Data Science Fundamentals

This session is geared towards all researchers wanting a deeper understanding of the data science process and techniques within each step, such as:

• Data Science Process
  • Problem Statement
  • Data Acquisition
  • Exploratory Data Analysis
  • Data Preparation
  • Modeling
  • Data Visualization
  • Taking Action
• Supervised, Unsupervised, and Deep Learning Techniques
• Specialized data science topics, including computer vision, natural language processing, and time series
• Data science in practice

This session will feature presentations by experts in data science at the Naval Information Warfare Systems Command (NAVWAR) 4.0 Data Science Learning Program.

Artificial Intelligence Fundamentals

This session is geared towards researchers interested in learning about artificial intelligence, including:

• AI Demystified
  • What is Data Science?
  • Data Science versus AI
  • What is AI?
  • Demystifying How Machines Learn
  • A Brief History of AI
  • AI in Action
  • AI Approaches and Examples
• Responsible AI
• AI Operations
  • Data Science Processes
  • Enterprise AI Operations
• AI Implementations

This session will feature presentations by experts in data science at the Naval Information Warfare Systems Command (NAVWAR) 4.0 Data Science Learning Program.

Artificial Intelligence Fundamentals Applications

This session builds upon the prior Artificial Intelligence fundamentals session and covers applications used in real world settings, including:

• Statistics versus Machine Learning
• Model Metrics
  • Classification Model Metrics – Accuracy, Precision, and Recall
  • Regression Model Metrics – MAE, MSE, RMSE, and R-squared
• Neural Networks and Their Applications
  • Feedforward Neural Networks
  • Increasing Complexity
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Network (RNN)
  • Transformer Learning
  • Generative Adversarial Networks (GAN)

This session will feature presentations by experts in data science at the Naval Information Warfare Systems Command (NAVWAR) 4.0 Data Science Learning Program.

FAIR and AI-Ready Data Sharing

This session is geared towards researchers wanting to understand and implement the skills and competencies needed to make biomedical and behavioral research data FAIR and AI-ready, including:

• FAIR principles and workflows
• Ethical considerations of data generating, handling, and sharing
• Core competencies to support AI-assisted biomedical research
• NIH-supported open-source data sharing
• Training and educational resources available to researchers

Different Privacy-Preserving Methodologies and Implications for Researchers

This session is geared towards researchers interested in learning about how privacy-preserving methodologies and technologies are used for data sharing and linkage to mitigate data privacy and the risk of re-identification of study participants, including:

• Importance of data privacy and participant confidentiality
• Privacy Preserving Record Linkage (PPRL)
• Differential privacy techniques
• Real-world applications and examples

Challenges, Opportunities, and Considerations for Researchers using Electronic Health Records and Real-World Data Sources

This session is geared towards researchers interested in leveraging real-world data in biomedical and behavioral research, including:

• What is Real-World Data (RWD)
• Scientific value and quality considerations for collection, use, and sharing of RWD
• Using RWD as part of the scientific paradigm
• RWD resources

Impact and Innovations from use of NIDDK-CR Resources

Hear from researchers who have accessed data from the NIDDK-CR on the impact and innovations from their research in the fields of data science, AI, and ML.