Programming and other computational methods can be useful as you collect, organize, and analyze research materials. Whether you are just getting started setting up a computational research environment for the first time or interested in expanding your skills working with different kinds of data, we are here to help you identify relevant tools, methods, and resources at Pitt for computational research.
Services
Support for Setting Up a Computational Research Environment
Before diving into a new research project, it can be helpful to ensure you have a plan for organizing your project’s code and data repository, are using tools designed for your operating system, and understand how to keep your coding environment up to date.
We can help you...
- Identify research computing resources available at Pitt
- Get familiar with the command line
- Set up your coding environment for R or Python
- Learn more about version control [link to data management page, version control service heading]
Programming and Research Software Support
Getting started learning a programming language or a new software package for computational research can often be daunting. We offer support for project design, tool selection, and identifying computational resources at Pitt.
We can help you...
- Find resources to get started with coding basics in R or Python
- Identify which R or Python packages and libraries might be relevant to your research questions
- Evaluate which applications implement the computational analysis methods you want to use (for example, to work with geospatial data, network data, or natural language processing)
- Learn how to interpret software documentation
- Connect with other Pitt community members who are using R and Python for computational research
Exploratory Data Analysis Tools and Methods
We support researchers and students in developing critical competencies for working with data, including the initial processes involved in making sense of quantitative and qualitative datasets that are often described as exploratory data analysis (EDA).
We can help you...
- Learn to use relational databases to structure and store data for efficient retrieval and analysis
- Find software and web applications for exploratory data analysis and data visualization
- Develop familiarity with data visualization best practices (see more below)
- Identify available Pitt resources for data science, statistical analysis, or qualitative data analysis (QDA)
Data Visualization Tools and Methods
Visualizing your data is valuable both for exploring patterns in the data and for explaining the results of your analyses to various audiences.
We can help you...
- Identify tools and methods for exploratory data analysis and visualization (see more above)
- Learn to visualize geospatial data using GIS and digital mapping software
- Develop familiarity with data visualization best practices, such as selecting appropriate chart types to visualize certain data patterns
- Learn to combine design principles with data visualization to communicate effectively with data (i.e., infographic design and digital storytelling)
Text Mining and Analysis
A range of different methods, including text data mining (TDM) and text analysis, can be applied to texts to discover new information or answer specific research questions. Quantitative text analysis is most useful when combined with traditional disciplinary research methods for critical interpretation, and we can help you navigate the complex landscape of tools and methods for working with text data.
We can help you...
- Understand how to clean and prepare texts for computational analysis
- Create text corpora through digitization and Optical Character Recognition (OCR)
- Identify appropriate text mining and natural language processing methods for particular research questions
- Find and use software for text mining and analysis
- Visualize text data using charts, word clouds, mind maps, and more
Network Analysis
Network analysis is a common research method in many disciplines, used to study social or historical networks as well as the spread of disease, complex species interaction, logistics networks, and more.
We can help you...
- Find network data
- Learn to use software for analyzing and visualizing network data, such as Gephi or Cytoscape
- Understand basic methods for analyzing networks, such as node centrality statistics and modularity algorithms
- Develop familiarity with best practices for creating network diagrams
For Instructors
Would you like to introduce your students to computational research tools, or to develop a course activity or assignment related to data analysis? We provide curriculum support in a variety of ways for arts, humanities, social sciences, and STEM courses. Request a teaching consultation or class workshop related to any of our Computational Research support areas by filling out the ULS instruction contact form.
We can help instructors...
- Determine which data analysis software best aligns with the rest of your syllabus
- Identify learning objectives related to data visualization or infographic design
- Create a digital assignment or classroom activity
- Plan a Digital Scholarship & Publishing class visit or customized workshop
Read more about Digital Scholarship and Publishing Instruction.
Recommended Resources and Events
Research Guides
Specialized Software and Hardware
The Hillman Digital Scholarship Lab offers a range of specialized software to support various computational research methods, including:
- R + RStudio (programming language and free environment for statistical computing and data visualization)
- Python + PyCharm for Education (programming language and free environment for learning and teaching Python)
- Anaconda (free and open-source package management and deployment platform)
- DB Browser for SQLite (free, open-source, visual tool for working with SQLite or SQLCipher database files)
- Gephi (open-source network analysis and visualization software)
- MALLET (Java-based toolkit for applying machine learning to text, including tasks like topic modeling, classification, clustering, and information extraction)
- NVivo (qualitative data analysis software)
- Tableau (visual data analytics platform)
Library Events
The Digital Scholarship and Publishing team offers public workshops and instruction sessions by request. Upcoming events can be viewed here . We also facilitate communities of practice and host special events related to Computational Research Methods.
Panthers Practicing Python (P3) is a community of practice that meets regularly throughout the year, enabling members of the Pitt community to connect, find support, and collaborate on their Python learning journeys. We welcome Python users of all experience levels, backgrounds, and roles (students, staff, instructors, researchers) across all Pitt campuses. Whether you’re a complete beginner looking for learning resources to get started, or an expert looking to connect with a broader community of Python practitioners, you are welcome in this community! Upcoming P3 community meetings can be viewed here.
