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.
AI Tools
AI-Powered Interactive Learning Guides Using Gemini Notebook
The rise of AI chatbots is creating new opportunities for interactive statistical learning, yet concerns remain about their reliability, susceptibility to hallucinations, and the transparency of underlying sources. To address these concerns, the Academic Research Services Statistical Team has curated Google Gemini Notebooks to help researchers confidently explore advanced statistical methods. Unlike standard generative AI tools, these notebooks are grounded in carefully selected literature and documentation, providing more reliable guidance for statistical theory, methodological decision-making, and analysis. In this tutorial, attendees will learn how to interact with Gemini Notebook and we will also explore real-world examples that illustrate the advantage of our curated notebooks compared to standard generative AI chatbots.
Academic Research Services Gemini Notebooks are available for Boston College researchers at: https://rs.bc.edu/notebooklms-for-researchers/
Presented by Leonard Faul.
Tuesday, September 22, 2026 from 11 am - 12:30 pm (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 Gemini Notebooks, and using large language models like ChatGPT or Claude to generate annotated R and RMarkdown files and troubleshoot coding issues. 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, September 23, 2026 from 2 - 3:30pm (Zoom)
Essential High-Performance Computing: Interactive Guide with Gemini Notebook
Get instant, source-grounded answers to your questions about our high-performance computing (HPC) cluster with our newly developed, AI-assisted Gemini Notebook knowledge base. Join us for an interactive tutorial designed to help faculty and student cluster users effortlessly navigate related information about cluster resources, popular software available on the cluster, and parallel computation. We will guide you through the interactive site, demonstrating AI chatbot, visual mind map, and specialized slide decks covering Conda, VS Code, Jupyter, Julia, MATLAB parallel computing, and Gaussian.
Presented by Yufeng Shi.
Tuesday, October 6, 2026 from 2:30 - 3:30 pm (Zoom)
Vibe Coding for Researchers
In the AI era, everyone can write code, the real skill is knowing when to trust it. This tutorial introduces researchers to vibe coding using AI coding agents like Claude Code to generate, edit, and run code from natural-language prompts. It's aimed at computational and experimental research groups who want to speed up everyday coding tasks (analysis scripts, SLURM scripts for HPC job submission, debugging) without compromising the correctness and reproducibility that research demands. The session balances hands-on practicality with a strong emphasis on safety: knowing where AI-generated code can quietly go wrong, and how to catch it before it affects your results.
We will cover:
- What vibe coding is: autocomplete vs. chat-assisted vs. agentic coding
- Where it helps: prototyping, refactoring, tests, docs, debugging
- What it shouldn't replace: scientific reasoning and interpretation
- Live demo: SLURM job sweep, prompted and iterated
- Correctness risks: hallucinated APIs, silent logic errors, reproducibility
- Other risks: data privacy, compute costs, citation/plagiarism
- Safety checklist: diff review, sanity checks, confirmation gates, logging
Presented by Mohamed AlBegaowe.
Thursday, October 15 , 2026 from 1 - 2:30pm (Zoom)
Data Acquisition
Introduction to REDCap
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
- How to enter data into REDCap
- How to control REDCap user access rights
- How to export your data
- Time permitting: 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
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 and indicate that you will be attending the REDCap Tutorial.
Presented by Viktoriya Babicheva. Moderated by Thomas Ackon.
Monday, October 5, 2026 from 2 – 3:30 pm (Zoom)
Developing and Administering Public Surveys in REDCap
Public surveys are a powerful part of REDCap (Research Electronic Data Capture) 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
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.
Presented by Viktoriya Babicheva. Moderated by Thomas Ackon.
Monday, October 5, 2026 from 3:30 – 5 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: tinyurl.com/BCqterm
Presented by Viktoriya Babicheva. Moderated by Thomas Ackon.
Tuesday, October 6, 2026 from 11 am - 12:30 pm (Zoom)
HPC Cluster
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:
- 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
- How to use software modules and SLURM queuing system
- How to submit jobs to cluster
Presented by Wei Qiu.
Wednesday, September 16, 2026 from 12 - 1:30 pm (Zoom)
Parallel Computing on HPC Cluster
Modern research often involves large datasets, complex simulations, and AI workloads that exceed the capabilities of a single processor. Parallel computing accelerates these tasks by using multiple CPU cores, GPUs, and computing nodes to perform computations simultaneously.
This tutorial introduces the fundamentals of parallel computing, including serial versus parallel execution, common parallel programming models. Participants will also learn about multicore CPUs, GPUs, and the high-performance computing resources available on Boston College's Andromeda cluster.
