Research Services offers tutorials and workshops on a variety of topics. Each semester, we present a series of tutorials. If you have suggestions, please contact researchservices@bc.edu. We are also available for consulting.
Tutorial Descriptions - Fall 2025
September Tutorials
Python For Everyone
This tutorial is designed for beginners with no prior experience in programming with Python. From this tutorial, you will gain a foundational understanding of Python, one of the most popular and versatile programming languages today. You'll also learn how to use Jupyter Notebook, a powerful tool for writing and running Python code interactively.
During this session, we’ll discuss:
- The basics of Python.
- How to write and execute Python code in Jupyter Notebook.
- Essential programming concepts.
- Hands-on practice with guided exercises to solidify your learning.
Presented by Yixin Pan.
Friday, September 12th, 2025 from 11 am - 12 pm (Zoom)
Introduction to BC’s Linux Cluster
This tutorial is intended to be an introduction to the Linux cluster at Boston College. Currently, the user can access the cluster of Andromeda. An overview, the primary components, and examples of how to use BC’s Linux cluster. This hands-on tutorial will cover:
This hands-on tutorial will cover:
- Overview of the Andromeda Linux cluster system at Boston College
- The hardware architecture
- Management of Linux Cluster
- How to remote access the cluster
- How to access the cluster through the web-based portal:OOD
- Common Unix/Linux commands
- How to use software modules and SLURM queuing system
- How to submit jobs to cluster
Presented by Wei Qiu.
Tuesday, September 16, 2025 from 12 – 1:30 pm (Zoom)
Intro to Stata 1: Getting Started, Descriptive Stats & Do Files
Stata is a powerful, yet easy-to-use statistical package. This hands-on tutorial is designed as an introduction for beginning users who are just getting started using Stata. The emphasis of this tutorial is on exploring the data, cleaning the data for research purposes, and generating descriptive Statistics.
- Accessing Stata on Amazon AppStream
- Loading data
- Data manipulation
- Descriptive statistics
- Do-files and log files
Presented by Yufeng Shi.
Tuesday, September 16, 2025 from 2 - 3:30 pm (Zoom)
Introduction to Machine Learning
Machine learning is a data analysis method of getting computers to act without being explicitly programmed. It is based on the algorithms that use statistics to build models and find patterns in massive amounts of data. Machine Learning is extensively used in a wide variety of applications and changing our day-to-day life.
This tutorial is for beginners to learn and will cover:
- Introduction/Definition
- Where and Why Machine Learning is used
- Types of Learning
- Supervised Learning
- Unsupervised Learning
Presented by Yixin Pan.
Friday, September 19th, 2025 from 11 am - 12 pm (Zoom)
Introduction to Regression
As the most common methodology in statistical analysis, regression is an important tool for any modern researcher. This course is intended as an introduction to standard or linear regression. We will focus on estimation methods, identifying and validating model assumptions. We will also focus on hypothesis testing for regression estimates and statistical model building. We will use R software but the goal of the course is to learn concepts and is not intended as a tutorial for any specific software.
Note: The mixed modeling course is a natural sequel to Introduction to Regression.
Presented by Matt Gregas.
Tuesday, September 23, 2025 from 10 – 11:30 am (Zoom)
Introduction to Linear Mixed Effects Modeling
This tutorial is a brief introduction to linear mixed effects (LME) modeling, also known as multilevel modeling or hierarchical linear modeling. LME models are essential for researchers handling either longitudinal (repeated measures) data or data that is hierarchical (e.g., students nested within classrooms, and classrooms nested within schools). Many familiar methods such as ANOVA or regression assume that all observations are recorded independently; Clustered data and data with repeated measures violate this assumption. LME modeling is an extension of regression that accounts for the correlated data structure inherent in repeated-measures and clustered designs. In this tutorial, we introduce the model, discuss when and why this method should be used, and how to interpret results in common statistical programs. This tutorial is appropriate for anyone with a background in linear regression. Those wanting a refresher may consider attending the Research Services tutorial on regression immediately preceding this tutorial.
Presented by Melissa McTernan.
Tuesday, September 23, 2025 from 12 - 1:30 pm (Zoom)
Intro to Stata 2: Graphing, Dataset Combining, Linear Regression
Stata is a powerful, yet easy-to-use statistical package. This hands-on tutorial is designed as an introduction for beginning users who are just getting started using Stata. The emphasis in this tutorial is on basic graphing, merging data, and linear regression.
- Basic graphing and graph editor
- Combining multiple datasets
- Linear Regression in Stata
Presented by Yufeng Shi.
Tuesday, September 23, 2025 from 2 - 3:30 pm (Zoom)
Introduction to MATLAB
This tutorial serves as a basic introduction to MATLAB, a versatile and user-friendly programming language. It will provide an overview and practical examples of how to use MATLAB.
