Spring 2021 Tutorial Series

January 25, 2021 – February 22, 2021

Our 2021 Spring Tutorial Series has ended. We will post the Summer schedule once it is available, generally 2-4 weeks before the tutorials begin in the Summer.

Registration is open for the RCS 2021 Spring Tutorial Series. All tutorials are being held online via Zoom and registration is required three business days in advance to receive a Zoom link.Most of the tutorials are hands-on and, to get the most out of the tutorial, you should have the appropriate software installed on your computer before the tutorial starts. Tutorial descriptions, registration, and recommendations for software and computer setups can be found below.

Note: Zoom 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 in the future.

Of interest: New tutorials this Spring: Research Computing Office Hours, Introduction to Julia, Introduction to PyTorch, and Introduction to Parallel Programming.

We have tagged tutorials as Beginner (), Intermediate (), or Advanced () to assist you in selecting the tutorials that are appropriate for your level of experience.

The IS&T Research Computing Services (RCS) group offers a tutorial series on programming, data analysis, high performance computing, and visualization 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 group or lab. Please contact us at help@scc.bu.edu if you are interested.

Register

Tutorials 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

This Spring’s tutorials will all be taught online using Zoom. The information for the Zoom session for each tutorial will be sent to all registered attendees two business days before the tutorial begins. Attendees must register before that date.


Tutorial Descriptions and Times

Research Computing Basics Tutorials

Introduction to Linux (Hands‐on)

Instructor: Augustine Abaris (augustin@bu.edu)

Monday, January 25, 10:00am – 12:00pm
Tuesday, January 26, 1:00pm – 3:00pm

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 home machine before, please read and follow these instructions prior to attending the tutorial.

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

Instructor: Aaron Fuegi (aarondf@bu.edu)

Monday, January 25, 1:00pm – 3:00pm
Tuesday, January 26, 10:00am – 12:00pm

This tutorial will introduce Boston University’s Shared Computing Cluster (SCC) in Holyoke, MA. This Linux cluster has more than 18500 processors and over 6 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.

We are also introducing a new option this semester to get you more detailed knowledge on using a particular application on the SCC. Fifteen minutes after this tutorial, you can take a tutorial focused on an application of your choice. These tutorials are entitled “Using (Python, MATLAB, R, or Machine Learning) on the SCC”. They run concurrently for 30 minutes so you can only sign up for one each day.

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

Using (Python, MATLAB, R, or ML) on the SCC (Hands‐on)

Instructors: Various RCS Staff Members

Monday, January 25, 3:15pm – 3:45pm
Tuesday, January 26, 12:15pm – 12:45pm

During these tutorials, we will introduce an appropriate workflow for working with a specific application (Python, MATLAB, R, or Machine Learning) on the Shared Computing Cluster. We will cover both interactive and batch jobs, address unique features of the application and step through the process of effective use of the SCC OnDemand tools.

Prerequisite: You should have some familiarity with the SCC, such as from taking our “Introduction to BU’s Shared Computing Cluster” tutorial. You should also have some familiarity with the application you are interested in, such as having used it on a desktop machine. Taking this tutorial and then later taking an Introduction tutorial to the application can be done but will be a bit harder to follow.

Intermediate Usage of the SCC (Lecture)

Instructor: Katia Bulekova (ktrn@bu.edu)

Thursday, January 28, 10:00am – 12:00pm

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

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

Managing Projects on the SCC for LPIs (Lecture)

Instructor: Charlie Jahnke (cjahnke@bu.edu)

Thursday, January 28, 1:00pm – 3:00pm

Intended for Lead Project Investigators (LPI) and Administrative Contacts, this tutorial covers a high level overview and best practices for managing projects on the Shared Computing Cluster (SCC). Topics include compute resources, project creation, project management, storage/SU allocation management, and the Buy-In Program.