By the end of the session, attendees will be able to identify opportunities for parallelization in their research workflows and better understand how to leverage BC's HPC resources to accelerate computational research.
Presented by Wei Qiu.
Monday, September 28, 2026 from 1 - 2:30 pm (Zoom)
Software
Introduction 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 (If you need access to STATA, please enter a ticket at bc.edu/researchhelp)
- Loading data
- Data manipulation
- Descriptive statistics
- Do-files and log files
Presented by Yufeng Shi.
Tuesday September 22, 2026 from 2:30 - 4pm (Zoom)
Introduction 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 29, 2026 from 2:30 - 4 pm (Zoom)
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 a Jupyter Notebook.
- Essential programming concepts.
- Hands-on practice with guided exercises to solidify your learning
Presented by Yixin Pan.
Thursday, September 24, 2026 from 11 am - 12 pm (Zoom)
No Code Needed: Getting Started with JMP & Jamovi
This interactive intro session offers a hands-on introduction to JMP and Jamovi: two powerful, point-and-click statistical tools designed to streamline your research workflow without writing code.
During this session, we will:
- Access, launch, and confidently navigate the intuitive layouts of both programs.
- Walk step-by-step through importing a dataset, generating descriptive statistics, producing dynamic data visualizations, and running core inferential stats.
- Discover the strengths, key differences, and research use cases for each tool so you can choose the right software for your projects.
Presented by Manjiri Sahasrabudhe.
Tuesday, September 29, 2026 from 11 am – 12:30 pm (Zoom)
MATLAB Tutorial 1: Introduction to MATLAB (with Copilot)
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, along with examples of how to use MATLAB Copilot in order to answer Mathworks-related questions, generate or complete code for various algorithms, and create unit tests to verify code is running correctly.
This hands-on tutorial will cover:
- Overview of MATLAB and its Applications
- Explanation of the MATLAB Interface
- Variables and Basic Commands
- Basic and Advanced Mathematical Operations
- Plotting Graphs and Array Data
- Generation of Unit Tests for Programming (using Copilot)
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, October 7, 2026 from 2 – 3:30 pm (Zoom)
MATLAB Tutorial 2: Numerical and Symbolic Math
MATLAB is a versatile and user-friendly programming language. This intermediate-level tutorial is designed to introduce a set of powerful symbolic calculation tools in MATLAB that help reduce the need for manual computations. Participants with no prior MATLAB experience are encouraged to attend the Introduction to MATLAB tutorial first.
- Polynomial evaluations, roots, and fitting
- Symbolic solutions to nonlinear functions
- Symbolic differentiation and integration
Presented by Yufeng Shi.
Wednesday, October 14, 2026 from 10:30 am - 12 pm noon (Zoom)
Introduction to R – Part 1: Basics, Syntax, and Data Management
This tutorial is intended for individuals who are new to R. In the first session of this two-part series, participants will build a foundational understanding of R within the RStudio environment. Topics will include navigating the RStudio interface, installing and managing packages, and writing basic R syntax. Participants will also learn how to import, create, and inspect data structures, as well as reshape datasets in preparation for statistical analyses. Instructions for downloading R, RStudio, and the example dataset will be provided prior to the session.
Presented by Leonard Faul.
Thursday, October 8, 2026 from 11 am - 12:30 pm (Zoom)
Introduction to R – Part 2: Data Analysis and Visualization
Building on the concepts introduced in Part 1, this session focuses on applying R for statistical analysis and data visualization. Participants will learn how to calculate descriptive statistics and conduct common statistical tests in R, including correlations, t-tests, and ANOVA. The tutorial will also introduce techniques for creating customized visualizations and using R Markdown to support reproducible reporting and presentation of results.
Presented by Leonard Faul.
Tuesday, October 13, 2026 from 11 am - 12:30 pm (Zoom)
Statistical Methodology
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 25, 2026 from 11 am - 12 pm (Zoom)
Generalized Linear Models and Analytical Software
Ever wondered what to do when your data doesn’t fit the neat assumptions of a simple linear regression? GLMs are used when data does not meet the assumptions of linear models such as when data is binary, count, or highly skewed (much like real world data).
In this tutorial, we’ll discuss:
- What GLMs are and why they matter
- Key assumptions (explained in plain language!)
- Syntax and considerations to run GLMs in popular software (e.g., R, SPSS, SAS, etc.)
Presented by Manjiri Sahasrabudhe and Viktoriya Babicheva.
Wednesday, September 30, 2026 from 3 - 4:30 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.
Thursday, October 1, 2026 from 10:30 – 12 pm (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.
Thursday, October 1, 2026 from 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 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, October 13, 2026 from 12:30 - 2 pm (Zoom)