This hands-on tutorial will cover:
- Overview of MATLAB and its Applications
- Explanation of the MATLAB Interface
- Variables and Basic Commands
- Basic Math Operations
- Introduction to Arrays
- Plotting Graphs
Presented by Tevin Li.
Wei L. Qiu, the MATLAB Administrator for Boston College, will also be available to provide an opening statement and answer questions.
Wednesday, September 24, 2025, 12 – 1:30 pm (Zoom)
Introduction to Latent Growth Curve Modeling
This tutorial is a brief introduction to latent growth curve modeling (LGCM). LGCM is a flexible approach for modeling longitudinal or repeated measures data. These models are fit within a structural equation modeling (SEM) framework where latent variables are used to capture linear or nonlinear "growth trajectories," or change across time. A researcher may be interested in modeling within-person growth patterns (e.g., how are student math scores changing across time?) or interested in explaining between-person differences in within-person growth patterns (e.g., what factors explain the differences between the students who are improving across time and those who are not?). LGCM will allow a researcher to study these kinds of questions, and many more, within a single model. LGCM can be implemented in R, Mplus, Stata, AMOS, SAS, or JMP. In this tutorial, we introduce the model, discuss when and why this method should be used, and briefly demonstrate the approach and how to interpret the estimated parameters. This tutorial is appropriate for anyone with a background in linear regression and some familiarity with SEM.
Presented by Melissa McTernan.
Tuesday, September 30, 2025 from 12 - 1:30 pm (Zoom)
Generalized Linear Models and Analytical Software
Generalized Linear Models (GLMs) are common methods of statistical modeling and are extensions of linear regressions. GLMs are used when data does not conform to the assumptions of linear models such as when data is binary, count, or highly skewed (much like real world data). In this tutorial, we will discuss GLM basics and assumptions appropriate for all levels of statistical knowledge across a variety of data-reliant fields. We will also discuss the syntax and considerations of setting up and running GLMs in various software programs such as R, Stata, SAS, etc.
Presented by Manjiri Sahasrabudhe and Viktoriya Babicheva.
Tuesday, September 30, 2025 from 2 – 3:30 pm (Zoom)
October & December Tutorials
Introduction to REDCap (Research Electronic Data Capture)
This tutorial is geared towards Boston College Principal Investigators, researchers, and research project team managers. REDCap stands for Research Electronic Data Capture. REDCap is a web-based, data collection, database management system that was originally developed at Vanderbilt University, initially for medical research. REDCap is now overseen by a consortium of academic research partners in the United States and throughout the world. Boston College is part of the REDCap Consortium.
In this introduction to REDCap we will discuss:
- How to request a REDCap project at Boston College
- How to make sure that your REDCap project complies with the mandates of your project's IRB approval
- How to create basic data collection forms
- An introduction to best practices for setting up your REDCap project
- We will discuss additional REDCap functionality including field embedding and piping, frequently used action tags, the potential for using twilio.com SMS services (for an additional fee), improved field calculations, and more
- How to enter data into REDCap
- How to control REDCap user access rights
- How to export your data
Research Services staff are available to meet with members of the Boston College community to discuss individual REDCap projects. Individual consultations or customized class consultations are available by emailing redcapadmin@bc.edu or viktoriya.babicheva@bc.edu.
If possible, prior to the tutorial, please fill out the BC REDCap Terms of Use survey described on the google doc below and indicate that you will be attending the REDCap Tutorial.
Presented by Viktoriya Babicheva.
Wednesday, October 1, 2025 from 1:30 – 2:30 pm (Zoom)
Developing and Administering Public Surveys in REDCap (Research Electronic Data Capture)
Public surveys are a powerful part of REDCap and are critical to researchers in collecting primary data for various research designs. This tutorial is geared toward Boston College Principal Investigators, researchers, and research project team managers. We will demonstrate the features of REDCap that are used most frequently to administer public surveys. People attending this survey should have at least a basic familiarity with setting up projects in REDCap.
In this beginner to intermediate level REDCap tutorial, we will discuss:
- Main project settings
- Survey distribution including Automated Survey Invitations (ASI)
- Survey workflows
- Optional modules and customizations
- Survey settings
Presented by Viktoriya Babichev.
(This tutorial was developed by Viktoriya Babicheva, BC REDCap Administrator and Research Data Consultant & Acquisition Analyst from Research Services with input from Kristen Dhanekula and Amanda Miller, REDCap Administrators, Vanderbilt University Medical Center.)
Wednesday, October 1, 2025 from 2:30 – 4 pm (Zoom)
Creating Web-Based Surveys with Qualtrics
Qualtrics offers a fairly intuitive graphical user interface to create complex surveys without complicated programming or coding. Qualtrics offers extensive documentation, free online tutorials, an extensive library of surveys and options for encryption and anonymity, and 24/7 customer support. Working within pre-defined templates, you can use many different types of questions, including text, multiple checkboxes, sliders, single-answer radio buttons, and Likert scales. Qualtrics offers extensive skip logic and validation functionality.
