonstrate some of the recent updates to ArcGIS Online by showing a configurable dashboard getting built on the fly.

Intermediate NVIDIA: 5 Ways to Get Started with GPUs (Lecture) New

Instructor: Bradley Palmer, Senior Solutions Architect, NVIDIA

Zoom Monday, February 13, 1:00pm – 2:00pm

An introduction to GPU acceleration that outlines the 5 ways to accelerate computationally intensive code using GPUs. This session is a great starting point for those who would like to begin leveraging the benefits of accelerated computing. We offer a variety of easy methods to get started and also touch on the more advanced methods.

Intermediate NVIDIA: Using NVIDIA GPUs with Python (Hands-on) New

Instructors: Kristopher Keipert & Zoe Ryan, Solutions Architects, NVIDIA

Zoom Wednesday, February 15, 1:00pm – 4:00pm

In this workshop, you’ll get hands-on experience accelerating Python codes with NVIDIA GPUs. We will utilize code samples in three main categories to introduce you to Python GPU accelerated computing. First, we will explore drop-in replacements for SciPy and NumPy code through the CuPy library. Next we’ll cover NVIDIA RAPIDS, which provides GPU acceleration for end-to-end data science workloads. Finally we’ll cover Numba, which gives you the flexibility to write custom accelerated code without leaving the Python language. We’ll finish with an end-to-end example that incorporates all the tools introduced to tackle a geospatial problem. By the end of the workshop, you’ll have the skills to start accelerating your own Python codes with NVIDIA GPUs!

Intermediate NVIDIA: Introduction to Accelerated Genomics Analysis (Lecture) New

Instructor: Huiwen Ju, Solutions Architect, NVIDIA

Zoom Friday, February 17, 1:00pm – 2:00pm

Genomic sequencing is faster and cheaper than ever. The new bottleneck in the genomics pipeline is in the analysis. It can take upwards of 30 hours to run variant calling on a single sample, it could take months or even years to process thousands of samples. This is where CLARA Parabricks comes in. Using GPU acceleration, we have cut down the variant calling time to below 30 minutes for a 30x human genome. This allows for new genomics projects to be done at a scale that was not previously possible. In this session, we will discuss the capabilities of Parabricks, the performance compared to traditional genomics software packages (such as GATK), and show a demo of what it looks like in action.

Register

Research Computing Basics Tutorials

Beginner Introduction to Linux (Hands‐on)

Instructor: Augustine Abaris (augustin@bu.edu)

BSC Thursday, January 19, 10:00am – 12:00pm
Zoom Friday, January 20, 9:30am – 11:30am

This tutorial will give attendees a hands-on introduction to Linux. Topics covered will include a short history of Linux, logging in with ssh, the Bash shell and shell scripts, I/O redirection (pipes), file system navigation, and job control. Time permitting, attendees will edit, compile, and run a simple C program.

If you have not connected to the SCC from your laptop before, please read and follow these instructions prior to attending the tutorial.

Beginner Introduction to BU’s Shared Computing Cluster (Hands‐on)

Instructor: Aaron Fuegi (aarondf@bu.edu)

BSC Thursday, January 19, 1:00pm – 3:00pm
Zoom Friday, January 20, 12:00pm – 2:00pm

This tutorial will introduce Boston University’s Shared Computing Cluster (SCC) in Holyoke, MA. This Linux cluster has more than 23000 processors and over 9 petabytes of storage available for Research Computing by students and faculty on the Charles River and BUMC campuses. A very large number of software packages for programming, mathematics, data analysis, plotting, statistics, visualization, and domain-specific disciplines are available as well on the SCC. You will get a general overview of the SCC and the facility that houses it and then a hands-on introduction covering connecting to and using the SCC for new users. This tutorial will cover a few basic Linux commands but we strongly encourage people to also take our more extensive “Introduction to Linux” tutorial.

There will also be ample time for questions of all types about the SCC.

For those in the BU community interested in using a particular package on the SCC, after taking this tutorial we also recommend viewing one of our short videos on that package if one is available. After that, if you have questions, you can sign up for one of our Office Hours sections.

Please read and follow these instructions prior to attending the tutorial.

Intermediate Intermediate Usage of the SCC (Lecture)

Instructor: Katia Bulekova (ktrn@bu.edu)

BSC Monday, January 23, 9:30am – 11:30am
Zoom Friday, January 27, 9:30am – 11:30am

This tutorial will provide some more advanced techniques and common strategies used for interacting with the Shared Computing Cluster and its resources.

The topics discussed during the tutorial include:

  • Customizing your environment
  • Parallel computing on the SCC
  • Jobs monitoring (CPU and memory usage)
  • Profiling programs for performance optimization
  • General optimization strategies

Prerequisites: some prior experience with high performance computing or attendance of our “Introduction to BU’s Shared Computing Cluster” tutorial.