Research Computing Office Hours

Instructors: Katia Bulekova (ktrn@bu.edu) and other RCS staff

Friday, January 29, 10:00am – 11:00am

During Research Computing Office Hours, our staff will be happy to answer any question you might have related to 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”, “Using (Python, MATLAB, R, or ML) on the SCC”, “Introduction to Linux”) first. For those interested in SAS or Stata, we also have short introductory videos available which you should watch first (SAS Video, Stata Video).

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 schedule a one-on-one session is probably a better option.

Register

Computer Programming Tutorials

Introduction to Python, Part One (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

Wednesday, January 27, 6:00pm – 8:00pm

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 home machine, please read and follow these instructions prior to attending the tutorial.

Introduction to Python, Part Two (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

Thursday, January 28, 6:00pm – 8:00pm

Note that this tutorial was originally scheduled a day later on Friday, January 29 from 6-8pm but was rescheduled to one day earlier.

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 home machine, please read and follow these instructions prior to attending the tutorial.

Numerical Computing in Python (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

Monday, February 1, 6:00pm – 8:00pm

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 home machine, please read and follow these instructions prior to attending the tutorial.

Introduction to Perl, Part One (Lecture)

Instructor: Tim Kohl (tkohl@bu.edu)

Tuesday, February 2, 10:00am – 12:00pm

Perl is a powerful and versatile programming language that can be used for a wide variety of programming tasks, including, but not limited to, text/data processing, system administration, and Web applications. Combing elements of C, Unix shell scripting languages, as well as text-processing utilities such as sed and awk, Perl can be used for both large scale projects and for small applications. Some experience in a command line environment (e.g. Unix) is helpful, but the basics of Perl are simpler than those of other languages making it accessible to a new programmer. Attending our “Introduction to Linux tutorial” or equivalent background is recommended. Please remember to sign up for all four sessions to complete the tutorial.

Introduction to Perl, Part Two (Lecture)

Instructor: Tim Kohl (tkohl@bu.edu)

Thursday, February 4, 10:00am – 12:00pm

This tutorial is a continuation of “Introduction to Perl, Part One“. We strongly recommend that if you are interested in this tutorial, you register for Parts One, Three, and Four as well.

Introduction to Perl, Part Three (Lecture)

Instructor: Tim Kohl (tkohl@bu.edu)

Tuesday, February 9, 10:00am – 12:00pm

This tutorial is a continuation of “Introduction to Perl, Part Two“. We strongly recommend that if you are interested in this tutorial, you register for Parts One, Two, and Four as well.

Introduction to Perl, Part Four (Lecture)

Instructor: Tim Kohl (tkohl@bu.edu)

Thursday, February 11, 10:00am – 12:00pm

This tutorial is a continuation of “Introduction to Perl, Part Three“. We strongly recommend that if you are interested in this tutorial, you register for Parts One, Two, and Three as well.

Introduction to Julia (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

Friday, February 5, 10:00am – 12:00pm

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.

Register

Data Analysis Tutorials

Introduction to R (Hands‐on)

Instructor: Katia Bulekova (ktrn@bu.edu)

Wednesday, January 27, 1:00pm ‐ 3:00pm

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 home machine, please read and follow these instructions prior to attending the tutorial.

Data Wrangling in R (Hands‐on)

Instructor: Katia Bulekova (ktrn@bu.edu)

Friday, January 29, 1:00pm ‐ 3:00pm

“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 home machine, please read and follow these instructions prior to attending the tutorial.

Graphics Using Base R Packages (Hands‐on)

Instructor: Katia Bulekova (ktrn@bu.edu)

Monday, February 1, 1:00pm ‐ 3:00pm

R provides extensive and powerful graphics options that allow for the production of publication-ready, high quality diagrams, and plots. This tutorial introduces R graphics libraries and functions.

After completing this tutorial you will:
  • understand what to expect from R’s graphics capabilities.
  • be able to create, modify, and customize graphs and plots used in statistical analysis.
  • find appropriate libraries, download, and use them for your visualization needs.

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 home machine, please read and follow these instructions prior to attending the tutorial.