Once the survey is completed, data can be downloaded into a format that can be used with a variety of quantitative and qualitative analysis programs. Qualtrics also offers foreign language functionality.
This tutorial will demonstrate how to create a survey in Qualtrics and also include a section on research protections and informed consent with respect to online survey development, distribution, and analysis.
Boston College faculty, students, researchers, and administrative staff may create their own Qualtrics accounts in advance of the tutorial at bostoncollege.qualtrics.com (login with your BC credentials).
We will also discuss recent changes to BC’s Qualtrics license.
If possible, please complete the short BC Qualtrics Terms of Use Survey below before attending the tutorial.
Presented by Viktoriya Babicheva.
Tuesday, October 7, 2025 from 2 - 3:30 pm (Zoom)
Open OnDemand Tutorial
Learn how to access BC’s HPC clusters through Open OnDemand — a browser-based platform that simplifies file management, job submission, and interactive applications like Jupyter, MATLAB, and Stata.
- Logging into BC HPC via Open OnDemand
- Navigating the OOD Dashboard
- Managing Files and Storage
- Explore storage options including /home, /scratch, and /projects
- Using the Cluster Terminal
- Launching Interactive Tools
- Get started with Virtual Desktop, Jupyter, MATLAB, and Stata
- Monitoring and Managing JobsLive Demo:
Set up a jupyter_stack environment and create/run a simple Jupyter Notebook
Presented by Eliot Heinrich.
Wednesday, October 8, 2025 from 1 – 2:30 pm (Zoom)
JMP & Jamovi: A Comparative Tutorial for Academic Researchers
An introduction to JMP and Jamovi, two point and click statistical softwares, focusing on their uses, features, and practical application for statistical analysis. In this tutorial, participants will learn how to access both programs, and how to perform fundamental descriptive and inferential statistics, along with visualizations. Additionally, by the end of this tutorial, participants will be able to identify the key strengths and differences of each program, understand their underlying technologies, and choose the right tool for their specific academic research needs.
Presented by Manjiri Sahasrabudhe.
Thursday, October 9, 2025 from 2 - 3:30pm (Zoom)
Enhancing Quantitative Research with AI: Tools, Applications, and Ethical Considerations
AI tools are reshaping quantitative research by accelerating coding, streamlining documentation, and enhancing reproducibility. This tutorial introduces practical applications of AI in the research workflow, including R code auto-completion with Copilot, creating interactive study guides with NotebookLM, and using large language models like ChatGPT or Claude to generate annotated R and RMarkdown files. Participants will see live demonstrations, explore real-world use cases, and learn to identify common pitfalls such as over-reliance and hallucinated outputs. The session also addresses ethical considerations, including transparency and responsible AI use. Designed for researchers and data analysts, this session offers a practical and critical overview of integrating AI into your research workflow.
Presented by Melissa McTernan
Wednesday, October 15, 2025 from 2 - 3:30pm (Zoom)
Introduction to Data Cleaning in R
This tutorial is designed to help researchers develop their own data cleaning processes and practice data cleaning in R. We will begin by reviewing the principles of data management, as well as common tasks that are part of the data cleaning process. Additionally, there will be a brief introduction to R and Rstudio that aims to equip learners with the tools and vocabulary necessary to navigate R. Finally, there will be time to practice data cleaning functions in R using a real dataset. Attendees will receive instructions on how to download R, Rstudio, and the dataset upon registration.
Presented by RoseMarie Rohrs.
Friday, October 17, 2025 from 10:30 am - 12 pm (Zoom)
MATLAB Parallel Computing Workshop - Part 1: Introduction to MATLAB Parallel Computing
Part I of this workshop focuses on parallel computing with MATLAB on the desktop. The host introduces the concept of parallel computing and briefly covers code optimization before diving into common parallel programming constructs in MATLAB.
This workshop will feature:
- Introduction to parallel computing with MATLAB on the desktop (Covers common parallel programming constructs like parfor, parfeval, and parsimIntroduction to GPU acceleration)
- Introduction to scaling to clusters/clouds
- Hands-on exercises throughout
Presented by Damian Pietrus and his colleagues from MathWorks.
Monday, December 1, 2025, 1 – 4 pm (Zoom)
MATLAB Parallel Computing Workshop - Part 2: Applying MATLAB Parallel Server on the BC Cluster
Part II of this workshop focuses on walking users through accessing the Andromeda HPC cluster via Open OnDemand, setting up a cluster profile, and submitting sample jobs.
This workshop will feature:
- Introduction to using MATLAB Parallel Server on the Andromeda HPC Cluster via Open OnDemand
- How to set job submission arguments
- How to submit both single-node and multi-node jobs
- Introduction to GPU usage on Andromeda
- Introduction to debugging and troubleshooting
Presented by Damian Pietrus and his colleagues from MathWorks.
Thursday, December 4, 2025, 1 – 4 pm (in person in O'Neill 246)