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Course Description

Course Description

This webinar series introduces the fundamental concepts and practical techniques for working with large-scale clinical data using modern data science tools. The course covers both structured data (e.g., electronic health records, lab results, and billing data) and unstructured data (e.g., clinical notes), emphasizing their role in healthcare analytics and decision-making.

Trainees will learn how to process, analyze, and prepare clinical datasets using Python-based tools such as Pandas and PySpark. The course also introduces key concepts in big data infrastructure, including distributed storage systems (HDFS, AWS S3) and scalable processing frameworks (Apache Spark). In addition, trainees will explore natural language processing (NLP) techniques for extracting meaningful information from clinical notes.

By integrating data cleaning, feature engineering, and multimodal data fusion, the course provides an end-to-end pipeline for transforming raw clinical data into machine learning–ready datasets. Real-world healthcare examples are used throughout to highlight practical challenges such as data quality, bias, and scalability.

This course equips trainees with the foundational skills required to work with real-world clinical data. It prepares them for advanced applications in healthcare analytics, machine learning, and data-driven decision-making.


Audience

This training is designed for healthcare professionals, researchers, and data practitioners who work with or are interested in large-scale clinical data, including clinicians, public health professionals, and health data analysts. It is also suitable for individuals collaborating with data scientists or involved in data-driven healthcare projects. 

The expected experience level is beginner to intermediate. Trainees should have a basic understanding of data concepts (e.g., datasets, variables, tabular data). Some familiarity with Python (e.g., basic syntax or data analysis using Pandas) is recommended, as the course includes hands-on examples using tools such as Pandas and PySpark. Prior experience with big data frameworks or distributed systems is not required.


Course Structure

Module 1: Clinical Data

This module introduces the fundamentals of clinical data and its role in healthcare analytics. Learners explore different types of clinical data, healthcare data standards, common data quality challenges, and basic data analysis using Pandas. The module also provides an overview of distributed storage and processing technologies, including HDFS, AWS S3, and Apache Spark.

Module 2: Clinical Notes

This module focuses on extracting meaningful information from unstructured clinical text using natural language processing (NLP). Learners explore techniques such as tokenization, text cleaning, Named Entity Recognition (NER), and feature extraction, while also examining scalable approaches for processing clinical notes with PySpark and real-time analytics.

Module 3: Data Preparation

This module covers the essential steps for preparing clinical data for analysis and machine learning. Learners explore data cleaning, feature engineering, and methods for integrating structured and unstructured clinical data into machine learning–ready datasets. The module also introduces best practices for storing processed data in scalable formats such as Parquet.


Learning Outcomes

By the end of this webinar series, trainees will be able to:

  • Explain the structure, types, and characteristics of clinical data, including both structured and unstructured sources.

  • Describe the challenges of healthcare data, including issues related to scale, data quality, bias, and heterogeneity.

  • Apply data analysis techniques using Pandas and extend them to PySpark for large-scale data processing. 

  • Utilize distributed storage and computing frameworks (e.g., HDFS, AWS S3, Apache Spark) to handle big clinical datasets.

  • Apply basic natural language processing (NLP) techniques to extract meaningful information from clinical notes.

  • Perform key data preparation tasks, including handling missing values, removing duplicates, detecting outliers, and correcting data types.

  • Engineer features for machine learning, including encoding categorical variables, scaling numerical features, and constructing feature vectors.

  • Integrate structured and unstructured clinical data to build comprehensive datasets for analysis.

  • Design an end-to-end data pipeline that transforms raw clinical data into machine learning–ready formats.

  • Evaluate analytical outputs and understand the importance of reliability, validation, and domain considerations in healthcare applications.

Instrumental Persons

  • Legand L. Burge III, PhD - MPI, AIM-AHEAD Coordinating Center, DSTC
  • Toufeeq Syed, PhD - MPI, AIM-AHEAD Coordinating Center, Leadership Core
  • Alyssa Parham, Program Manager - Howard University, DSTC
  • Desta Haileselassie Hagos, PhD - Howard University, DSTC

Funding

The AIM-AHEAD program is funded by NIH, Agreement No. 1OT2OD032581. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.