Beginner Introduction to the SCC for Neuroimagers (Hands-on)

Instructor: Mitch Horn (mhorn@bu.edu)

BSC Monday, January 30, 9:30am – 11:30am

This tutorial will introduce Boston University’s Shared Computing Cluster (SCC) for use with Magnetic Resonance (MR) neuroimaging methods. It will address basic concepts in MR neuroimaging and give investigators the background required to start or optimize their research. We will work hands-on with the SCC to highlight the wide variety of neuroimaging tools available on the SCC and how to effectively utilize them for high-performance computing. Software covered includes FreeSurfer, FSL, AFNI, MRIcron, Nipype, MATLAB, SPM, and CONN. Topics covered include loading data onto the SCC, project management (directory structure/permissions), medical imaging handling, batch processing, and interactive environments.

Beginner Research Computing Office Hours – Machine Learning New

Instructors: Brian Gregor (bgregor@bu.edu) and Scott Ladenheim (saladenh@bu.edu)

Zoom Friday, February 3, 9:30am – 10:30am

During Research Computing Office Hours, our staff will be happy to answer any question you might have related to using Machine Learning on the Shared Computing Cluster. If you are new to the SCC, we highly recommend you first attend one or more of our introductory SCC tutorials (“Introduction to BU’s Shared Computing Cluster” or “Introduction to Linux”) first or watch some of our introductory videos.

During our Office Hours we will be happy to answer questions, assist with use of the cluster, and help you get started with using the SCC. For more complex questions, emailing help@scc.bu.edu to receive tailored assistance is probably a better option.

Beginner Research Computing Office Hours – R, SAS, and Stata

Instructor: Katia Bulekova (ktrn@bu.edu)

Zoom Friday, February 3, 10:30am – 11:30am

During Research Computing Office Hours, our staff will be happy to answer any question you might have related to using R, SAS, and/or Stata on the Shared Computing Cluster. If you are new to the SCC, we highly recommend you first attend one or more of our introductory SCC tutorials (“Introduction to BU’s Shared Computing Cluster” or “Introduction to Linux”) first or watch some of our introductory videos.

During our Office Hours we will be happy to answer questions, assist with use of the cluster, and help you get started with using the SCC. For more complex questions, emailing help@scc.bu.edu to receive tailored assistance is probably a better option.

Register

Computer Programming Tutorials

Beginner Introduction to Python, Part One (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSC Monday, January 23, 12:00pm – 2:00pm

This is an introduction to the essential features of Python. This first part of the tutorial includes an introduction to basic types, if-statements, functions, lists, dictionaries, loops, and modules. The tutorial includes the use of a popular Python development environment and covers setting up Python on your own computer in addition to using Python on the SCC. This is a two-part tutorial so please remember to sign up for both sessions.

Recommended but not required: some programming experience. For example, you should understand concepts like loops and functions.

If you do not have Python installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Introduction to Python, Part Two (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSC Wednesday, January 25, 12:00pm – 2:00pm

This tutorial is a continuation of “Introduction to Python, Part One” and introduces more features of the language, common libraries such as numpy and matplotlib, and the basics of debugging Python programs. Please make sure you sign up for part one as well.

If you do not have Python installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Intermediate Numerical Computing in Python (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSCFriday, January 27, 12:00pm – 2:00pm
ZoomTuesday, January 31, 1:00pm – 3:00pm

Python is now widely used for numerical calculations and data analysis. This tutorial is an introduction primarily to the Numpy library which provides data structures and algorithms that are optimized for numeric data. The Numpy library is the basis for a wide variety of numeric and graphics libraries in Python. The usage of the numpy multi-dimensional array type will be covered in detail. The Scipy library and how it can be effectively used with Numpy arrays and other Python data structures will be discussed. This tutorial assumes familiarity with Python.

Prerequisite: If you are new to the Python programming language we strongly recommend that you also register for the “Introduction to Python” two-part tutorial.

If you do not have Python installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Introduction to Julia (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSC Monday, February 6, 9:30am – 11:30am

Julia is a high-performance programming language, but with many features more common to lower performance interpreted languages. Many of its features are well suited for numerical analysis and computational science, with functions and syntax that are built around supporting this. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a “why” versus “what” way. The tutorial introduces participants to common ways of using Julia and basic features including operators, loops, conditionals, and functions.

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Data Analysis Tutorials

Beginner Introduction to R (Hands‐on)

Instructor: (BUMC) Katia Bulekova (ktrn@bu.edu) and (CRC) Dennis Milechin (milechin@bu.edu)

BUMC Tuesday, January 17, 10:00am ‐ 12:00pm
BSC Tuesday, January 31, 10:00am ‐ 12:00pm

R is the most powerful, rapidly developing, highly reliable, open source statistical language. It is widely used among statisticians for the development of statistical software and for data analysis. New features appear every few months.

