Data Services Classes

  • Data Wrangling in R

    Data Wrangling in R is the third class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of manipulating, analyzing and exporting data with the R tidyverse. R is a programming language and open source environment for statistical computing and graphics.

  • Figshare: A Repository for Researchers Publishing Open Research Data

    This one-hour webinar introduces researchers to Figshare, a generalist repository that has been supporting open access to research data for more than 10 years. Attendees will learn how to create an author profile, upload files, add high-quality metadata, and publish their research. In addition, this webinar will include tips for data sharing best practices and how to include Figshare in a data management plan. It will also introduce Figshare+, a newly launched repository for sharing large datasets over 100GB and up to many TBs. 

  • Hands-On Virtual Lab: Deep Learning

    In this hands-on virtual lab, the participants will familiarize themselves with Deep Learning concepts and techniques, using MATLAB Online to train deep neural networks on GPUs in the cloud, create deep learning models from scratch for images and signal data, explore pretrained models and use transfer learning, import and export models from Python frameworks such as Keras and PyTorch, as well as automatically generate code for embedded targets. Deep Learning can achieve state-of-the-art accuracy when it comes to complex problems such as image classification or developing predictive models f

  • Hands-On Virtual Lab: Machine Learning

    This virtual hands-on workshop explores the fundamentals of machine learning using MATLAB. The participants will be introduced to machine learning techniques to quickly explore data, use classification and regression apps to interactively train, compare and tune a model, and optimize the model using hyperparameter tuning. The participants will also learn how to get started with deep learning in MATLAB for data preparation, design, simulation, and deployment of deep neural networks. This is an introductory level class.

  • Image Processing and Computer Vision (IPCV) Hands-on MATLAB Workshop

    Image processing and computer vision techniques are used to extract, enhance, and analyze information from sensors that generate 2D and 3D data. These techniques enable researchers to automate image and vision-based tasks.

  • Introduction to Categorical Data

    This presentation serves to introduce the concepts associated with the analysis of categorical data. The presentation will begin with a definition of categorical data and a review of some of its benefits and limitations. Then the presenter will introduce the idea of analysis via proportions in the framework of a contingency table. From here, the audience will then learn about some common types of inference that may be employed in this setting.

  • Introduction to Data Science Statistical Methods with SAS

    This full-day course provides an overview of the statistical methods used by data scientists, including examples describing use of the SAS statistical software. This course will cover topics related to feature engineering, such as sampling, variable transformations, imputation, and variable selection. At the end of this course participants should be able to describe applications of supervised models, such as decision trees, neural networks, support vector machines, factorization machines, and logistic regression.

  • Introduction to Data Visualization in R: ggplot

    Introduction to Data Visualization in R: ggplot in R is the fourth class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of producing scatter plots, boxplots, and time series plots using ggplot. R is a programming language and open source environment for statistical computing and graphics.

  • Introduction to R and RStudio

    This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of R programming language and RStudio. R is a programming language and open source environment for statistical computing and graphics.

  • Introduction to R Data Types

    This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics.

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