Graphics in R: ggplot2 (Hands‐on)

Instructor: Katia Bulekova (ktrn@bu.edu)

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

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 home machine, please read and follow these instructions prior to attending the tutorial.

Programming in R (Hands‐on)

Instructor: Katia Bulekova (ktrn@bu.edu)

Friday, February 5, 1:00pm ‐ 3:00pm

This tutorial is the third in a series of R tutorials. It introduces basic R programming. It covers the following topics:

  • if-else statements
  • loops
  • user functions and argument definitions
  • local and global variables
  • apply function family
  • sourcing

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

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

R Code Optimization (Lecture)

Instructor: Katia Bulekova (ktrn@bu.edu)

Monday, February 8, 1:00pm ‐ 3:00pm

This tutorial is primarily aimed at those who have some experience working in a Linux environment and programming in R. The topics covered in this tutorial:

  • debugging and profiling R code
  • choosing the right functions to speed-up your code
  • parallelization techniques
  • tuning your code for faster performance on the SCC cluster

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

Introduction to MATLAB, Part One (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

Tuesday, February 2, 1:00pm ‐ 3:00pm

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 home machine, please read and follow these instructions prior to attending the tutorial.

Introduction to MATLAB, Part Two (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

Thursday, February 4, 1:00pm ‐ 3:00pm

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 home machine, please read and follow these instructions prior to attending the tutorial.

Python for Data Analysis (Hands‐on)

Instructor: Ahmed Aly (aaly@bu.edu)

Wednesday, February 3, 3:30pm – 5:30pm

This tutorial will introduce the basics of Data Analysis with Python and its powerful libraries such as Pandas and Matplotlib.

What you will learn:
  • Importing and Exporting the data
  • Basic data processing, cleaning, and manipulation
  • Basic inferential statistical analysis
  • Data Visualization techniques

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

Introduction to PyTorch (Hands‐on)

Instructor: Ahmed Aly (aaly@bu.edu)

Monday, February 8, 3:30pm – 5:30pm

This tutorial is aimed towards an audience with moderate Python experience. In addition, familiarity with principal Machine Learning concepts is required, such as Loss Function, Classification Accuracy, and Training/Validation data split.

We will explore various aspects of the PyTorch Deep Learning framework library. This will be a gentle introduction, catering more to getting the participants confident to “make the first leap” towards using PyTorch for their projects. We will go through some basic aspects of PyTorch, followed by a complete example of a Machine Learning task. The session will feature hands-on coding by the presenter, as well as exercises for the participants.

If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial or otherwise install Python as you deem fit. In addition, to install PyTorch follow the instructions on the PyTorch webpage. Make sure you can import the “torch” package successfully in Python.

Introduction to SQL (Hands‐on)

Instructor: Yun Shen (yshen16@bu.edu)

Wednesday, February 10, 1:00pm – 3:00pm

This tutorial will introduce the basics of SQL (Structural Query Language), the language used in relational database management systems. It will cover the most commonly used database query statements, from very basic ones on a single table to more complicated join queries on multiple tables. It will also briefly go over some advanced data manipulation queries, such as insert/update/delete. Some database technology fundamentals will also be introduced. No previous SQL or database knowledge is required. Students will be actively participating in this tutorial.

Python Parallelization (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

Wednesday, February 10, 6:00pm – 8:00pm

This tutorial is an introduction to the variety of ways that parallel computations can be performed in Python. Ways of identifying code that can benefit from parallelization will be discussed. Several parallelization methods using the Python language and external libraries will be covered with examples. This tutorial assumes an intermediate understanding of the Python language and parallel computing concepts. It is strongly recommended that the “Introduction to Parallel Programming” tutorial be taken first for those new to parallel software development.

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

Introduction to SAS (Hands‐on)

Instructor: Jack Chan (jack@bu.edu)

Friday, February 12, 1:00pm – 2:00pm

SAS (Statistical Analysis System) is one of the most powerful statistical packages available on any computer platform. This tutorial will introduce you to SAS on the desktop.