This tutorial introduces the R environment for statistical computing and will cover the following topics:
  • operators and arithmetic operations
  • atomic types, variable rules and built-in constants
  • scalar and vector function overview
  • working with data (workspace setup as well as reading, creating, exploring, and saving data)
  • working with R data types (vectors, matrices, lists, data frames)
  • working with script files
  • installing and loading R extension packages and getting help
  • overview of functions for data analysis
After completing this tutorial you will:
  • know the basics of the R environment.
  • get a solid understanding of various data types and objects used in R.
  • be able to create, load and analyze data.
  • find appropriate functions and get necessary help and examples for these functions.

If you do not have R and RStudio installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Data Wrangling in R (Hands‐on)

Instructor: (BUMC) Katia Bulekova (ktrn@bu.edu) and (CRC) Dennis Milechin (milechin@bu.edu)

BUMC Thursday, January 19, 10:00am ‐ 12:00pm
BSC Thursday, February 2, 10:00am ‐ 12:00pm

“Tidy data” is a term that describes a standardized approach to structuring datasets to make statistical analyses and visualizations easier. In this tutorial you will learn how to modify, filter, arrange, and summarize your data with dplyr and other tidyverse packages. We will go over operations like merging two or more datasets, reshaping your data into the layout that works the best, and summarizing the data to explore hidden levels of information.

Prerequisite: If you are new to the R environment we strongly recommend that you also register for the “Introduction to R” tutorial.

If you do not have R and RStudio installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Graphics in R: ggplot2 (Hands‐on)

Instructor: (BUMC) Katia Bulekova (ktrn@bu.edu) and (CRC) Dennis Milechin (milechin@bu.edu)

BUMC Thursday, January 26, 10:00am ‐ 12:00pm
BSC Tuesday, February 7, 10:00am ‐ 12:00pm

The R package ggplot2 is a reliable and powerful tool for graphics and plotting scientific data. This tutorial will cover the theory behind ggplot2’s approach to visualization. We will cover the general flow of building a plot, mapping aesthetics, adding layers, and manipulating scales, facets, and coordinates. After this tutorial, you will be able to navigate the ggplot2 package with an understanding of how to design visualizations from data frames versus vectors (as in base R graphics) for elegant and professional illustrations of data. Documentation and alternative resources are included to help you continue developing in ggplot2 on your own after the tutorial.

Prerequisite: If you are new to the R environment we strongly recommend that you also register for the “Introduction to R” tutorial.

If you do not have R and RStudio installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Intermediate Creating Pretty Documents using R Markdown and Quarto (Hands‐on) New

Instructor: Katia Bulekova (ktrn@bu.edu)

BUMC Thursday, February 2, 10:00am ‐ 12:00pm
BSC Thursday, February 9, 10:00am ‐ 12:00pm

R Markdown is a powerful tool that helps produce elegantly formatted documents.

In this tutorial, we will learn how to use the rmarkdown, ggplot2, and kable packages, as well as their extensions to develop reports that include code, graphics, and tables.

We will cover various options to customize code chunks, figures, and tables and go over the best practices for organizing the code.

We will also explore Quarto which is a new tool that allows using markdown and creating elegantly formatted articles, reports, and presentations.

If you do not have R and RStudio installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Python for Data Analysis (Hands‐on)

Scott Ladenheim (saladenh@bu.edu)

BSC Thursday, January 26, 1:00pm – 3:00pm

This tutorial will introduce the basics of Data Analysis with Python and its powerful library Pandas.

What you will learn:
  • Importing and exporting data
  • Basic data processing, cleaning, and manipulation
  • Simple data visualization techniques (more advanced techniques are introduced in the Data Visualization in Python tutorial)
  • Basic statistical analysis (time permitting)

This tutorial will introduce and use the popular Jupyter Notebook system for working with Python code.

If you do not have Python installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Data Visualization in Python (Hands‐on)

Scott Ladenheim (saladenh@bu.edu)

BSCMonday, January 30, 12:00pm – 2:00pm

Python is one of the most popular programming languages for data analysis, data mining, and machine learning applications. Powerful Python plotting/visualization packages such as matplotlib and seaborn provide convenient tools for data visualization. This tutorial will explore many useful functions of these two packages by doing hands-on exercises on some real data sets. Some Python programming experience is required. This tutorial will introduce and use the popular Jupyter Notebook system for working with Python code. We will briefly introduce the pandas library for data preprocessing but strongly recommend that you also register for the Python for Data Analysis (Hands‐on) tutorial to learn more about Pandas.

