Data Services Classes

  • Introduction to Taxonomies

    This webinar provides an overview of taxonomies, including the origins of taxonomies, where they are used in everyday life, and why they are important. The overview will present existing taxonomies in multiple areas and subjects (science, art, biology, medicine, industry, and news). The participants will learn about the importance of organizing their files and data and basic best practices for taxonomy building.

  • Introduction to Text Mining in R

    Many applications in bioinformatics and portfolio analysis involve extracting meaning from large volumes of natural language text. This class will provide an introduction to some of the tools available for text mining in the R programming language. Participants will gain knowledge in obtaining text data via API, processing text for analysis, extracting frequently occurring terms and term associations, and cluster documents by topic. Note: This course assumes that you already have a basic understanding of R and RStudio.

  • Introduction to the Systematic Review Process

    Conducting a systematic review can be time consuming and challenging. This class provides an overview of the different steps in the comprehensive process of conducting a systematic review. At the completion of the class, participants will be able to identify each step of the systematic review process and know where to access valuable resources.

  • Machine Learning with MATLAB Hands-on Workshop

    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.

  • Managing Data in Excel

    This one-hour training will provide detailed information and demonstrations on how to manage data in Excel. By the end of this course, the participants will recognize how to filter data by text, numbers, and date; how to sort data alphabetically and by color; how to remove duplicates; how to split and combine columns; and how to create customs lists.

    This is an introductory class for those who need to quickly learn basic Excel data management features and for those who are interested in a refresher.

  • MATLAB Automated Labeling and Iterative Learning

    Labeling signal data is a very important step in creating AI-based signal processing solutions.  However, this step can be very time consuming and manual. In this beginner/intermediate one-hour session, the attendees will be introduced to signal labeling for use in AI applications and discuss how MATLAB can be used to speed up and simplify the process, from preprocessing to extracting information from signals. The session will cover different approaches for signal labeling, including using algorithms and automating with deep learning models.

  • MATLAB for Excel Users

    When it comes to data analysis and visualization, technical professionals who use Excel often encounter functional limitations. MATLAB supplements the capabilities of Excel by providing access to pre-built mathematical and analysis functions, visualization tools, and the ability to automate analysis workflows. Attendees will learn the main steps required for performing data analysis with MATLAB, including data access and pre-processing, modeling, and deployment. Attendees will gain an understanding of how to efficiently apply data analysis techniques using the MATLAB platform.

  • MATLAB for Open Science

    In this webinar, participants will learn how to tell a story with their data using notebook-style live scripts and how to integrate MATLAB with Python code. Attendees will also learn how to share code and take advantage of MATLAB community tools such as File Exchange and GitHub. Finally, the participants will find out how they can host MATLAB offerings at their High-Performance Computing Centers or a Science Gateway. This session is for beginners; no software installation required.

  • MATLAB with Python

    MATLAB provides two-way integration with many programming languages such as Python, which allows for greater collaboration between investigators. This webinar will cover how to call MATLAB from Python and how to call Python libraries from MATLAB. Attendees will learn how to use MATLAB’s Python integration to improve the compatibility and usability of their code. This webinar will be helpful for participants who have collaborators using Python with MATLAB, or for attendees who want to integrate MATLAB’s capabilities in a Python program.

  • Medical Image Processing Techniques

    Learn about a wide range of capabilities for image processing and computer vision including machine and deep learning using deep convolutional neural networks (CNNs). Transitioning image models from pixel-based to feature-based allows us to extract information from images and video at a high level, to detect, classify, and track objects, co-register images, or understand a real-world scene. Using collections of features, we can train computers to recognize objects, with user-specified or automatically determined features.