After completing this tutorial, you will be able to:
  • access SAS on Windows
  • create, edit, and save program files containing SAS commands
  • obtain printed output
  • create, run, and modify your own programs

SAS is only available for Windows. If you do not currently have it installed, please email Jack Chan of IS&T RCS (jack@bu.edu) to see if you may qualify to get it from BUMC OIT.

Introduction to SPSS (Hands‐on)

Instructor: Jack Chan (jack@bu.edu)

Friday, February 12, 2:15pm – 3:15pm

SPSS (Statistical Package for the Social Sciences) is a widely used program for analyzing data. SPSS uses windows and dialog boxes to manipulate data and perform statistical analyses. This hands-on tutorial will introduce you to the basics of SPSS and will give you one hours’ practice using SPSS on Microsoft Windows.

After completing this tutorial, you will be able to:
  • enter data into SPSS
  • use SPSS to transform data
  • use SPSS to perform basic statistical analyses

Prior to the tutorial, current BU students, faculty, and staff members should download and install SPSS for free.

Register

High Performance Computing Tutorials

Python Optimization (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

Thursday, February 4, 6:00pm – 8:00pm

This tutorial is for those with intermediate Python experience who are interested in optimizing their code to maximize performance. The topics covered are profiling and timing Python code, selecting data structures, avoiding common pitfalls, using external libraries, and tuning Python code.

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

Introduction to Parallel Programming (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

Monday, February 8, 6:00pm ‐ 8:00pm

This “Introduction to Parallel Programming” tutorial is recommended for anyone interested in learning more about the topic or who plans on taking our language-specific tutorials on parallel programming. This tutorial is not oriented towards any program language in particular and is intended for anyone with programming experience. This tutorial covers basic topics such as the use of processes and threads, types of computer hardware for parallel computing, and the limits of parallelization as a strategy. Additionally, several common data and algorithm patterns in software will be discussed along with effective strategies on how to parallelize them.

MATLAB Performance Optimization (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

Tuesday, February 9, 1:00pm ‐ 3:00pm

For many programs speed is the main research productivity factor, meaning the difference between hours versus months in getting results. For many applications it is possible to write MATLAB programs that are as fast or only a factor of 2-3 times slower than a compiled C++ or Fortran version, while requiring dramatically fewer lines of code and/or being faster to develop. For existing MATLAB programs, optimizing their performance can provide dramatic speedups. This tutorial will take a “case study” hands-on approach, examining several example programs and optimizing their performance. In the process this will demonstrate useful speedup techniques including vectorization, bsxfun, memory management, and “big O” algorithmic improvements.

Basic knowledge of MATLAB is assumed.

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

MATLAB Parallel Computing Toolbox (PCT) (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

Thursday, February 11, 1:00pm ‐ 3:00pm

The MATLAB Parallel Computing Toolbox allows you to write programs that leverage parallelism by dividing up work between independent cores. It is possible to convert many serial MATLAB applications to parallel MATLAB applications with relatively few code modifications and no programming in a low-level language. This tutorial will introduce MATLAB parallel processing tools, such as parfor, spmd, and distributed array types. Participants interested in trying this hands-on will be able to try this on BU’s Shared Computing Cluster (SCC). It is strongly recommended that the “Introduction to Parallel Programming” tutorial be taken first for those new to parallel software development.

Basic knowledge of MATLAB is assumed.

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

Register

Visualization Tutorials

Introduction to GIS Theory (Lecture)

Instructor: Dennis Milechin (milechin@bu.edu)

Tuesday, February 16, 1:00pm – 2:00pm

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

This tutorial is a prerequisite for “Introduction to QGIS” and “Introduction to ArcGIS Pro” offered by RCS. The content will be presented in lecture style and therefore no software needs to be installed prior to the tutorial.

Introduction to ImageJ (Hands‐on)

Instructor: Brian Gregor (bgregor@bu.edu)

Tuesday, February 16, 6:00pm – 8:00pm

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 home machine, please read and follow these instructions prior to attending the tutorial.