If you do not have Python installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Introduction to MATLAB, Part One (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSC Monday, January 30, 3:00pm ‐ 5:00pm

MATLAB is an interpreted programming language. It was originally developed for linear algebra and engineering problems, but now has wide applicability and toolboxes for areas ranging from medicine, economics, and machine learning. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a “why” versus “what” way. Part One introduces participants to the user-interface and basic features including operators, loops, and conditionals. Please also register for Part Two.

No prior programming experience in any language is required to attend this course.

If you do not have MATLAB installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Introduction to MATLAB, Part Two (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSC Wednesday, February 1, 3:00pm ‐ 5:00pm

MATLAB is an interpreted programming language. It was originally developed for linear algebra and engineering problems, but now has wide applicability and toolboxes for areas ranging from medicine, economics, and machine learning. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a “why” versus “what” way. Part Two introduces participants to basic features including file reading/writing, functions, and text processing. Please also register for Part One.

No prior programming experience in any language is required to attend this course.

If you do not have MATLAB installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Introduction to FreeSurfer (Hands-on)

Instructor: Mitch Horn (mhorn@bu.edu)

BSC Wednesday, February 1, 12:00pm – 2:00pm

This tutorial is a hands-on walkthrough of the basics of using FreeSurfer, including how to process subjects through the FreeSurfer pipeline, how to inspects processed data for accuracy, how to intervene if results are inaccurate, and how to export statistics. It is recommended that you attend either the “Neuroimaging on the SCC” or the “Introduction to BU’s SCC” tutorial before this one ifyou are not a regular user of the SCC.

Register

Domain Specific Topics Tutorials

Beginner Introduction to ImageJ (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

ZoomMonday, February 6, 12:00pm – 2:00pm

ImageJ is a popular open source tool for image analysis and processing. In this tutorial we will cover the basics of digital images, the ImageJ interface, image manipulation, and performing quantitative measurements. ImageJ’s macro language and its macro recorder will be introduced to show how ImageJ can be used to perform automated image analysis.

If you do not have ImageJ installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Beginner Introduction to GIS Theory (Lecture)

Instructor: Dennis Milechin (milechin@bu.edu)

BSC Monday, February 13, 9:30am – 11:30am

This tutorial will introduce select core Geographic Information System (GIS) theory concepts that are utilized by the majority of GIS software and GIS libraries. The goal of this tutorial is to get you familiar with common GIS terminology and concepts that may not be clearly described when reading “How To” manuals of GIS software packages and GIS libraries. Topics that will be covered include:

  • What is GIS?
  • Geographic Coordinate Systems & Projections
  • Spatial Data Models
  • Data Layers
  • Overview of spatial data files
  • Example of a GIS workflow
  • Overview of available GIS software and libraries

The content will be presented in lecture style and therefore no software needs to be installed prior to the tutorial.

Intermediate GIS Using R: sf package (Hands-on) New

Instructor: Dennis Milechin (milechin@bu.edu)

BSC Tuesday, February 14, 10:00am – 12:00pm

In this tutorial we will go over the basic usage of the package “sf”, which provides tools for working with vector spatial data (points, lines, and polygons). We will go over how to read/write GIS vector files, how to work with the attribute table, and introduce some basic GIS spatial functions.

Prerequisites: It is encouraged that anyone who attends this tutorial be familiar with the topics and concepts presented in the following tutorials:

  • Introduction to R
  • Data Wrangling in R
  • Graphics in R: ggplot2
  • Introduction to GIS Theory

If you do not have R and RStudio installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Intermediate GIS Using R: terra package (Hands-on) New

Instructor: Dennis Milechin (milechin@bu.edu)

BSC Thursday, February 16, 10:00am – 12:00pm

In this tutorial we will go over the basic usage of the package “terra”, which provides tools for working with raster data. We will go over the basic functions of reading, writing, and manipulating raster data.

Prerequisites: It is encouraged that anyone who attends this tutorial be familiar with the topics and concepts presented in the following tutorials:

  • Introduction to R
  • Introduction to GIS Theory

If you do not have R and RStudio installed on your laptop, please read and follow these instructions prior to attending the tutorial.

Register

Student Demonstration

Beginner From Spatial Data to Time-Lapse: Creating Videos with ArcGIS and Premiere (Demonstration)

Instructors: Yingtong Zhang, PhD Candidate, BU Earth & Environment
Instructor: Yaxiong Ma, Postdoctoral Researcher, BU Sargent College

BSC Thursday, February 2, 1:00pm – 3:00pm

In this training session, you will learn how to use ArcGIS and Adobe Premiere to create a time-lapse video that visualizes medium-high resolution spatial-temporal data over a large area. Time-lapse videos can be an effective way to communicate research findings or to showcase changes in a particular area. Visualizing time series data with small details at a continental to global scale can be challenging, but with some coding skills and software, you can create engaging animations/movies that get your ideas and work delivered more effectively. By the end of this session, I will share the tools, scripts, and steps needed to create your own video.

Register