Introduction to ArcGIS Online, Part One (Hands‐on)

Instructor: Dennis Milechin (milechin@bu.edu)

Wednesday, February 17, 1:00pm – 3:00pm

This tutorial is Part One of a two part series. ArcGIS Online is a cloud platform, developed by ESRI, that provides a collection of tools and services that allow one to create interactive web maps that can be published and shared with the world. Explore some examples in the ESRI lesson gallery.

Part One will cover the following topics:

  • ArcGIS Online – What Is it?
  • GIS Theory and ArcGIS Online
  • Example Workflow
  • Permissions
  • Geo Apps
  • Resources
  • Explore ArcGIS Online (Hands-on)

See Part Two for session description.

No software installation is required; just an internet connection and an internet browser. For those who register for this tutorial, a BU ArcGIS Online account will be created prior to the tutorial for free. More information about account creation can be found here.

Prerequisite: Introduction to GIS Theory

Related Tutorials: Introduction to ArcGIS Pro

Introduction to ArcGIS Online, Part Two (Hands‐on)

Instructor: Dennis Milechin (milechin@bu.edu)

Friday, February 19, 1:00pm – 3:00pm

This tutorial is Part Two of a two part series. ArcGIS Online is a cloud platform, developed by ESRI, that provides a collection of tools and services that allows one to create interactive web maps that can be published and shared with the world. Explore some examples in the ESRI lesson gallery.

In this Part Two, we will use ArcGIS Online to complete an ESRI provided excerise “Get Started with Map Viewer”, where we will create a map showing the locations of Hawaii’s volcanoes, volcano shleters, and volcano hazard zones. We will find and import data into a map, apply symbology to the data, publish the map as a Geo App, and then explore tools for creating a Story Map.

No software installation is required; just an internet connection and an internet browser. For those who register for this tutorial, a BU ArcGIS Online account will be created prior to the tutorial for free. More information about account creation can be found here.

Prerequisite: Introduction to GIS Theory

Related Tutorials: Introduction to ArcGIS Pro

Introduction to QGIS (Demonstration)

Instructor: Dennis Milechin (milechin@bu.edu)

Thursday, February 18, 1:00pm – 3:00pm

QGIS is an open source GIS desktop application that can be downlaoded for free and it provides a collection of tools for managing, analyzing, and visualizing spatial data. This application has a resemblance to commercial GIS products ArcGIS Pro and ArcMap. In this session I will introduce you to the user interface of QGIS and go through simple workflows to get you started on using the software, such as importing data, symbolizing data, adding labels, and more.

So that we can cover more material during this timeframe, this tutorial will be presented as a demonstration, rather than a hands-on session. This tutorial will be recorded and the link to the recorded session will be distributed to the attendees so that you can practice my examples at your own pace.

Prerequisite: Introduction to GIS Theory

Related Tutorials:

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

Introduction to ArcGIS Pro (Demonstration)

Instructor: Dennis Milechin (milechin@bu.edu)

Monday, February 22, 1:00pm – 3:00pm

ArcGIS Pro is a popular commercial Geographic Information System (GIS) software package used to manage, analyze, and visualize spatial data. This software package has a broad use of applications, including research, emergency response planning, tracking location of utility lines and their condition, and identifying prime locations for cell phone towers or wind turbines. You can also use ArcGIS Pro to publish your maps and results to ArcGIS Online and quickly share your findings with others.

In this tutorial I will introduce you to the ArcGIS Pro user interface and explore the basic tools you need to get started. I will work through examples of importing data, exploring the attributes of the data, symbolizing the data, and publishing the data to ArcGIS Online.

At the moment, I am not able to provide attendees of this session access to ArcGIS Pro software for free; therefore this tutorial will be presented as a demonstration rather than hands-on. If you wish to obtain a copy of ArcGIS Pro, please see the CAS IT page in regards to obtaining ArcGIS Pro for your machine.

Related Tutorials:

Register